From 4dba5484765fff71a505c9cbe748d704eb44eb51 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 17 Nov 2021 21:39:12 +0100 Subject: [PATCH 001/176] update version number --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 1506ac03..f435afac 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.2.9000 +Version: 1.0.3 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), From 002bafe1c1620670fa0ea889c5f56e40064df7b0 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 17 Nov 2021 22:21:55 +0100 Subject: [PATCH 002/176] update website --- .github/workflows/pkgdown.yaml | 48 ++++++++++------------------------ NEWS.md | 2 +- cran-comments.md | 21 +++++++++++++++ 3 files changed, 36 insertions(+), 35 deletions(-) diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml index f118aded..59ae3087 100644 --- a/.github/workflows/pkgdown.yaml +++ b/.github/workflows/pkgdown.yaml @@ -1,53 +1,33 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: - branches: master + branches: [main, master] + tags: ['*'] name: pkgdown jobs: pkgdown: - runs-on: macOS-latest - if: "! contains(toJSON(github.event.commits.*.message), '[skip-pkgdown]')" + runs-on: ubuntu-latest env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} steps: - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-r@master + - uses: r-lib/actions/setup-pandoc@v1 - - uses: r-lib/actions/setup-pandoc@master - - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} - - - name: Cache R packages - uses: actions/cache@v1 + - uses: r-lib/actions/setup-r@v1 with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - - - name: Install JAGS macOS - if: runner.os == 'macOS' - run: | - rm '/usr/local/bin/gfortran' - brew install jags - - - name: Install dependencies - run: | - remotes::install_deps(dependencies = TRUE) - install.packages("pkgdown") - shell: Rscript {0} + use-public-rspm: true - - name: Install package - run: R CMD INSTALL . + - uses: r-lib/actions/setup-r-dependencies@v1 + with: + extra-packages: pkgdown + needs: website - name: Deploy package run: | - git config --local user.email "actions@github.com" - git config --local user.name "GitHub Actions" + git config --local user.name "$GITHUB_ACTOR" + git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com" Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)' diff --git a/NEWS.md b/NEWS.md index 7ee67fa3..aa6aff6c 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# JointAI (development version) +# JointAI (1.0.3) ## New features diff --git a/cran-comments.md b/cran-comments.md index 78cf12d0..ccb760e4 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,3 +1,24 @@ +# JointAI (version 1.0.3) + +## Round 1 + +### Test environments +* local Windows 10, R 4.1.1 +* windows server x64 (via github actions), R 3.6.3, R 4.1.2 +* ubuntu 20.04.3 LTS (via github actions), R 4.0.5, R 4.1.2, devel +* win-builder (oldrelease, devel and release) + + +### R CMD check results + +0 errors | 0 warnings | 0 notes + +### Reverse dependencies + +There are no reverse dependencies. + +--- + # JointAI (version 1.0.2) ## Round 1 From 9453f027827578245f002bba6f7e03aa21289ba2 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 17 Nov 2021 22:31:35 +0100 Subject: [PATCH 003/176] update links --- R/JointAI.R | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/JointAI.R b/R/JointAI.R index de02eca3..7ab57165 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -4,7 +4,7 @@ #' for incomplete or complete data under the Bayesian framework. #' Models for incomplete covariates, conditional on other covariates, #' are specified automatically and modelled jointly with the analysis model. -#' MCMC sampling is performed in \href{http://mcmc-jags.sourceforge.net/}{'JAGS'} +#' MCMC sampling is performed in \href{https://mcmc-jags.sourceforge.io/}{'JAGS'} #' via the R package #' \href{https://CRAN.R-project.org/package=rjags}{\strong{rjags}}. #' diff --git a/README.md b/README.md index b3ca6d22..df5abf9a 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ [![Download counter](https://cranlogs.r-pkg.org/badges/JointAI)](https://cran.r-project.org/package=JointAI) [![Rdoc](https://www.rdocumentation.org/badges/version/JointAI)](https://www.rdocumentation.org/packages/JointAI) -[![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://codecov.io/gh/NErler/JointAI) +[![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://app.codecov.io/gh/NErler/JointAI) [![Travis-CI Build Status](https://travis-ci.org/NErler/JointAI.svg?branch=master)](https://travis-ci.org/NErler/JointAI) [![R build From 33b07d7d7de9dc5f9049286db41ad556cc556cc9 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 17 Nov 2021 22:34:45 +0100 Subject: [PATCH 004/176] remove parentheses around version number in news.md --- NEWS.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/NEWS.md b/NEWS.md index aa6aff6c..d45183cc 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# JointAI (1.0.3) +# JointAI 1.0.3 ## New features @@ -33,7 +33,7 @@ -------------------------------------------------------------------------------- -# JointAI (1.0.2) +# JointAI 1.0.2 ## New features * `rd_vcov()`: new function added to extract the random effect variance- @@ -50,7 +50,7 @@ -------------------------------------------------------------------------------- -# JointAI (1.0.1) +# JointAI 1.0.1 ## Minor improvements and bug fixes * `data_list`: omit data matrix `M_*` from `data_list` if `ncol == 0` @@ -69,7 +69,7 @@ -------------------------------------------------------------------------------- -# JointAI (1.0.0) +# JointAI 1.0.0 This version of **JointAI** contains some major changes. To extend the package it was necessary to change the internal structure and it was not possible to From 6de6d27ed561588e322831e96e419da4129217d2 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 09:56:30 +0100 Subject: [PATCH 005/176] fix urls --- DESCRIPTION | 6 +- NEWS.md | 2 +- README.Rmd | 4 +- README.md | 4 +- man/JointAI.Rd | 2 +- vignettes/MCMCsettings.Rmd | 2 +- vignettes/ModelSpecification.Rmd | 4 +- vignettes/ModelSpecification.Rmd.orig | 180 ++++++++++++------------- vignettes/SelectingParameters.Rmd | 2 +- vignettes/SelectingParameters.Rmd.orig | 2 +- 10 files changed, 104 insertions(+), 104 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f435afac..e3e2909a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.3 +Version: 1.0.2.9000 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), @@ -10,7 +10,7 @@ Description: Joint analysis and imputation of incomplete data in the Bayesian Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' - with the help of + with the help of the package 'rjags'. URL: https://nerler.github.io/JointAI/ License: GPL (>= 2) @@ -19,7 +19,7 @@ LazyData: TRUE RoxygenNote: 7.1.2 Roxygen: list(old_usage = TRUE, markdown = TRUE) Imports: rjags, mcmcse, coda, rlang, future, foreach, mathjaxr, survival, MASS -SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net/) +SystemRequirements: JAGS (https://mcmc-jags.sourceforge.io/) Suggests: knitr, rmarkdown, diff --git a/NEWS.md b/NEWS.md index d45183cc..aaf24ae8 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# JointAI 1.0.3 +# JointAI Development Vesion ## New features diff --git a/README.Rmd b/README.Rmd index 3cc7402b..94ce5d4b 100644 --- a/README.Rmd +++ b/README.Rmd @@ -41,8 +41,8 @@ are available. It is also possible to fit multiple models of mixed types simultaneously. Missing values in (if present) will be imputed automatically. **JointAI** performs some preprocessing of the data and creates a -[JAGS](http://mcmc-jags.sourceforge.net/) model, which will then automatically be -passed to [JAGS](http://mcmc-jags.sourceforge.net/) with the help of the R +[JAGS](https://mcmc-jags.sourceforge.io/) model, which will then automatically be +passed to [JAGS](https://mcmc-jags.sourceforge.io/) with the help of the R package [**rjags**](https://CRAN.R-project.org/package=rjags). Besides the main modelling functions, **JointAI** also provides a number of diff --git a/README.md b/README.md index df5abf9a..a00d1b0b 100644 --- a/README.md +++ b/README.md @@ -29,8 +29,8 @@ types simultaneously. Missing values in (if present) will be imputed automatically. **JointAI** performs some preprocessing of the data and creates a -[JAGS](http://mcmc-jags.sourceforge.net/) model, which will then -automatically be passed to [JAGS](http://mcmc-jags.sourceforge.net/) +[JAGS](https://mcmc-jags.sourceforge.io/) model, which will then +automatically be passed to [JAGS](https://mcmc-jags.sourceforge.io/) with the help of the R package [**rjags**](https://CRAN.R-project.org/package=rjags). diff --git a/man/JointAI.Rd b/man/JointAI.Rd index ca05f15d..6f9998bd 100644 --- a/man/JointAI.Rd +++ b/man/JointAI.Rd @@ -9,7 +9,7 @@ The \strong{JointAI} package performs simultaneous imputation and inference for incomplete or complete data under the Bayesian framework. Models for incomplete covariates, conditional on other covariates, are specified automatically and modelled jointly with the analysis model. -MCMC sampling is performed in \href{http://mcmc-jags.sourceforge.net/}{'JAGS'} +MCMC sampling is performed in \href{https://mcmc-jags.sourceforge.io/}{'JAGS'} via the R package \href{https://CRAN.R-project.org/package=rjags}{\strong{rjags}}. } diff --git a/vignettes/MCMCsettings.Rmd b/vignettes/MCMCsettings.Rmd index 47f7000e..973fb4a2 100644 --- a/vignettes/MCMCsettings.Rmd +++ b/vignettes/MCMCsettings.Rmd @@ -27,7 +27,7 @@ options(width = 100) In **JointAI**, models are estimated in the Bayesian framework, using MCMC ([Markov Chain Monte Carlo](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo)) sampling. -The sampling is done by the software [JAGS](http://mcmc-jags.sourceforge.net/) +The sampling is done by the software [JAGS](https://mcmc-jags.sourceforge.io/) ("Just Another Gibbs Sampler"), which performs [Gibbs](https://en.wikipedia.org/wiki/Gibbs_sampling) sampling. **JointAI** pre-processes the data to get it into a form that can be diff --git a/vignettes/ModelSpecification.Rmd b/vignettes/ModelSpecification.Rmd index a0278eee..9856cbf0 100644 --- a/vignettes/ModelSpecification.Rmd +++ b/vignettes/ModelSpecification.Rmd @@ -329,7 +329,7 @@ from the package **splines** (which is automatically installed with R). Functions involving *variables that have missing values* need to be re-calculated in each iteration of the MCMC sampling. Therefore, currently, only functions that can be interpreted by -[JAGS](http://mcmc-jags.sourceforge.net/) can be used for incomplete variables. +[JAGS](https://mcmc-jags.sourceforge.io/) can be used for incomplete variables. Those functions include: * `log()`, `exp()` @@ -338,7 +338,7 @@ Those functions include: * `sin()`, `cos()` * polynomials (using `I()`) and other algebraic operations involving one or multiple (in)complete variables, as long as the formula can be interpreted by - [JAGS](http://mcmc-jags.sourceforge.net/). + [JAGS](https://mcmc-jags.sourceforge.io/). The list of functions implemented in JAGS can be found in the [JAGS user manual](https://sourceforge.net/projects/mcmc-jags/files/Manuals/). diff --git a/vignettes/ModelSpecification.Rmd.orig b/vignettes/ModelSpecification.Rmd.orig index 53b84267..d50cb1f7 100644 --- a/vignettes/ModelSpecification.Rmd.orig +++ b/vignettes/ModelSpecification.Rmd.orig @@ -1,7 +1,7 @@ --- title: "Model Specification" date: "2020-06-20" -output: +output: rmarkdown::html_vignette: toc: true depth: 4 @@ -25,21 +25,21 @@ options(width = 100) In this vignette, we use the [NHANES](https://nerler.github.io/JointAI/reference/NHANES.html) -data for examples in cross-sectional data and the +data for examples in cross-sectional data and the dataset [simLong](https://nerler.github.io/JointAI/reference/simLong.html) for examples in longitudinal data. -For more info on these datasets, check out the vignette +For more info on these datasets, check out the vignette [*Visualizing Incomplete Data*](https://nerler.github.io/JointAI/articles/VisualizingIncompleteData.html), in which the distributions of variables and missing values in both sets is explored. To learn more about the theoretical background of the statistical approach -implemented in **JointAI**, check out the vignette +implemented in **JointAI**, check out the vignette [Theoretical Background](https://nerler.github.io/JointAI/articles/TheoreticalBackground.html). **Note:**
In some of the examples we use `n.adapt = 0` (and `n.iter = 0`, which is the -default). +default). This is to prevent the MCMC sampling and thereby reduce computational time when compiling this vignette. @@ -61,7 +61,7 @@ compiling this vignette. * `survreg_imp()`: parametric (Weibull) survival models * `coxph_imp()`: Proportional hazards survival models * `JM_imp()`: Joint model for longitudinal and survival data - + Specification of these functions is similar to the specification of the complete data versions `lm()`, `glm()`, `lme()` @@ -76,18 +76,18 @@ and [`coxph()`](https://stat.ethz.ch/R-manual/R-devel/library/survival/html/coxp All functions require the arguments `formula` (or `fixed` and `random` in for mixed models) and `data`. -Specification of the (fixed effects) model formula is demonstrated in section +Specification of the (fixed effects) model formula is demonstrated in section [Model formula](#ModelFormula), specification of the random random effects in section [Multi-level structure & longitudinal covariates](#MultiLevelStructure). -Additionally, `glm_imp()`, `glme_imp()` and `glmer_imp()` require the -specification of the model +Additionally, `glm_imp()`, `glme_imp()` and `glmer_imp()` require the +specification of the model [`family`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html) -(and `link` function). +(and `link` function). ### Model family and link functions -Implemented families and links for `glm_imp()`, `glme_imp()` and `glmer_imp()` +Implemented families and links for `glm_imp()`, `glme_imp()` and `glmer_imp()` are ```{r, echo = FALSE} tab <- rbind(gaussian = "with links: `identity`, `log`", @@ -140,7 +140,7 @@ linear predictor, in which covariates (independent variables) are separated by An intercept is added automatically (except in proportional hazard models or models for ordinal outcomes). -`survreg_imp()` and `coxph_imp()` expect a +`survreg_imp()` and `coxph_imp()` expect a [survival object](https://stat.ethz.ch/R-manual/R-devel/library/survival/html/Surv.html) (created with `Surv()`) on the left hand side of the model formula. Currently, only right censored data can be handled and there can only be @@ -151,7 +151,7 @@ provide the argument `formula` or the arguments `fixed` and `random`. ### Interactions Interactions between variables can be introduced using `:` or `*`, which adds -the interaction term AND the main effects, i.e., +the interaction term AND the main effects, i.e., ```{r, eval = FALSE} SBP ~ age + gender + smoke * creat ``` @@ -167,16 +167,16 @@ mod2a <- glm_imp(educ ~ gender * (age + smoke + creat), data = NHANES, family = binomial(), n.adapt = 0) ``` -The function +The function [`parameters()`](https://nerler.github.io/JointAI/reference/parameters.html) -returns a matrix off all parameters that are specified to be followed +returns a matrix off all parameters that are specified to be followed (column `coef`) and, for regression coefficients, the name of the variable -the coefficient relates to (`varname`), the outcome variable of the +the coefficient relates to (`varname`), the outcome variable of the respective model `outcome`. For multinomial models, which have multiple linear -predictors, the column `outcat` identifies the category of the outcome the -parameters refer to. +predictors, the column `outcat` identifies the category of the outcome the +parameters refer to. -We use the function `parameters()` here and in other vignettes to demonstrate +We use the function `parameters()` here and in other vignettes to demonstrate the effect that different model specifications have. ```{r} parameters(mod2a) @@ -199,8 +199,8 @@ mod2c <- glm_imp(educ ~ gender + (age + smoke + creat)^3, parameters(mod2c) ``` -In **JointAI**, interactions between any variables, observed or -incomplete, variables on different levels of a hierarchical structure, +In **JointAI**, interactions between any variables, observed or +incomplete, variables on different levels of a hierarchical structure, can be handled. When an incomplete variable is involved, the interaction term is re-calculated within each iteration of the MCMC sampling, using the imputed values from the @@ -215,7 +215,7 @@ match, and results may be incorrect. ### Non-linear functional forms In practice, associations between outcome and covariates do not always meet -the standard assumption that all covariate effects are linear. +the standard assumption that all covariate effects are linear. Often, assuming a logarithmic, quadratic, or other non-linear effect is more appropriate. @@ -224,30 +224,30 @@ functions such as [`log()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/Log.html) (the natural logarithm), [`sqrt()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/MathFun.html) -(the square root) or +(the square root) or [`exp()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/Log.html) (the exponential function). It is also possible to use algebraic operations to calculate a new variable from one or more covariates. To indicate to R that the operators used in the formula -should be interpreted as algebraic operators and not as formula operators, -such calculations need to be wrapped in the function +should be interpreted as algebraic operators and not as formula operators, +such calculations need to be wrapped in the function [`I()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/AsIs.html). -For example, to include a quadratic effect of the variable `x` we would have +For example, to include a quadratic effect of the variable `x` we would have to use `I(x^2)`. Just writing `x^2` would be interpreted as the interaction of `x` with itself, which simplifies to just `x`. -For *completely observed covariates*, **JointAI** can handle any standard type -of function implemented in R. This also includes splines, e.g., using +For *completely observed covariates*, **JointAI** can handle any standard type +of function implemented in R. This also includes splines, e.g., using [`ns()`](https://stat.ethz.ch/R-manual/R-devel/library/splines/html/ns.html) or [`bs()`](https://stat.ethz.ch/R-manual/R-devel/library/splines/html/bs.html) from the package **splines** (which is automatically installed with R). -Functions involving *variables that have missing values* need to be -re-calculated in each iteration of the MCMC sampling. +Functions involving *variables that have missing values* need to be +re-calculated in each iteration of the MCMC sampling. Therefore, currently, only functions that can be interpreted by -[JAGS](http://mcmc-jags.sourceforge.net/) can be used for incomplete variables. +[JAGS](https://mcmc-jags.sourceforge.io/) can be used for incomplete variables. Those functions include: * `log()`, `exp()` @@ -256,9 +256,9 @@ Those functions include: * `sin()`, `cos()` * polynomials (using `I()`) and other algebraic operations involving one or multiple (in)complete variables, as long as the formula can be interpreted by - [JAGS](http://mcmc-jags.sourceforge.net/). + [JAGS](https://mcmc-jags.sourceforge.io/). -The list of functions implemented in JAGS can be found in the +The list of functions implemented in JAGS can be found in the [JAGS user manual](https://sourceforge.net/projects/mcmc-jags/files/Manuals/). **Some examples:**^[Note: these examples are chosen to demonstrate functionality @@ -285,9 +285,9 @@ mod3d <- lm_imp(SBP ~ bili + sin(creat) + cos(albu), data = NHANES) #### What happens inside **JointAI**? When a model formula includes a function of a complete or incomplete variable, the main effect of that variable is automatically added as an auxiliary variable. -(For more info on auxiliary variables, see the section +(For more info on auxiliary variables, see the section ["Auxiliary variables"](#auxvars).) -In the linear predictors of models for covariates, usually, only the main +In the linear predictors of models for covariates, usually, only the main effects are used. In `mod3b` from above, for example, the spline of age is used as predictor for @@ -310,7 +310,7 @@ and other parameters by setting `priors`, `regcoef` and `otherpars` to `FALSE`. When a function of a variable is specified as an auxiliary variable, this function is used (as well) in the models for covariates. For example, in `mod3e`, waist circumference (`WC`) -is not part of the model for `SBP`, and the auxiliary variable `I(WC^2)` is +is not part of the model for `SBP`, and the auxiliary variable `I(WC^2)` is used in the linear predictor of the imputation model for `bili`: ```{r, message = FALSE} @@ -343,22 +343,22 @@ list_models(mod3g, priors = FALSE, regcoef = FALSE, otherpars = FALSE) Incomplete variables are always imputed on their original scale, i.e., * in `mod3b` the variable `bili` is imputed and the quadratic and cubic versions -calculated from the imputed values. -* Likewise, `creat` and `albu` in `mod3c` +calculated from the imputed values. +* Likewise, `creat` and `albu` in `mod3c` are imputed separately, and `I(creat/albu^2)` calculated from the imputed (and observed) values. **Important:**
-When different transformations of the same incomplete variable are used in one +When different transformations of the same incomplete variable are used in one model, it is strongly discouraged to calculate these transformations beforehand and to supply them as separate variables. The same is the case for interactions.
If, for example, a model formula contains both `x` and `x2` (where `x2` = `x^2`), they are treated as separate variables and imputed with different models. -Imputed values of `x2` are thus not equal to the square of imputed values of -`x`. +Imputed values of `x2` are thus not equal to the square of imputed values of +`x`. Instead, `x + I(x^2)` should be used in the model formula. Then, only `x` is imputed and used in the linear predictor of models for other incomplete variables, and `x^2` is calculated from the imputed values @@ -367,7 +367,7 @@ of `x`. #### Functions with restricted support -When a function has restricted support, e.g., `log(x)` is only defined for +When a function has restricted support, e.g., `log(x)` is only defined for `x > 0`, the model used to impute `x` needs to comply with these restrictions. This can either be achieved by truncating the distribution assumed for `x`, using the argument `trunc`, or by specifying a model for `x` that meets @@ -381,8 +381,8 @@ use the default model for continuous variables, `"lm"`, a linear model, i.e., assuming a normal distribution and truncate this distribution by specifying `trunc = list(bili = c(, ))` (where the lower and upper limits are the smallest and largest allowed values) or choose a model -(using the argument `models`; more details see the section on -[covariate model types](#meth)) that only imputes positive values such as a +(using the argument `models`; more details see the section on +[covariate model types](#meth)) that only imputes positive values such as a log-normal distribution (`"glm_lognorm"`) or a Gamma distribution (e.g., `"glm_gamma_log"`): @@ -409,7 +409,7 @@ that do exist in JAGS, but not in R, by defining a new function in R that has the name of the function in JAGS. **Example**:
-In JAGS the inverse logit transformation is defined in the function +In JAGS the inverse logit transformation is defined in the function `ilogit`. In R, there is no function `ilogit`, but the inverse logit is available as the distribution function of the logistic distribution `plogis()`. @@ -423,10 +423,10 @@ mod5a <- lm_imp(SBP ~ age + gender + ilogit(creat), data = NHANES) #### Nested functions -It is also possible to nest a function in another function. +It is also possible to nest a function in another function. **Example:**^[Again, this is just a demonstration of the possibilities in JointAI, but nesting -transformations will most often result in coefficients that that do not have +transformations will most often result in coefficients that that do not have meaningful interpretation in practice.] The complementary log log transformation is restricted to values larger than 0 @@ -451,16 +451,16 @@ mod6a <- lm_imp(SBP ~ age + gender + cloglog(ilogit(creat)), data = NHANES) ## Multi-level structure & longitudinal covariates{#MultiLevelStructure} In multi-level models, additional to the fixed effects structure specified by the argument `fixed` a random effects structure needs to be provided via the -argument `random`. +argument `random`. Alternatively, it is possible to provide a `formula` that contains both the -fixed and random effects structure (corresponding to the specification used +fixed and random effects structure (corresponding to the specification used in [**lme4**](https://CRAN.R-project.org/package=lme4)). ### Random effects `random` takes a one-sided formula starting with a `~`. Variables for which a random effect should be included are usually separated by a `+`, and the grouping -variable is separated by `|`. A random intercept is added automatically and +variable is separated by `|`. A random intercept is added automatically and only needs to be specified in a random intercept only model. A few examples: @@ -471,7 +471,7 @@ A few examples: random effect for `time` * `random = ~ time + x | id` random intercept, random slope for `time` and random effect for variable `x` - + The corresponding specifications using the argument `formula` would be * ` + (1 | id)` @@ -495,11 +495,11 @@ needs to be used: It is possible to model both crossed and nested random effects, however the distinction between crossed and nested random effects must come from the coding -of the id variables. +of the id variables. For example, if patients are nested in hospitals, all observations that have the same patient id also need to have the same hospital id. -When this is not the case, i.e., some patients were measured at multiple +When this is not the case, i.e., some patients were measured at multiple hospitals, the random effects are crossed. There is (theoretically) no restriction as to how many grouping levels @@ -507,7 +507,7 @@ are possible. ### Longitudinal covariates From **JointAI** version 0.5.0 onward imputation of longitudinal covariates is -possible. For details the types of models that are available for covariates +possible. For details the types of models that are available for covariates in a multi-level setting, see the section [covariate model types](#meth) below. @@ -517,19 +517,19 @@ When incomplete baseline covariates (level > 1) are involved in the model it is usually necessary to specify models for all variables on lower levels, even if they are completely observed. This is done automatically by **JointAI**, but it may be necessary to change -the default model types to models that better fit the distributions of the +the default model types to models that better fit the distributions of the respective variables. -It is typically not necessary to specify models for variables on higher levels +It is typically not necessary to specify models for variables on higher levels if there are no incomplete covariates on lower levels. For example, in a 2-level setting, if there are no missing values in level-2 -variables, it is not necessary to specify models for completely observed +variables, it is not necessary to specify models for completely observed level-1 variables. But if there are missing values in level-2 variables, models need to be specified for all level-1 variables. #### Why do we need models for completely observed covariates? -The joint distribution of an outcome $y$, covariates $x$, random effects $b$ +The joint distribution of an outcome $y$, covariates $x$, random effects $b$ and parameters $\theta$, $p(y, x, b, \theta)$, is modelled as the product of univariate conditional distributions. To facilitate the specification of these distributions they are ordered so that longitudinal (level-1) variables may have @@ -548,7 +548,7 @@ p(y, x, b, \theta) = & p(y \mid x_1, ..., x_4, b_y, \theta_y) && \text{analysis Since the parameter vectors $\theta_{x1}$, $\theta_{x2}$, ... are assumed to be a priori independent, and furthermore $x_1$ is completely observed and modelled independently of incomplete variables, -estimation of the other model parts is not affected by $p(x_1\mid \theta_{x1})$ +estimation of the other model parts is not affected by $p(x_1\mid \theta_{x1})$ and, hence, this model can be omitted. $p(x_3 \mid x_1, x_2, b_{x3}, \theta_{x3})$, on the other hand is modelled @@ -563,7 +563,7 @@ estimation of parameters in the other parts of the model and could be omitted. ## Covariate model types {#meth} **JointAI** automatically selects models for all incomplete covariates (and if necessary also for some complete covariates). -The type of model is selected automatically based on the `class` of the +The type of model is selected automatically based on the `class` of the variable and the number of levels. The automatically selected types for baseline (highest level) covariates are: @@ -585,9 +585,9 @@ The default methods for lower level covariates are: ```{r, echo = FALSE} tab <- rbind(lmm = c("linear mixed model", "continuous longitudinal variables"), glmm_logit = c("logistic mixed model", "longitudinal factors with two levels"), - mlogitmm = c("multinomial logit mixed model", + mlogitmm = c("multinomial logit mixed model", "longitudinal unordered factors with >2 levels"), - clmm = c("cumulative logit mixed model", + clmm = c("cumulative logit mixed model", "longitudinal ordered factors with >2 levels") ) @@ -619,7 +619,7 @@ knitr::kable(tab, row.names = FALSE) ``` -`lognorm` assumes a normal distribution for the natural logarithm of the +`lognorm` assumes a normal distribution for the natural logarithm of the variable, but the variable enters the linear predictor of the analysis model (and possibly other imputation models) on its original scale. @@ -662,16 +662,16 @@ mod8a$models When there is a "time" variable in the model, such as `age` (age of the child -at the time of the measurement) in the `simLong` +at the time of the measurement) in the `simLong` it may not be meaningful to specify a model for that variable. -Especially when the "time" variable is pre-specified by the design of the study it can +Especially when the "time" variable is pre-specified by the design of the study it can usually be assumed to be independent of the covariates and a model for it has no useful interpretation. The argument `no_model` allows us to exclude models for such variables (as long as they are completely observed): ```{r, message = FALSE, warning = FALSE} -mod8b <- lme_imp(bmi ~ GESTBIR + ETHN + HEIGHT_M + SMOKE + hc + MARITAL + +mod8b <- lme_imp(bmi ~ GESTBIR + ETHN + HEIGHT_M + SMOKE + hc + MARITAL + ns(age, df = 2), random = ~ns(age, df = 2) | ID, data = simLong, no_model = "age", n.adapt = 0) @@ -695,10 +695,10 @@ Variables of type `logical` are automatically converted to binary factors. In **JointAI**, the models automatically specified for covariates are ordered by the hierarchical level of the respective response variable (descending). The linear predictor of each model contains the incomplete variables that are -specified later in the sequence and all complete variables of the same or +specified later in the sequence and all complete variables of the same or lower level. -Within each level, models are ordered by the proportion of missing values in +Within each level, models are ordered by the proportion of missing values in the respective response variables, so that the variable with the most missing values has the most covariates in its linear predictor. @@ -712,13 +712,13 @@ list_models(mod8a, priors = FALSE, regcoef = FALSE, otherpars = FALSE, refcat = ## Auxiliary variables {#auxvars} Auxiliary variables are variables that are not part of the analysis model, but -should be considered as predictor variables in the imputation models because +should be considered as predictor variables in the imputation models because they can inform the imputation of unobserved values. Good auxiliary variables are ^[Van Buuren, S. (2012). Flexible imputation of missing data. Chapman and Hall/CRC. See also the [second edition online](https://stefvanbuuren.name/fimd/).] * associated with an incomplete variable of interest, or are -* associated with the missingness of that variable, and +* associated with the missingness of that variable, and * do not have too many missing values themselves. Importantly, they should be observed for a large proportion of the cases that have a missing value in the variable to be imputed. @@ -743,7 +743,7 @@ list_models(mod9a, priors = FALSE, regcoef = FALSE, otherpars = FALSE, refcat = ### Functions of variables as auxiliary variables As shown above in [`mod3e`](#mod3e) and [`mod3f`](#mod3e), it is possible to -specify functions of auxiliary variables. In that case, the auxiliary variable +specify functions of auxiliary variables. In that case, the auxiliary variable is not considered as linear effect but as specified by the function: ```{r, message = FALSE, warning = FALSE} mod9b <- lm_imp(SBP ~ gender + age + occup, data = NHANES, @@ -756,7 +756,7 @@ list_models(mod9b, priors = FALSE, regcoef = FALSE, otherpars = FALSE, refcat = ``` **Note:**
-Omitting auxiliary variables from the analysis model implies that the outcome +Omitting auxiliary variables from the analysis model implies that the outcome is independent of these variables, conditional on the other variables in the model. If this is not true, the model is mis-specified which may lead to biased results (similar to leaving a confounding variable out of a model). @@ -820,8 +820,8 @@ mod10a <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, #### Setting reference categories for individual variables Alternatively, `refcats` takes a named vector, in which the reference category -for each variable can be specified either by its number or its name, or one of -the three global types: "first", "last" or "largest". +for each variable can be specified either by its number or its name, or one of +the three global types: "first", "last" or "largest". For variables for which no reference category is specified in the list the default is used. @@ -834,7 +834,7 @@ mod10b <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, To help to specify the reference category, the function [`set_refcat()`](https://nerler.github.io/JointAI/reference/set_refcat.html) -can be used. +can be used. It prints the names of the categorical variables that are selected by * a specified model formula and/or @@ -842,7 +842,7 @@ It prints the names of the categorical variables that are selected by * a vector of naming covariates or all categorical variables in the data if only `data` is provided, -and asks a number of questions which the user needs to reply to by input of +and asks a number of questions which the user needs to reply to by input of a number. ```{r, echo = FALSE} @@ -877,8 +877,8 @@ refs_mod10 <- set_refcat(NHANES, formula = formula(mod10b)) When option 4 is chosen, a question for each categorical variable is asked, for example: ```{r } -#> The reference category for “race” should be -#> +#> The reference category for “race” should be +#> #> 1: Mexican American #> 2: Other Hispanic #> 3: Non-Hispanic White @@ -892,7 +892,7 @@ the determined specification for the argument `refcats` is printed: #> In the JointAI model specify: #> refcats = c(gender = 'female', race = 'Non-Hispanic White', educ = 'low', #> occup = 'not working', smoke = 'never') -#> +#> #> or use the output of this function. ``` @@ -911,13 +911,13 @@ mod10c <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, **Note:**
Changing a reference category via the argument `refcats` does not change the order of levels in the dataset or any of the data matrices inside **JointAI**. -Only when, in the JAGS model, the categorical variables is converted into -dummy variables, the reference category is used to determine for which levels +Only when, in the JAGS model, the categorical variables is converted into +dummy variables, the reference category is used to determine for which levels the dummies are created. ## Hyper-parameters -In the Bayesian framework, parameters are random variables for which a +In the Bayesian framework, parameters are random variables for which a distribution needs to be specified. These distributions depend on parameters themselves, i.e., on hyper-parameters. @@ -928,8 +928,8 @@ parameters used in a `JointAI` model. default_hyperpars() ``` -To change the hyper-parameters in a **JointAI** model, the list obtained from -`default_hyperpars()` can be edited and passed to the argument `hyperpars` +To change the hyper-parameters in a **JointAI** model, the list obtained from +`default_hyperpars()` can be edited and passed to the argument `hyperpars` in the main functions `*_imp()`. * `mu_reg_*` and `tau_reg_*` refer to the mean and precision in the @@ -942,7 +942,7 @@ in the main functions `*_imp()`. of degrees of freedom depending on the number of random effects `nranef` (dimension of `D`). By default, `KinvD` will be set to the number of random effects plus one. -* `shape_diag_RinvD` and `rate_diag_RinvD` are the scale and rate parameters +* `shape_diag_RinvD` and `rate_diag_RinvD` are the scale and rate parameters of the Gamma prior of the diagonal elements of `RinvD`. In random effects models with only one random effect, instead of the Wishart @@ -951,7 +951,7 @@ distribution a Gamma prior is used for the inverse of `D`. ## Scaling When variables are measured on very different scales this can result in slow -convergence and bad mixing. +convergence and bad mixing. Therefore, **JointAI** includes scaling of continuous covariates in the JAGS model (i.e., instead of writing `... + covar + ...` in the linear predictor, `... + (covar - mean)/sd) + ...` is written). @@ -964,7 +964,7 @@ If `scale_vars` is a vector of variable names, scaling will only be done for those variables. By default, only the MCMC samples that is scaled back to the scale of the data -is stored in a `JointAI` object. When the argument `keep_scaled_mcmc = TRUE` +is stored in a `JointAI` object. When the argument `keep_scaled_mcmc = TRUE` also the scaled sample is kept. This is mainly for de-bugging purposes. @@ -973,13 +973,13 @@ It is possible to use shrinkage priors to penalize large regression coefficients. This can be specified via the argument `shrinkage`. At the moment, only ridge regression is implemented. -Setting `shrinkage = 'ridge'` will impose ridge priors on all regression +Setting `shrinkage = 'ridge'` will impose ridge priors on all regression coefficients. To only use shrinkage for some of the sub-models (main analysis model and covariate models), a vector can be provided that contains the names of the response variables of the models in which shrinkage should be applied, and the type of shrinkage for each of them. -For example, in `mod11a` ridge regression is used for all models, and in +For example, in `mod11a` ridge regression is used for all models, and in `modd11b` only in the models for `SBP` and `educ`: ```{r, warning = FALSE} mod11a <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, @@ -993,5 +993,5 @@ mod11b <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, ``` Ridge regression is implemented as a $\text{Ga}(0.01, 0.01)$ prior for the -precision of the regression coefficients $\beta$ instead of setting this +precision of the regression coefficients $\beta$ instead of setting this precision to a fixed (small) value. diff --git a/vignettes/SelectingParameters.Rmd b/vignettes/SelectingParameters.Rmd index f57fa16d..0dbf8121 100644 --- a/vignettes/SelectingParameters.Rmd +++ b/vignettes/SelectingParameters.Rmd @@ -35,7 +35,7 @@ practice. ## Monitoring parameters -**JointAI** uses [JAGS](http://mcmc-jags.sourceforge.net/) +**JointAI** uses [JAGS](https://mcmc-jags.sourceforge.io/) for performing the MCMC ([Markov Chain Monte Carlo](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo)) sampling. Since JAGS only saves the values of MCMC chains for those parameters/variables for which the user has specified that they should be monitored, this is also the diff --git a/vignettes/SelectingParameters.Rmd.orig b/vignettes/SelectingParameters.Rmd.orig index 4ad4a29f..43370cdb 100644 --- a/vignettes/SelectingParameters.Rmd.orig +++ b/vignettes/SelectingParameters.Rmd.orig @@ -44,7 +44,7 @@ practice. ## Monitoring parameters -**JointAI** uses [JAGS](http://mcmc-jags.sourceforge.net/) +**JointAI** uses [JAGS](https://mcmc-jags.sourceforge.io/) for performing the MCMC ([Markov Chain Monte Carlo](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo)) sampling. Since JAGS only saves the values of MCMC chains for those parameters/variables for which the user has specified that they should be monitored, this is also the From 24307afa285dc605f76baf0554b9d6c85828d8c3 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 12:08:58 +0100 Subject: [PATCH 006/176] fix typos --- R/model_imp.R | 4 ++-- man/model_imp.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/model_imp.R b/R/model_imp.R index 429e9a5a..a8259b33 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -220,7 +220,7 @@ #' ## Survival models with frailties or time-varying covariates #' Random effects specified in brackets can also be used to indicate a #' multi-level structure in survival models, as would, for instance be needed -#' in a multicentre setting, where patients are from multiple hospitals. +#' in a multi-centre setting, where patients are from multiple hospitals. #' #' It also allows to model time-dependent covariates in a proportional #' hazards survival model (using \code{coxph_imp}), also in combination with @@ -246,7 +246,7 @@ #' #' Moreover, it is not possible to use `.` to indicate that all variables in a #' `data.frame` other than the outcome variable should be used as covariates. -#' I.e., a formula `y ~ .` is valid in **JointAI**. +#' I.e., a formula `y ~ .` is not valid in **JointAI**. #' #' #' @details # Data structure diff --git a/man/model_imp.Rd b/man/model_imp.Rd index 163d2808..8e461604 100644 --- a/man/model_imp.Rd +++ b/man/model_imp.Rd @@ -400,7 +400,7 @@ specified per level:\if{html}{\out{
}}\preformatted{rd_ Random effects specified in brackets can also be used to indicate a multi-level structure in survival models, as would, for instance be needed -in a multicentre setting, where patients are from multiple hospitals. +in a multi-centre setting, where patients are from multiple hospitals. It also allows to model time-dependent covariates in a proportional hazards survival model (using \code{coxph_imp}), also in combination with @@ -426,7 +426,7 @@ a log-normal model can be used instead of a normal model. Moreover, it is not possible to use \code{.} to indicate that all variables in a \code{data.frame} other than the outcome variable should be used as covariates. -I.e., a formula \code{y ~ .} is valid in \strong{JointAI}. +I.e., a formula \code{y ~ .} is not valid in \strong{JointAI}. } } From 5013f08e42c3e36274c5bf6374d586f7b12eae6a Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 12:09:13 +0100 Subject: [PATCH 007/176] add note about new/experimental features --- R/model_imp.R | 6 +++++- man/model_imp.Rd | 6 +++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/R/model_imp.R b/R/model_imp.R index a8259b33..349d7351 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -173,10 +173,12 @@ #' variable. #' #' ### Random effects variance-covariance structure +#' (Note: This feature is new and has not been fully tested yet.) +#' #' By default, a block-diagonal structure is assumed for the variance-covariance #' matrices of the random effects in models with random effects. This means that #' per outcome and level random effects are assumed to be correlated, but -#' random effects of different outcomes are modeled as independent. +#' random effects of different outcomes are modelled as independent. #' The argument `rd_vcov` allows the user specify different assumptions about #' these variance-covariance matrices. Implemented structures are `full`, #' `blockdiag` and `indep` (all off-diagonal elements are zero). @@ -482,6 +484,8 @@ #' #' #' @section Custom model parts: +#' (Note: This feature is experimental and has not been fully tested yet.) +#' #' Via the argument `custom` it is possible to provide custom sub-models that #' replace the sub-models that are automatically generated by **JointAI**. #' diff --git a/man/model_imp.Rd b/man/model_imp.Rd index 8e461604..ca6c8f0d 100644 --- a/man/model_imp.Rd +++ b/man/model_imp.Rd @@ -356,10 +356,12 @@ Note that it is not possible to specify multiple models for the same outcome variable. \subsection{Random effects variance-covariance structure}{ +(Note: This feature is new and has not been fully tested yet.) + By default, a block-diagonal structure is assumed for the variance-covariance matrices of the random effects in models with random effects. This means that per outcome and level random effects are assumed to be correlated, but -random effects of different outcomes are modeled as independent. +random effects of different outcomes are modelled as independent. The argument \code{rd_vcov} allows the user specify different assumptions about these variance-covariance matrices. Implemented structures are \code{full}, \code{blockdiag} and \code{indep} (all off-diagonal elements are zero). @@ -651,6 +653,8 @@ part of the regular model formula. \section{Custom model parts}{ +(Note: This feature is experimental and has not been fully tested yet.) + Via the argument \code{custom} it is possible to provide custom sub-models that replace the sub-models that are automatically generated by \strong{JointAI}. From 0231f85ae4b4125f4ba556883a0eb13eaf7f3c63 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 12:09:43 +0100 Subject: [PATCH 008/176] bugfix for the case with function of a variable as auxiliary variable --- R/helpfunctions_divide_matrices.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/helpfunctions_divide_matrices.R b/R/helpfunctions_divide_matrices.R index 5656af76..b36bee6c 100644 --- a/R/helpfunctions_divide_matrices.R +++ b/R/helpfunctions_divide_matrices.R @@ -619,7 +619,8 @@ get_linpreds <- function(fixed, random, data, models, auxvars = NULL, keep_terms <- lvapply(covar_terms, function(k) { check_effect <- try(model.frame(paste0("~", k), testdat), silent = TRUE) - !inherits(check_effect, "try-error") & k %in% names(testdat) + !inherits(check_effect, "try-error") & + all_vars(formula(paste("~", k))) %in% names(testdat) }) covar_terms <- covar_terms[keep_terms] From 0e37b359e31973d2caa3677f96f982085f2ca252 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 12:10:00 +0100 Subject: [PATCH 009/176] ignore bash.exe.stackdump --- .Rbuildignore | 1 + .gitignore | 2 ++ 2 files changed, 3 insertions(+) diff --git a/.Rbuildignore b/.Rbuildignore index 36a6b05a..2a27ed37 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -10,6 +10,7 @@ ^inst/WORDLIST$ ^inst/Ideas\.*$ +bash.exe.stackdump ^cran-comments\.md$ ^docs$ diff --git a/.gitignore b/.gitignore index f1092aca..11d980e1 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,5 @@ vignettes/ModelSpecification_cache docs tests/testthat/*.pdf + +bash.exe.stackdump From a42305020f7d2182092fdccc17fe458c78c3bb6a Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 12:16:49 +0100 Subject: [PATCH 010/176] update news.md --- NEWS.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/NEWS.md b/NEWS.md index aaf24ae8..f0536125 100644 --- a/NEWS.md +++ b/NEWS.md @@ -28,6 +28,9 @@ variance-covariance matrix of the random effects that had the same number of random effects for each outcome, for which the printing of this matrix resulted in an error. +* Bug fix: when using a function of a variable as auxiliary this is now (again) + correctly used as covariate in the linear predictor of covariate models (bug + was introduced in commit 15014dcd) From 8da7e709d1a10b972d89582cd53fbd75bdaef877 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 12:20:56 +0100 Subject: [PATCH 011/176] set version number to 1.0.3 --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index e3e2909a..5733907f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.2.9000 +Version: 1.0.3 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), From 9d01c4f848da6e97af3f9301fa5e1ef7c5a4aa33 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 13:45:17 +0100 Subject: [PATCH 012/176] remove Rdoc badge --- README.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/README.Rmd b/README.Rmd index 94ce5d4b..e71ea2ef 100644 --- a/README.Rmd +++ b/README.Rmd @@ -21,7 +21,6 @@ knitr::opts_chunk$set( [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/JointAI)](https://CRAN.R-project.org/package=JointAI) [![](https://cranlogs.r-pkg.org/badges/grand-total/JointAI)](https://CRAN.R-project.org/package=JointAI) [![Download counter](https://cranlogs.r-pkg.org/badges/JointAI)](https://cran.r-project.org/package=JointAI) -[![Rdoc](https://www.rdocumentation.org/badges/version/JointAI)](https://www.rdocumentation.org/packages/JointAI) [![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://codecov.io/gh/NErler/JointAI) [![Travis-CI Build Status](https://travis-ci.org/NErler/JointAI.svg?branch=master)](https://travis-ci.org/NErler/JointAI) [![R build status](https://github.com/NErler/JointAI/workflows/R-CMD-check/badge.svg)](https://github.com/NErler/JointAI/actions) From 7da877f70fc272ad879ab063ece9ab743b4892b8 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 13:45:29 +0100 Subject: [PATCH 013/176] update workflows --- .github/workflows/R-CMD-check.yaml | 1 - .github/workflows/render-readme.yaml | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 9605d34c..7e3b0686 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -23,7 +23,6 @@ jobs: config: - {os: macOS-latest, r: 'release'} - {os: windows-latest, r: 'release'} - - {os: windows-latest, r: '3.6'} - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'release'} - {os: ubuntu-latest, r: 'oldrel-1'} diff --git a/.github/workflows/render-readme.yaml b/.github/workflows/render-readme.yaml index 7a712677..439edc74 100644 --- a/.github/workflows/render-readme.yaml +++ b/.github/workflows/render-readme.yaml @@ -8,7 +8,7 @@ name: Render README jobs: render: name: Render README - runs-on: ubuntu-16.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: r-lib/actions/setup-r@v1 From 64e932fc0957257c90d5bbfb1971c9ca603de453 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 13:45:45 +0100 Subject: [PATCH 014/176] try skip test on mac --- tests/testthat/test-clm.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/testthat/test-clm.R b/tests/testthat/test-clm.R index c53539af..27e03b1b 100644 --- a/tests/testthat/test-clm.R +++ b/tests/testthat/test-clm.R @@ -240,6 +240,8 @@ test_that("model can be plottet", { test_that("wrong models give errors", { + skip_on_os("mac") + expect_error(clm_imp(y ~ O1 + C1 + C2, data = wideDF)) expect_error(clm_imp(O2 ~ O1 + C1 + C2 + (1 | id), data = longDF, warn = FALSE)) From d21a04124a17ee2c992dd3431b07bff776ba2dfd Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 16:08:03 +0100 Subject: [PATCH 015/176] exclude example to save time --- R/convergence_criteria.R | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/R/convergence_criteria.R b/R/convergence_criteria.R index 55614cbd..2379d41f 100644 --- a/R/convergence_criteria.R +++ b/R/convergence_criteria.R @@ -103,12 +103,16 @@ GR_crit <- function(object, confidence = 0.95, transform = FALSE, #' provides some examples how to specify the argument \code{subset}. #' #' @examples +#' +#' \dontrun{ +#' #' mod <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100) #' #' MC_error(mod) #' #' plot(MC_error(mod), ablinepars = list(lty = 2), #' plotpars = list(pch = 19, col = 'blue')) +#' } #' #' @export MC_error <- function(x, subset = NULL, exclude_chains = NULL, From 4ada81752667661b54b85b369deb9765264e95b7 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 18 Nov 2021 16:08:23 +0100 Subject: [PATCH 016/176] edit cran comments --- cran-comments.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cran-comments.md b/cran-comments.md index ccb760e4..9704b861 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -4,7 +4,7 @@ ### Test environments * local Windows 10, R 4.1.1 -* windows server x64 (via github actions), R 3.6.3, R 4.1.2 +* windows server x64 (via github actions), R 4.1.2 * ubuntu 20.04.3 LTS (via github actions), R 4.0.5, R 4.1.2, devel * win-builder (oldrelease, devel and release) From 50a25e9233bbf43facdbbcad8b65cec8e5f42bb5 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 26 Nov 2021 12:18:13 +0100 Subject: [PATCH 017/176] update citation to JSS paper --- inst/CITATION | 39 ++++++++++++++++----------------------- 1 file changed, 16 insertions(+), 23 deletions(-) diff --git a/inst/CITATION b/inst/CITATION index e6912bc9..76038bba 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -1,24 +1,17 @@ -citHeader("To cite JointAI in publications use:") - -citEntry( - textVersion = - paste("Nicole S. Erler, Dimitris Rizopoulos and Emmanuel M.E.H. Lesaffre (2019).", - "JointAI: Joint Analysis and Imputation of Incomplete Data in R.", - "arXiv e-prints, arXiv:1907.10867, July 2019.", - "URL https://arxiv.org/abs/1907.10867."), - entry = "article", - key = 'JointAI', - title = "{JointAI}: Joint Analysis and Imputation of Incomplete Data in R", - author = personList(as.person("Nicole S. Erler"), - as.person("Dimitris Rizopoulos"), - as.person("Emmanuel M.E.H. Lesaffre")), - journal = "{arXiv e-prints}", - year = "2019", - month = "Jul", - eid = "arXiv:1907.10867", - pages = "arXiv:1907.10867", -archivePrefix = "arXiv", - eprint = "1907.10867", - primaryClass = "stat.ME", - url = "https://arxiv.org/abs/1907.10867" +bibentry(bibtype = "Article", + title = "{JointAI}: Joint Analysis and Imputation of Incomplete Data in {R}", + author = c(person(given = c("Nicole", "S."), + family = "Erler", + email = "n.erler@erasmusmc.nl"), + person(given = "Dimitris", + family = "Rizopoulos"), + person(given = c("Emmanuel", "M.", "E.", "H."), + family = "Lesaffre")), + journal = "Journal of Statistical Software", + year = "2021", + volume = "100", + number = "20", + pages = "1--56", + doi = "10.18637/jss.v100.i20", + header = "To cite JointAI in publications use:" ) From 7153348b7bee12f5c245c400f9845fd39fe23fe5 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 26 Nov 2021 15:54:25 +0100 Subject: [PATCH 018/176] add/update reference to JSS paper --- DESCRIPTION | 3 ++- R/JointAI.R | 8 ++++---- R/list_models.R | 8 ++++---- man/JointAI.Rd | 8 ++++---- man/list_models.Rd | 8 ++++---- 5 files changed, 18 insertions(+), 17 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 5733907f..5ca2176e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -6,7 +6,8 @@ Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", comment = c(ORCID = "0000-0002-9370-6832"))) Description: Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, - survival models, or joint models for longitudinal and survival data. + survival models, or joint models for longitudinal and survival data, as + described in Erler, Rizopoulos and Lesaffre (2021) . Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' diff --git a/R/JointAI.R b/R/JointAI.R index 7ab57165..1c225c33 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -132,10 +132,10 @@ #' Explanation of the statistical method implemented in \strong{JointAI}. #'} #' @references -#' Nicole S. Erler, Dimitris Rizopoulos and Emmanuel M.E.H. Lesaffre (2019). -#' JointAI: Joint Analysis and Imputation of Incomplete Data in R. -#' \emph{arXiv e-prints}, arXiv:1907.10867. -#' URL \href{https://arxiv.org/abs/1907.10867}{https://arxiv.org/abs/1907.10867}. +#' Erler NS, Rizopoulos D, Lesaffre EMEH (2021). +#' "JointAI: Joint Analysis and Imputation of Incomplete Data in R." +#' _Journal of Statistical Software_, *100*(20), 1-56. +#' doi: \href{https://doi.org/10.18637/jss.v100.i20}{10.18637/jss.v100.i20}. #' #' Erler, N.S., Rizopoulos, D., Rosmalen, J., Jaddoe, V.W.V., #' Franco, O. H., & Lesaffre, E.M.E.H. (2016). diff --git a/R/list_models.R b/R/list_models.R index a8db1d35..d6456bf0 100644 --- a/R/list_models.R +++ b/R/list_models.R @@ -50,10 +50,10 @@ #' between multiple imputation and a full Bayesian approach. #' \emph{Statistics in Medicine}, 35(17), 2955-2974. #' -#' Erler, N.S., Rizopoulos D. and Lesaffre E.M.E.H. (2019). -#' JointAI: Joint Analysis and Imputation of Incomplete Data in R. -#' \emph{arXiv e-prints}, arXiv:1907.10867. -#' URL https://arxiv.org/abs/1907.10867. +#' Erler NS, Rizopoulos D, Lesaffre EMEH (2021). +#' "JointAI: Joint Analysis and Imputation of Incomplete Data in R." +#' _Journal of Statistical Software_, *100*(20), 1-56. +#' doi: \href{https://doi.org/10.18637/jss.v100.i20}{10.18637/jss.v100.i20}. #' #' #' @examples diff --git a/man/JointAI.Rd b/man/JointAI.Rd index 6f9998bd..e0f37e80 100644 --- a/man/JointAI.Rd +++ b/man/JointAI.Rd @@ -143,10 +143,10 @@ Explanation of the statistical method implemented in \strong{JointAI}. } \references{ -Nicole S. Erler, Dimitris Rizopoulos and Emmanuel M.E.H. Lesaffre (2019). -JointAI: Joint Analysis and Imputation of Incomplete Data in R. -\emph{arXiv e-prints}, arXiv:1907.10867. -URL \href{https://arxiv.org/abs/1907.10867}{https://arxiv.org/abs/1907.10867}. +Erler NS, Rizopoulos D, Lesaffre EMEH (2021). +"JointAI: Joint Analysis and Imputation of Incomplete Data in R." +\emph{Journal of Statistical Software}, \emph{100}(20), 1-56. +doi: \href{https://doi.org/10.18637/jss.v100.i20}{10.18637/jss.v100.i20}. Erler, N.S., Rizopoulos, D., Rosmalen, J., Jaddoe, V.W.V., Franco, O. H., & Lesaffre, E.M.E.H. (2016). diff --git a/man/list_models.Rd b/man/list_models.Rd index 7dc987cd..eab13d3a 100644 --- a/man/list_models.Rd +++ b/man/list_models.Rd @@ -72,8 +72,8 @@ Dealing with missing covariates in epidemiologic studies: A comparison between multiple imputation and a full Bayesian approach. \emph{Statistics in Medicine}, 35(17), 2955-2974. -Erler, N.S., Rizopoulos D. and Lesaffre E.M.E.H. (2019). -JointAI: Joint Analysis and Imputation of Incomplete Data in R. -\emph{arXiv e-prints}, arXiv:1907.10867. -URL https://arxiv.org/abs/1907.10867. +Erler NS, Rizopoulos D, Lesaffre EMEH (2021). +"JointAI: Joint Analysis and Imputation of Incomplete Data in R." +\emph{Journal of Statistical Software}, \emph{100}(20), 1-56. +doi: \href{https://doi.org/10.18637/jss.v100.i20}{10.18637/jss.v100.i20}. } From cf53ce8cddcb4292bd2c7e1002e25e0520dac051 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 26 Nov 2021 15:54:57 +0100 Subject: [PATCH 019/176] use \dontrun in examples --- R/get_MIdat.R | 4 +++- R/plot_imp_distr.R | 4 +++- R/plots.R | 3 ++- R/summary.JointAI.R | 4 +++- man/MC_error.Rd | 4 ++++ man/densplot.Rd | 3 ++- man/get_MIdat.Rd | 4 +++- man/plot_imp_distr.Rd | 4 +++- man/summary.JointAI.Rd | 4 +++- 9 files changed, 26 insertions(+), 8 deletions(-) diff --git a/R/get_MIdat.R b/R/get_MIdat.R index 19662bd1..4dcfd596 100644 --- a/R/get_MIdat.R +++ b/R/get_MIdat.R @@ -42,6 +42,8 @@ #' @seealso \code{\link{plot_imp_distr}} #' #' @examples +#' +#' \dontrun{ #' # fit a model and monitor the imputed values with #' # monitor_params = c(imps = TRUE) #' @@ -52,7 +54,7 @@ #' MIs <- get_MIdat(mod, m = 3, seed = 123) #' #' -#' \dontrun{ +#' #' # Example 2: with export for SPSS #' # (here: to the temporary directory "temp_dir") #' diff --git a/R/plot_imp_distr.R b/R/plot_imp_distr.R index f0a19747..a3d179f6 100644 --- a/R/plot_imp_distr.R +++ b/R/plot_imp_distr.R @@ -16,11 +16,13 @@ #' @export #' #' @examples +#' +#' \dontrun{ #' mod <- lme_imp(y ~ C1 + c2 + B2 + C2, random = ~ 1 | id, data = longDF, #' n.iter = 200, monitor_params = c(imps = TRUE), mess = FALSE) #' impDF <- get_MIdat(mod, m = 5) #' plot_imp_distr(impDF, id = "id", ncol = 3) -#' +#' } plot_imp_distr <- function(data, imp = 'Imputation_', id = '.id', rownr = '.rownr', diff --git a/R/plots.R b/R/plots.R index c660922e..b2d01758 100644 --- a/R/plots.R +++ b/R/plots.R @@ -130,6 +130,7 @@ traceplot.JointAI <- function(object, start = NULL, end = NULL, thin = NULL, #' @param ... additional parameters passed to \code{plot()} #' @examples #' +#' \dontrun{ #' # fit a JointAI object: #' mod <- lm_imp(y ~ C1 + C2 + M1, data = wideDF, n.iter = 100) #' @@ -165,7 +166,7 @@ traceplot.JointAI <- function(object, start = NULL, end = NULL, thin = NULL, #' xlab("value") + #' theme(legend.position = 'bottom') + #' scale_color_brewer(palette = 'Dark2', name = 'chain') -#' +#' } #' #' @seealso #' The vignette diff --git a/R/summary.JointAI.R b/R/summary.JointAI.R index 7edd9d2a..588de9db 100644 --- a/R/summary.JointAI.R +++ b/R/summary.JointAI.R @@ -17,12 +17,14 @@ #' @param \dots currently not used #' #' @examples +#' +#' \dontrun{ #' mod1 <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100) #' #' summary(mod1, missinfo = TRUE) #' coef(mod1) #' confint(mod1) -#' +#' } #' #' @seealso The model fitting functions \code{\link{lm_imp}}, #' \code{\link{glm_imp}}, \code{\link{clm_imp}}, \code{\link{lme_imp}}, diff --git a/man/MC_error.Rd b/man/MC_error.Rd index 48a88901..5ba4b21a 100644 --- a/man/MC_error.Rd +++ b/man/MC_error.Rd @@ -101,12 +101,16 @@ longer than a few characters. The plot margin can be adjusted (globally) using the argument \code{"mar"} in \code{\link[graphics]{par}}. } \examples{ + +\dontrun{ + mod <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100) MC_error(mod) plot(MC_error(mod), ablinepars = list(lty = 2), plotpars = list(pch = 19, col = 'blue')) +} } \references{ diff --git a/man/densplot.Rd b/man/densplot.Rd index 90e48217..bb0018ed 100644 --- a/man/densplot.Rd +++ b/man/densplot.Rd @@ -72,6 +72,7 @@ the MCMC sample of an object of class "JointAI". } \examples{ +\dontrun{ # fit a JointAI object: mod <- lm_imp(y ~ C1 + C2 + M1, data = wideDF, n.iter = 100) @@ -107,7 +108,7 @@ densplot(mod, use_ggplot = TRUE) + xlab("value") + theme(legend.position = 'bottom') + scale_color_brewer(palette = 'Dark2', name = 'chain') - +} } \seealso{ diff --git a/man/get_MIdat.Rd b/man/get_MIdat.Rd index bb89534e..3155659d 100644 --- a/man/get_MIdat.Rd +++ b/man/get_MIdat.Rd @@ -62,6 +62,8 @@ argument \code{monitor_params} in \code{\link[JointAI:model_imp]{*_imp}}. } \examples{ + +\dontrun{ # fit a model and monitor the imputed values with # monitor_params = c(imps = TRUE) @@ -72,7 +74,7 @@ mod <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, MIs <- get_MIdat(mod, m = 3, seed = 123) -\dontrun{ + # Example 2: with export for SPSS # (here: to the temporary directory "temp_dir") diff --git a/man/plot_imp_distr.Rd b/man/plot_imp_distr.Rd index dae12982..4447138c 100644 --- a/man/plot_imp_distr.Rd +++ b/man/plot_imp_distr.Rd @@ -33,9 +33,11 @@ Plots densities and bar plots of the observed and imputed values in a long-format dataset (multiple imputed datasets stacked onto each other). } \examples{ + +\dontrun{ mod <- lme_imp(y ~ C1 + c2 + B2 + C2, random = ~ 1 | id, data = longDF, n.iter = 200, monitor_params = c(imps = TRUE), mess = FALSE) impDF <- get_MIdat(mod, m = 5) plot_imp_distr(impDF, id = "id", ncol = 3) - +} } diff --git a/man/summary.JointAI.Rd b/man/summary.JointAI.Rd index 193a42e4..a89cee25 100644 --- a/man/summary.JointAI.Rd +++ b/man/summary.JointAI.Rd @@ -86,12 +86,14 @@ Obtain and print the \code{summary}, (fixed effects) coefficients class 'JointAI'. } \examples{ + +\dontrun{ mod1 <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100) summary(mod1, missinfo = TRUE) coef(mod1) confint(mod1) - +} } \seealso{ From 49aa11243197f79220ec759ab6a104ffa5c0c389 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 26 Nov 2021 16:14:22 +0100 Subject: [PATCH 020/176] edit cran comment --- cran-comments.md | 33 ++++++++++++++++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) diff --git a/cran-comments.md b/cran-comments.md index 9704b861..22f458ac 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -9,14 +9,45 @@ * win-builder (oldrelease, devel and release) + ### R CMD check results -0 errors | 0 warnings | 0 notes +0 errors | 0 warnings | 1 note + +NOTE: + +Possibly misspelled words in DESCRIPTION: + Erler (10:18) + Lesaffre (10:40) + Rizopoulos (10:25) + +Found the following (possibly) invalid URLs: + URL: https://doi.org/10.18637/jss.v100.i20 + From: man/JointAI.Rd + man/list_models.Rd + Status: 404 + Message: Not Found + +Found the following (possibly) invalid DOIs: + DOI: 10.18637/jss.v100.i20 + From: DESCRIPTION + inst/CITATION + Status: Not Found + Message: 404 + + +REPLY: +The DOI/URL in the CITATION, DESCRIPTION and \reference section of man/JointAI.Rd +and man/list_models.Rd is for a new JSS publication that will be registered +after publication on CRAN. (And the names of the authors in the DESCRIPTION +are fact spelled correctly.) + ### Reverse dependencies There are no reverse dependencies. + --- # JointAI (version 1.0.2) From 38f72f802ca5d0b06f175366408ef141b0becaba Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 28 Nov 2021 10:12:08 +0100 Subject: [PATCH 021/176] fixed use of doi with \doi instead of \href --- R/JointAI.R | 2 +- R/list_models.R | 2 +- cran-comments.md | 27 +++++++++++++++++++++++++++ man/JointAI.Rd | 2 +- man/list_models.Rd | 2 +- 5 files changed, 31 insertions(+), 4 deletions(-) diff --git a/R/JointAI.R b/R/JointAI.R index 1c225c33..0844287f 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -135,7 +135,7 @@ #' Erler NS, Rizopoulos D, Lesaffre EMEH (2021). #' "JointAI: Joint Analysis and Imputation of Incomplete Data in R." #' _Journal of Statistical Software_, *100*(20), 1-56. -#' doi: \href{https://doi.org/10.18637/jss.v100.i20}{10.18637/jss.v100.i20}. +#' \doi{10.18637/jss.v100.i20}. #' #' Erler, N.S., Rizopoulos, D., Rosmalen, J., Jaddoe, V.W.V., #' Franco, O. H., & Lesaffre, E.M.E.H. (2016). diff --git a/R/list_models.R b/R/list_models.R index d6456bf0..b0a54132 100644 --- a/R/list_models.R +++ b/R/list_models.R @@ -53,7 +53,7 @@ #' Erler NS, Rizopoulos D, Lesaffre EMEH (2021). #' "JointAI: Joint Analysis and Imputation of Incomplete Data in R." #' _Journal of Statistical Software_, *100*(20), 1-56. -#' doi: \href{https://doi.org/10.18637/jss.v100.i20}{10.18637/jss.v100.i20}. +#' \doi{10.18637/jss.v100.i20}. #' #' #' @examples diff --git a/cran-comments.md b/cran-comments.md index 22f458ac..7fef5077 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,5 +1,6 @@ # JointAI (version 1.0.3) + ## Round 1 ### Test environments @@ -48,6 +49,32 @@ are fact spelled correctly.) There are no reverse dependencies. + +### Reviewer comments +2021-11-27 Uwe Ligges + +``` +Thanks, we see: + + + Found the following URLs which should use \doi (with the DOI name only): + File 'JointAI.Rd': + https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.18637%2Fjss.v100.i20&data=04%7C01%7Cn.erler%40erasmusmc.nl%7C931c6a6ed615439baa0508d9b17be3b8%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C0%7C637735967925897409%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=GWvnOg8CZdyClEWsYiYbthNN6SZtZQzNmPTb0ROsIDQ%3D&reserved=0 + File 'list_models.Rd': + https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.18637%2Fjss.v100.i20&data=04%7C01%7Cn.erler%40erasmusmc.nl%7C931c6a6ed615439baa0508d9b17be3b8%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C0%7C637735967925907404%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=Xq4TyvG66rGkvJF7n75wwPcEMLUu%2BSul2mB5qr7KXNg%3D&reserved=0 + +Please fix and resubmit. +``` + + +## Round 2 +### Submission comments +2021-11-28 + +I've fixed the DOIs the two references. + + + --- # JointAI (version 1.0.2) diff --git a/man/JointAI.Rd b/man/JointAI.Rd index e0f37e80..e3b80ea9 100644 --- a/man/JointAI.Rd +++ b/man/JointAI.Rd @@ -146,7 +146,7 @@ Explanation of the statistical method implemented in \strong{JointAI}. Erler NS, Rizopoulos D, Lesaffre EMEH (2021). "JointAI: Joint Analysis and Imputation of Incomplete Data in R." \emph{Journal of Statistical Software}, \emph{100}(20), 1-56. -doi: \href{https://doi.org/10.18637/jss.v100.i20}{10.18637/jss.v100.i20}. +\doi{10.18637/jss.v100.i20}. Erler, N.S., Rizopoulos, D., Rosmalen, J., Jaddoe, V.W.V., Franco, O. H., & Lesaffre, E.M.E.H. (2016). diff --git a/man/list_models.Rd b/man/list_models.Rd index eab13d3a..dd090239 100644 --- a/man/list_models.Rd +++ b/man/list_models.Rd @@ -75,5 +75,5 @@ between multiple imputation and a full Bayesian approach. Erler NS, Rizopoulos D, Lesaffre EMEH (2021). "JointAI: Joint Analysis and Imputation of Incomplete Data in R." \emph{Journal of Statistical Software}, \emph{100}(20), 1-56. -doi: \href{https://doi.org/10.18637/jss.v100.i20}{10.18637/jss.v100.i20}. +\doi{10.18637/jss.v100.i20}. } From 20632463b73d5172c9e1c6caae59821762bcbe81 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 31 Jan 2022 10:53:07 +0100 Subject: [PATCH 022/176] set version number to dev --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 5ca2176e..e8a46205 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.3 +Version: 1.0.3.9000 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), From 2282466781b1cee68fcd2e6422432990f6beb38e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 31 Jan 2022 10:55:02 +0100 Subject: [PATCH 023/176] add argument to md_pattern to prevent sorting of columns --- NEWS.md | 11 ++++++++++- R/md_pattern.R | 16 ++++++++++++---- man/md_pattern.Rd | 6 +++++- 3 files changed, 27 insertions(+), 6 deletions(-) diff --git a/NEWS.md b/NEWS.md index f0536125..b36c9bb8 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,15 @@ # JointAI Development Vesion +## New features +* `md_pattern()` has an additional argument `sort_columns` to provide the option to switch off the sorting of columns by number of missing values. + + +----------------------------------------------------------------------------- + +# JointAI 1.0.3 + + ## New features * `custom`: new argument in the main analysis functions that allows the user to replace the JAGS syntax for sub-models with custom syntax. The argument @@ -34,7 +43,7 @@ --------------------------------------------------------------------------------- +----------------------------------------------------------------------------- # JointAI 1.0.2 diff --git a/R/md_pattern.R b/R/md_pattern.R index 008c6c19..36ffabcc 100644 --- a/R/md_pattern.R +++ b/R/md_pattern.R @@ -15,6 +15,8 @@ #' @param ylab y-axis label #' @inheritParams ggplot2::theme #' @importFrom rlang .data +#' @param sort_columns logical; should the columns be sorted by number of missing +#' values? (default is `TRUE`) #' @param ... optional additional parameters, currently not used #' #' @seealso See the vignette @@ -36,7 +38,8 @@ md_pattern <- function(data, color = c(grDevices::grey(0.1), border = grDevices::grey(0.5), plot = TRUE, pattern = FALSE, print_xaxis = TRUE, ylab = 'Number of observations per pattern', - print_yaxis = TRUE, legend.position = 'bottom', ...) { + print_yaxis = TRUE, legend.position = 'bottom', + sort_columns = TRUE, ...) { naX <- ifelse(is.na(data), 0, 1) unaX <- unique(naX) @@ -49,14 +52,19 @@ md_pattern <- function(data, color = c(grDevices::grey(0.1), tab <- table(NApat) Npat <- tab[match(NAupat, names(tab))] + # sort rows unaX <- unaX[order(Npat, decreasing = TRUE), ] Npat <- sort(Npat, decreasing = TRUE) rownames(unaX) <- rev(seq_len(nrow(unaX))) - vars <- colnames(unaX)[order(Nmis)] - unaX <- unaX[, order(Nmis)] + if (sort_columns) { + vars <- colnames(unaX)[order(Nmis)] + unaX <- unaX[, order(Nmis)] + Nmis <- sort(Nmis) + } else { + vars <- colnames(unaX) + } colnames(unaX) <- seq_len(ncol(unaX)) - Nmis <- sort(Nmis) if (plot) { diff --git a/man/md_pattern.Rd b/man/md_pattern.Rd index 2a22cfee..668e5549 100644 --- a/man/md_pattern.Rd +++ b/man/md_pattern.Rd @@ -7,7 +7,8 @@ md_pattern(data, color = c(grDevices::grey(0.1), grDevices::grey(0.7)), border = grDevices::grey(0.5), plot = TRUE, pattern = FALSE, print_xaxis = TRUE, ylab = "Number of observations per pattern", - print_yaxis = TRUE, legend.position = "bottom", ...) + print_yaxis = TRUE, legend.position = "bottom", sort_columns = TRUE, + ...) } \arguments{ \item{data}{data frame} @@ -31,6 +32,9 @@ and y-axis (on the right) be printed?} \item{legend.position}{the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector)} +\item{sort_columns}{logical; should the columns be sorted by number of missing +values? (default is \code{TRUE})} + \item{...}{optional additional parameters, currently not used} } \description{ From 60a56f943449c24c9da37fb527f8bad03c52fe6d Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 31 Jan 2022 15:44:28 +0100 Subject: [PATCH 024/176] add function get_datlvls() to simultaneously check level of all variabbles in a data.frame to increase efficiency --- R/divide_matrices.R | 6 ++++-- R/get_modeltypes.R | 19 ++++++++++++++-- R/helpfunctions.R | 36 ++++++++++++++++++++++++++++++- R/helpfunctions_divide_matrices.R | 18 ++++++++++------ R/plots.R | 3 ++- R/summary.JointAI.R | 8 ++++--- 6 files changed, 75 insertions(+), 15 deletions(-) diff --git a/R/divide_matrices.R b/R/divide_matrices.R index 47dcf9de..a5df64ce 100644 --- a/R/divide_matrices.R +++ b/R/divide_matrices.R @@ -105,8 +105,10 @@ divide_matrices <- function(data, fixed, random = NULL, analysis_type, MX <- MX[, unique(colnames(MX))] # identify levels of all variables - Mlvls <- apply(MX, 2, check_varlevel, groups = groups, - group_lvls = identify_level_relations(groups)) + # Mlvls <- apply(MX, 2, check_varlevel, groups = groups, + # group_lvls = identify_level_relations(groups)) + Mlvls <- get_datlvls(MX, groups) + Mlvls <- setNames(paste0("M_", Mlvls), names(Mlvls)) diff --git a/R/get_modeltypes.R b/R/get_modeltypes.R index 1b0234e7..42ad549c 100644 --- a/R/get_modeltypes.R +++ b/R/get_modeltypes.R @@ -67,13 +67,28 @@ get_models <- function(fixed, random = NULL, data, auxvars = NULL, group_lvls <- colSums(!identify_level_relations(groups)) max_lvl <- max(group_lvls) + + dat_all <- if (any(!allvars %in% names(data))) { + cbind(data[, allvars[allvars %in% names(data)], drop = FALSE], + sapply(allvars[!allvars %in% names(data)], function(x) { + eval(parse(text = x), envir = data) + }, simplify = FALSE) + ) + } else { + data[, allvars, drop = FALSE] + } + + all_lvls <- get_datlvls(dat_all, groups) + + + + if (length(allvars) > 0) { varinfo <- sapply(allvars, function(k) { x <- eval(parse(text = k), envir = data) out <- k %in% names(fixed) - lvl <- group_lvls[ - check_varlevel(x, groups, group_lvls = identify_level_relations(groups))] + lvl <- group_lvls[all_lvls[k]] nmis <- sum(is.na(x[match(unique(groups[[names(lvl)]]), groups[[names(lvl)]])])) nlev <- length(levels(x)) diff --git a/R/helpfunctions.R b/R/helpfunctions.R index 6d8e53c8..3d14f4b1 100644 --- a/R/helpfunctions.R +++ b/R/helpfunctions.R @@ -133,7 +133,8 @@ identify_level_relations <- function(grouping) { # turn the list into a matrix, with the different levels as columns g <- do.call(cbind, grouping) # check if the grouping information varies within each of the clusters - res <- apply(g, 2L, check_cluster, grouping = grouping) + res <- apply(g, 2L, check_cluster, grouping = grouping, simplify = FALSE) + res <- do.call(cbind, res) if (!is.matrix(res)) res <- t(res) @@ -144,6 +145,39 @@ identify_level_relations <- function(grouping) { } + +# More efficient alternative to running check_varlevel for each column of a +# data.frame separately +get_datlvls <- function(data, groups) { + + if (!inherits(data, "data.frame")) + data <- as.data.frame(data) + + + + clus <- lapply(groups, function(k) { + # for each level of grouping, compare the original vector with a + # reconstructed vector in which the first element per group is repeated + # for each group member + d_rep <- data[match(unique(k), k)[match(k, unique(k))], , drop = FALSE] + !unlist(Map(identical, d_rep, data)) + }) + clus <- do.call(cbind, clus) + + lvl_rel <- identify_level_relations(groups) + + k <- match( + data.frame(t(clus)), + data.frame(lvl_rel[colnames(clus), ]) + ) + + k[is.na(k)] <- which.max(colSums(!lvl_rel)) + setNames(colnames(clus)[k], rownames(clus)) + +} + + + # used in divide_matrices, get_modeltypes, helpfunctions_checks, # helpfunctions_formulas, plots, simulate_data (2020-06-09) check_varlevel <- function(x, groups, group_lvls = NULL) { diff --git a/R/helpfunctions_divide_matrices.R b/R/helpfunctions_divide_matrices.R index b36bee6c..1d6d09d9 100644 --- a/R/helpfunctions_divide_matrices.R +++ b/R/helpfunctions_divide_matrices.R @@ -20,8 +20,9 @@ reformat_longsurvdata <- function(data, fixed, random, timevar, idvar) { survinfo <- extract_outcome(fixed)[grepl("^Surv\\(", fixed)] # identify levels of all variables in the data - datlvls <- cvapply(data, check_varlevel, groups = groups, - group_lvls = identify_level_relations(groups)) + datlvls <- get_datlvls(data, groups) + # datlvls <- cvapply(data, check_varlevel, groups = groups, + # group_lvls = identify_level_relations(groups)) # if there are multiple survival variables and some time-varying variables if (length(survinfo) > 0L & any(datlvls[unlist(survinfo)] != "lvlone")) { @@ -105,7 +106,8 @@ fill_locf <- function(data, fixed, random, auxvars, timevar, groups) { survout <- extract_outcome(fixed)[grepl("^Surv\\(", fixed)] # identify data levels - datlvls <- cvapply(data[, allvars], check_varlevel, groups = groups) + # datlvls <- cvapply(data[, allvars], check_varlevel, groups = groups) + datlvls <- get_datlvls(data[, allvars], groups) surv_lvl <- unique(datlvls[unlist(survout)]) # identify covariates in the survival models @@ -219,7 +221,9 @@ extract_outcome_data <- function(fixed, random = NULL, data, } else { outcomes[[i]] <- split_outcome(lhs = extract_lhs(fixed[[i]]), data = data) nlev <- ivapply(outcomes[[i]], function(x) length(levels(x))) - varlvl <- cvapply(outcomes[[i]], check_varlevel, groups = groups) + # varlvl <- cvapply(outcomes[[i]], check_varlevel, groups = groups) + varlvl <- get_datlvls(outcomes[[i]], groups) + if (any(nlev > 2L)) { # ordinal variables have values 1, 2, 3, ... @@ -566,8 +570,10 @@ get_linpreds <- function(fixed, random, data, models, auxvars = NULL, # identify the levels of all variables - lvl <- cvapply(data[, allvars, drop = FALSE], check_varlevel, groups = groups, - group_lvls = identify_level_relations(groups)) + # lvl <- cvapply(data[, allvars, drop = FALSE], check_varlevel, groups = groups, + # group_lvls = identify_level_relations(groups)) + lvl <- get_datlvls(data[, allvars, drop = FALSE], groups) + group_lvls <- colSums(!identify_level_relations(groups)) # make a subset containing only covariates diff --git a/R/plots.R b/R/plots.R index b2d01758..2baee73e 100644 --- a/R/plots.R +++ b/R/plots.R @@ -460,7 +460,8 @@ plot_all <- function(data, nrow = NULL, ncol = NULL, if (!is.null(idvars)) { groups <- data[, idvars, drop = FALSE] groups$lvlone <- seq_len(nrow(groups)) - varlvls <- sapply(data, check_varlevel, groups = groups) + varlvls <- get_datlvls(data, groups) + # varlvls <- sapply(data, check_varlevel, groups = groups) } for (i in names(data)) { diff --git a/R/summary.JointAI.R b/R/summary.JointAI.R index 588de9db..7fb4d2a9 100644 --- a/R/summary.JointAI.R +++ b/R/summary.JointAI.R @@ -696,7 +696,8 @@ get_missinfo <- function(object) { groups <- object$Mlist$groups - data_lvls <- cvapply(object$data[, allvars], check_varlevel, groups = groups) + # data_lvls <- cvapply(object$data[, allvars], check_varlevel, groups = groups) + data_lvls <- get_datlvls(object$data[, allvars, drop = FALSE], groups) complcases <- lapply(names(groups), function(k) { cc <- complete.cases(object$data[match(unique(groups[[k]]), groups[[k]]), @@ -712,8 +713,9 @@ get_missinfo <- function(object) { ), check.names = FALSE, row.names = k) }) - dat_lvls <- sapply(object$data[allvars], check_varlevel, - groups = object$Mlist$groups) + # dat_lvls <- sapply(object$data[allvars], check_varlevel, + # groups = object$Mlist$groups) + dat_lvls <- get_datlvls(object$data[allvars], object$Mlist$groups) miss_list <- sapply(unique(dat_lvls), function(lvl) { subdat <- object$data[match(unique(object$Mlist$groups[[lvl]]), From 162d93be8834268814de9a3636f2113c44853e5d Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 4 Feb 2022 14:20:04 +0100 Subject: [PATCH 025/176] bugfix: formula.JointAI() did not return a formula when add_samples was used --- NEWS.md | 3 +++ R/summary.JointAI.R | 23 +++++++++++++++++------ 2 files changed, 20 insertions(+), 6 deletions(-) diff --git a/NEWS.md b/NEWS.md index b36c9bb8..28c86cc1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -5,6 +5,9 @@ * `md_pattern()` has an additional argument `sort_columns` to provide the option to switch off the sorting of columns by number of missing values. +## Bug fixes +* `formula()` did not return a formula when `add_samples()` was used. + ----------------------------------------------------------------------------- # JointAI 1.0.3 diff --git a/R/summary.JointAI.R b/R/summary.JointAI.R index 7fb4d2a9..3871647a 100644 --- a/R/summary.JointAI.R +++ b/R/summary.JointAI.R @@ -426,13 +426,24 @@ formula.JointAI <- function(x, ...) { if (!(inherits(x, "JointAI") | inherits(x, "JointAI_errored"))) errormsg("Use only %s with objects.", sQuote("JointAI")) - if (is.null(x$call$formula)) { - as.formula(x$call$fixed) - } else { - if (inherits(x$call$formula, "list")) { - x$call$formula + if (inherits(x$call, "call")) { + if (is.null(x$call$formula)) { + as.formula(x$call$fixed) + } else { + fmla <- eval(x$call$formula) + if (inherits(eval(x$call$formula), "list")) { + x$call$formula + } else { + as.formula(x$call$formula) + } + } + } else if (inherits(x$call, "list")) { + fmla_list <- lapply(x$call, "[[", "formula") + fmla_list <- lapply(fmla[lvapply(fmla, inherits, "call")], as.formula) + if (length(fmla_list) == 1L) { + fmla_list[[1]] } else { - as.formula(x$call$formula) + fmla_list } } } From daa71376ec4eb8d5afa06dab081db58cd0bd4e39 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 4 Feb 2022 14:31:03 +0100 Subject: [PATCH 026/176] bugfix formula.JointAI() --- R/summary.JointAI.R | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/R/summary.JointAI.R b/R/summary.JointAI.R index 3871647a..7983d3df 100644 --- a/R/summary.JointAI.R +++ b/R/summary.JointAI.R @@ -438,8 +438,14 @@ formula.JointAI <- function(x, ...) { } } } else if (inherits(x$call, "list")) { - fmla_list <- lapply(x$call, "[[", "formula") - fmla_list <- lapply(fmla[lvapply(fmla, inherits, "call")], as.formula) + fmla_list <- lapply(x$call, function(k) { + if (!is.null(k$formula)) { + k$formula + } else { + k$fixed + } + }) + fmla_list <- lapply(fmla_list[lvapply(fmla_list, inherits, "call")], as.formula) if (length(fmla_list) == 1L) { fmla_list[[1]] } else { From 0e93030cf41363c6d5e92540c5638ad35869091e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:32:50 +0100 Subject: [PATCH 027/176] update check_formula_list and add internal documentation --- R/helpfunctions_formulas.R | 39 ++++++++++++++++++++++++++------------ 1 file changed, 27 insertions(+), 12 deletions(-) diff --git a/R/helpfunctions_formulas.R b/R/helpfunctions_formulas.R index 3b0591ed..40a06476 100644 --- a/R/helpfunctions_formulas.R +++ b/R/helpfunctions_formulas.R @@ -1,27 +1,42 @@ +#' Check/convert formula to list +#' +#' Check if an object is a list of formulas and/or NULL elements and convert +#' it to a list if it is a formula object. +#' +#' Internal function; used in many help functions, get_refs, *_imp, predict +#' (2022-02-05) +#' @param formula any object +#' @param convert logical; should the input be converted to a list? +#' @keywords internal +check_formula_list <- function(formula, convert = TRUE) { -# used in many help functions, get_refs, *_imp, predict (2020-06-09) -check_formula_list <- function(formula) { - # check if a formula is a list, and turn it into a list if it is not. - - # if formula is NULL, return NULL if (is.null(formula)) { return(NULL) } - # if formula is not a list, make it one - if (!is.list(formula)) - formula <- list(formula) + # if formula is a formula, turn it into a list + if (inherits(formula, "formula")) { + if (convert) { + formula <- list(formula) + } + } else if (inherits(formula, "list")) { + # check if all elements are either formulas or NULL + fmla_elmt <- lvapply(formula, inherits, "formula") + null_elmt <- lvapply(formula, is.null) - # check that all elements of formula are either formulas or NULL - if (!all(lvapply(formula, function(x) inherits(x, "formula") | is.null(x)))) - errormsg("At least one element of the provided formula is not of class + if (!all(fmla_elmt | null_elmt)) { + errormsg("At least one element of the provided object is not of class %s.", dQuote("formula")) + } + } else { + errormsg("The provided object is not of class %s nor is it a %s.", + dQuote("formula"), dQuote("list")) + } formula } - # used in divide_matrices, get_models, various help functions, # predict (2020-06-09) extract_id <- function(random, warn = TRUE) { From 3b0b726a337be155d5418032c4d006296f1b3f37 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:33:44 +0100 Subject: [PATCH 028/176] update extract_lhs() and added internal documentation --- R/helpfunctions_formulas.R | 40 +++++++++++++++++++++++--------------- 1 file changed, 24 insertions(+), 16 deletions(-) diff --git a/R/helpfunctions_formulas.R b/R/helpfunctions_formulas.R index 40a06476..5e397bb5 100644 --- a/R/helpfunctions_formulas.R +++ b/R/helpfunctions_formulas.R @@ -123,11 +123,22 @@ extract_outcome <- function(fixed) { } -# used in various help functions (2020-06-09) +#' Extract the left hand side of a formula +#' +#' Extracts the left hand side from a `formula` object and returns it as +#' character string. +#' Relevant, for example, for survival formulas, where `Surv(...)` is a +#' `call`. +#' +#' Internal; used in various help functions (2022-02-05) +#' +#' @param formula a `formula` object (NOT a `list` of formulas) +#' +#' @returns A character string. +#' +#' @keywords internal +#' extract_lhs <- function(formula) { - # Extract the outcome formula from a formula - # (relevant for example for survival formulas, where Surv(...) is a formula) - # - formula: two-sided formula (no list of formulas!!!) if (is.null(formula)) { return(NULL) @@ -135,8 +146,7 @@ extract_lhs <- function(formula) { # check that formula is a formula object if (!inherits(formula, "formula")) - errormsg("The provided formula is not a %s object.", - dQuote("formula")) + errormsg("The provided formula is not a %s object.", dQuote("formula")) # check that the formula has a LHS @@ -144,17 +154,15 @@ extract_lhs <- function(formula) { errormsg("Unable to extract response from the formula.") - # get the LHS of the formula - # lhs <- sub("[[:space:]]*\\~[[:print:]]*", "", - # deparse(formula, width.cutoff = 500L)) - - if (length(formula) == 3) { - deparse(formula[[2]], width.cutoff = 500L) - } else if (length(formula) == 2) { - "" + if (length(formula) == 3L) { + deparse(formula[[2L]], width.cutoff = 500L) + # } else if (length(formula) == 2L) { + # "" } else { - errormsg("Unable to extract respone from the formula. - Formula is not of length 2 or 3.") + # not sure this is ever needed... Can't come up with an example for a + # formula that has a response and length 2. + errormsg("Unable to extract a response from the formula. + Formula is not of length 3.") } } From 9c3fc9d77621bdb1fe95a1c7c642ffa7a83118aa Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:34:30 +0100 Subject: [PATCH 029/176] updated remove_lhs and added internal documentation --- R/helpfunctions_formulas.R | 39 ++++++++++++++++---------------------- 1 file changed, 16 insertions(+), 23 deletions(-) diff --git a/R/helpfunctions_formulas.R b/R/helpfunctions_formulas.R index 5e397bb5..00724688 100644 --- a/R/helpfunctions_formulas.R +++ b/R/helpfunctions_formulas.R @@ -167,36 +167,29 @@ extract_lhs <- function(formula) { } -# used in divide_matrices, get_models and help functions (2020-06-09) #' Remove the left hand side of a (list of) formula(s) +#' +#' Internal function; used in divide_matrices, get_models and help functions +#' (2022-02-05) +#' #' @param formula a formula object or a list of formula objects -#' @export +#' +#' @returns A `formula` object or a `list` of `formula` objects. +#' #' @keywords internal +#' remove_lhs <- function(formula) { - # if formula is not a list, turn into list - formula <- check_formula_list(formula) - if (is.null(formula)) { - return(NULL) - } + formula <- check_formula_list(formula, convert = FALSE) + if (is.null(formula)) + return(NULL) - lapply(formula, function(x) { - as.formula(paste("~", paste(deparse(x[[length(x)]]), collapse = " "))) - }) - - # lapply(formula, function(x) { - # if (!is.null(x)) { - # lhs <- try(extract_lhs(x), silent = TRUE) - # if (inherits(lhs, "try-error")) { - # x - # } else { - # clean_lhs <- gsub("([^\\])\\(", "\\1\\\\(", extract_lhs(x)) - # as.formula(gsub(paste0("^", clean_lhs, "[[ ]]*~"), "~", - # deparse(x, width.cutoff = 500L))) - # } - # } - # }) + if (inherits(formula, "list")) { + lapply(formula, remove_lhs) + } else { + formula(delete.response(terms(formula))) + } } From f99a09d9ca04bebafd72d9cd575821d6fb31e57d Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:35:57 +0100 Subject: [PATCH 030/176] documentation added/updated --- man/check_formula_list.Rd | 22 ++++++++++++++++++++++ man/extract_lhs.Rd | 24 ++++++++++++++++++++++++ man/remove_lhs.Rd | 8 ++++++-- 3 files changed, 52 insertions(+), 2 deletions(-) create mode 100644 man/check_formula_list.Rd create mode 100644 man/extract_lhs.Rd diff --git a/man/check_formula_list.Rd b/man/check_formula_list.Rd new file mode 100644 index 00000000..fa519360 --- /dev/null +++ b/man/check_formula_list.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_formulas_general.R +\name{check_formula_list} +\alias{check_formula_list} +\title{Check/convert formula to list} +\usage{ +check_formula_list(formula, convert = TRUE) +} +\arguments{ +\item{formula}{any object} + +\item{convert}{logical; should the input be converted to a list?} +} +\description{ +Check if an object is a list of formulas and/or NULL elements and convert +it to a list if it is a formula object. +} +\details{ +Internal function; used in many help functions, get_refs, *_imp, predict +(2022-02-05) +} +\keyword{internal} diff --git a/man/extract_lhs.Rd b/man/extract_lhs.Rd new file mode 100644 index 00000000..437afd69 --- /dev/null +++ b/man/extract_lhs.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_formulas_general.R +\name{extract_lhs} +\alias{extract_lhs} +\title{Extract the left hand side of a formula} +\usage{ +extract_lhs(formula) +} +\arguments{ +\item{formula}{a \code{formula} object (NOT a \code{list} of formulas)} +} +\value{ +A character string. +} +\description{ +Extracts the left hand side from a \code{formula} object and returns it as +character string. +Relevant, for example, for survival formulas, where \code{Surv(...)} is a +\code{call}. +} +\details{ +Internal; used in various help functions (2022-02-05) +} +\keyword{internal} diff --git a/man/remove_lhs.Rd b/man/remove_lhs.Rd index e045f15e..6820cb11 100644 --- a/man/remove_lhs.Rd +++ b/man/remove_lhs.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/helpfunctions_formulas.R +% Please edit documentation in R/helpfunctions_formulas_general.R \name{remove_lhs} \alias{remove_lhs} \title{Remove the left hand side of a (list of) formula(s)} @@ -9,7 +9,11 @@ remove_lhs(formula) \arguments{ \item{formula}{a formula object or a list of formula objects} } +\value{ +A \code{formula} object or a \code{list} of \code{formula} objects. +} \description{ -Remove the left hand side of a (list of) formula(s) +Internal function; used in divide_matrices, get_models and help functions +(2022-02-05) } \keyword{internal} From 0d0bf444e3f7d4a7bf5a23d6fc45be0b412e726c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:38:40 +0100 Subject: [PATCH 031/176] moved functions remove_lhs(), extract_lhs() and check_formula_list() to file helpfunctions_formulas_general.R, added new functions combine_formula_lists and combine_formulas --- R/helpfunctions_formulas.R | 105 ---------------- R/helpfunctions_formulas_general.R | 196 +++++++++++++++++++++++++++++ man/combine_formula_lists.Rd | 25 ++++ man/combine_formulas.Rd | 20 +++ 4 files changed, 241 insertions(+), 105 deletions(-) create mode 100644 R/helpfunctions_formulas_general.R create mode 100644 man/combine_formula_lists.Rd create mode 100644 man/combine_formulas.Rd diff --git a/R/helpfunctions_formulas.R b/R/helpfunctions_formulas.R index 00724688..0c20370e 100644 --- a/R/helpfunctions_formulas.R +++ b/R/helpfunctions_formulas.R @@ -1,41 +1,3 @@ -#' Check/convert formula to list -#' -#' Check if an object is a list of formulas and/or NULL elements and convert -#' it to a list if it is a formula object. -#' -#' Internal function; used in many help functions, get_refs, *_imp, predict -#' (2022-02-05) -#' @param formula any object -#' @param convert logical; should the input be converted to a list? -#' @keywords internal -check_formula_list <- function(formula, convert = TRUE) { - - if (is.null(formula)) { - return(NULL) - } - - # if formula is a formula, turn it into a list - if (inherits(formula, "formula")) { - if (convert) { - formula <- list(formula) - } - } else if (inherits(formula, "list")) { - # check if all elements are either formulas or NULL - fmla_elmt <- lvapply(formula, inherits, "formula") - null_elmt <- lvapply(formula, is.null) - - if (!all(fmla_elmt | null_elmt)) { - errormsg("At least one element of the provided object is not of class - %s.", dQuote("formula")) - } - } else { - errormsg("The provided object is not of class %s nor is it a %s.", - dQuote("formula"), dQuote("list")) - } - - formula -} - # used in divide_matrices, get_models, various help functions, # predict (2020-06-09) @@ -123,74 +85,7 @@ extract_outcome <- function(fixed) { } -#' Extract the left hand side of a formula -#' -#' Extracts the left hand side from a `formula` object and returns it as -#' character string. -#' Relevant, for example, for survival formulas, where `Surv(...)` is a -#' `call`. -#' -#' Internal; used in various help functions (2022-02-05) -#' -#' @param formula a `formula` object (NOT a `list` of formulas) -#' -#' @returns A character string. -#' -#' @keywords internal -#' -extract_lhs <- function(formula) { - - if (is.null(formula)) { - return(NULL) - } - # check that formula is a formula object - if (!inherits(formula, "formula")) - errormsg("The provided formula is not a %s object.", dQuote("formula")) - - - # check that the formula has a LHS - if (attr(terms(formula), "response") != 1L) - errormsg("Unable to extract response from the formula.") - - - if (length(formula) == 3L) { - deparse(formula[[2L]], width.cutoff = 500L) - # } else if (length(formula) == 2L) { - # "" - } else { - # not sure this is ever needed... Can't come up with an example for a - # formula that has a response and length 2. - errormsg("Unable to extract a response from the formula. - Formula is not of length 3.") - } -} - - -#' Remove the left hand side of a (list of) formula(s) -#' -#' Internal function; used in divide_matrices, get_models and help functions -#' (2022-02-05) -#' -#' @param formula a formula object or a list of formula objects -#' -#' @returns A `formula` object or a `list` of `formula` objects. -#' -#' @keywords internal -#' -remove_lhs <- function(formula) { - - formula <- check_formula_list(formula, convert = FALSE) - - if (is.null(formula)) - return(NULL) - - if (inherits(formula, "list")) { - lapply(formula, remove_lhs) - } else { - formula(delete.response(terms(formula))) - } -} # used in divide_matrices, get_models, and help functions (20120-06-09) diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R new file mode 100644 index 00000000..24f5a1bd --- /dev/null +++ b/R/helpfunctions_formulas_general.R @@ -0,0 +1,196 @@ +#' Check/convert formula to list +#' +#' Check if an object is a list of formulas and/or NULL elements and convert +#' it to a list if it is a formula object. +#' +#' Internal function; used in many help functions, get_refs, *_imp, predict +#' (2022-02-05) +#' @param formula any object +#' @param convert logical; should the input be converted to a list? +#' @keywords internal +check_formula_list <- function(formula, convert = TRUE) { + + if (is.null(formula)) { + return(NULL) + } + + # if formula is a formula, turn it into a list + if (inherits(formula, "formula")) { + if (convert) { + formula <- list(formula) + } + } else if (inherits(formula, "list")) { + # check if all elements are either formulas or NULL + fmla_elmt <- lvapply(formula, inherits, "formula") + null_elmt <- lvapply(formula, is.null) + + if (!all(fmla_elmt | null_elmt)) { + errormsg("At least one element of the provided object is not of class + %s.", dQuote("formula")) + } + } else { + errormsg("The provided object is not of class %s nor is it a %s.", + dQuote("formula"), dQuote("list")) + } + + formula +} + + +#' Combine fixed and random effects formulas +#' +#' A function to combine nlme-style fixed and random effects formulas into +#' lme4 style formulas. +#' +#' Internal function. +#' Lists of formulas can be named or unnamed. +#' Uses `combine_formulas()`. +#' +#' @param fixed a fixed effects formula or list of such formulas +#' @param random a random effects formula (only RHS) or list of such formulas +#' @param warn logical; should the warning(s) be printed +#' +#' @keywords internal +#' + +combine_formula_lists <- function(fixed, random, warn = TRUE) { + + # check if the input objects are lists and convert them to lists otherwise + fixed <- check_formula_list(fixed) + random <- check_formula_list(random) + + # check if there are any random effects formulas with names that do not + # appear in the list of fixed effects formulas (if there any names) + if (any(!names(random) %in% names(fixed))) { + errormsg("There are random effects formulas for outcomes for which no fixed + effects formula is specified.") + } + + if (length(random) > length(fixed)) { + errormsg("There are more random effects formulas than fixed effects + formulas.") + } + + if ((!is.null(random) & is.null(names(random)) & length(fixed) > 1L) & warn) { + warnmsg("I assume that the order of the fixed and random effects formulas + matches each other.") + } + + # if fixed and random have names, make sure they are in the same order by + # sorting the elements of random + if (!is.null(names(fixed)) & !is.null(names(random))) { + random <- random[names(fixed)] + } + # if fixed is longer than random the previous step will have introduced NULL + # elements in random (with names ) so that they now have the same length + + # if fixed and random are not properly named and random is shorter than fixed, + # random has to be filled up with NULL elements + if (length(random) < length(fixed)) { + random <- c(random, replicate(length(fixed) - length(random), NULL)) + } + + # overwrite names of random with those of fixed + names(random) <- names(fixed) + + + Map(combine_formulas, fixed, random) +} + + + +#' Combine a fixed and random effects formula +#' +#' Combine a single fixed and random effects formula by pasting them together. +#' +#' Internal function, used in `combine_formula_lists()`. +#' +#' @param fixed fixed effects formula (two-sided `formula` object) +#' @param random random effects formula (one-sided `formula` object) +#' @keywords internal +#' +combine_formulas <- function(fixed, random) { + fmla <- paste( + c(deparse(fixed, width.cutoff = 500L), + if (!is.null(random)) { + paste0(gsub("~", "(", deparse(random, width.cutoff = 500L)), + ")") + }), collapse = " + ") + + as.formula(fmla) +} + + + + + +#' Remove the left hand side of a (list of) formula(s) +#' +#' Internal function; used in divide_matrices, get_models and help functions +#' (2022-02-05) +#' +#' @param formula a formula object or a list of formula objects +#' +#' @returns A `formula` object or a `list` of `formula` objects. +#' +#' @keywords internal +#' +remove_lhs <- function(formula) { + + formula <- check_formula_list(formula, convert = FALSE) + + if (is.null(formula)) + return(NULL) + + if (inherits(formula, "list")) { + lapply(formula, remove_lhs) + } else { + formula(delete.response(terms(formula))) + } +} + + + + +#' Extract the left hand side of a formula +#' +#' Extracts the left hand side from a `formula` object and returns it as +#' character string. +#' Relevant, for example, for survival formulas, where `Surv(...)` is a +#' `call`. +#' +#' Internal; used in various help functions (2022-02-05) +#' +#' @param formula a `formula` object (NOT a `list` of formulas) +#' +#' @returns A character string. +#' +#' @keywords internal +#' +extract_lhs <- function(formula) { + + if (is.null(formula)) { + return(NULL) + } + + # check that formula is a formula object + if (!inherits(formula, "formula")) + errormsg("The provided formula is not a %s object.", dQuote("formula")) + + + # check that the formula has a LHS + if (attr(terms(formula), "response") != 1L) + errormsg("Unable to extract response from the formula.") + + + if (length(formula) == 3L) { + deparse(formula[[2L]], width.cutoff = 500L) + # } else if (length(formula) == 2L) { + # "" + } else { + # not sure this is ever needed... Can't come up with an example for a + # formula that has a response and length 2. + errormsg("Unable to extract a response from the formula. + Formula is not of length 3.") + } +} diff --git a/man/combine_formula_lists.Rd b/man/combine_formula_lists.Rd new file mode 100644 index 00000000..7722cc8b --- /dev/null +++ b/man/combine_formula_lists.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_formulas_general.R +\name{combine_formula_lists} +\alias{combine_formula_lists} +\title{Combine fixed and random effects formulas} +\usage{ +combine_formula_lists(fixed, random, warn = TRUE) +} +\arguments{ +\item{fixed}{a fixed effects formula or list of such formulas} + +\item{random}{a random effects formula (only RHS) or list of such formulas} + +\item{warn}{logical; should the warning(s) be printed} +} +\description{ +A function to combine nlme-style fixed and random effects formulas into +lme4 style formulas. +} +\details{ +Internal function. +Lists of formulas can be named or unnamed. +Uses \code{combine_formulas()}. +} +\keyword{internal} diff --git a/man/combine_formulas.Rd b/man/combine_formulas.Rd new file mode 100644 index 00000000..3ad342dc --- /dev/null +++ b/man/combine_formulas.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_formulas_general.R +\name{combine_formulas} +\alias{combine_formulas} +\title{Combine a fixed and random effects formula} +\usage{ +combine_formulas(fixed, random) +} +\arguments{ +\item{fixed}{fixed effects formula (two-sided \code{formula} object)} + +\item{random}{random effects formula (one-sided \code{formula} object)} +} +\description{ +Combine a single fixed and random effects formula by pasting them together. +} +\details{ +Internal function, used in \code{combine_formula_lists()}. +} +\keyword{internal} From c8b5418e2c92120b57854520592a385d478268e0 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:39:26 +0100 Subject: [PATCH 032/176] moved/updated tests for helpfunctions-formulas_general.R --- tests/testthat/test-helpfunctions_formulas.R | 41 +--- .../test-helpfunctions_formulas_general.R | 221 ++++++++++++++++++ 2 files changed, 223 insertions(+), 39 deletions(-) create mode 100644 tests/testthat/test-helpfunctions_formulas_general.R diff --git a/tests/testthat/test-helpfunctions_formulas.R b/tests/testthat/test-helpfunctions_formulas.R index eb322f35..030bf2dd 100644 --- a/tests/testthat/test-helpfunctions_formulas.R +++ b/tests/testthat/test-helpfunctions_formulas.R @@ -3,28 +3,6 @@ library("JointAI") library("survival") -# check_formula_list ----------------------------------------------------------- -test_that('check_formula_list works', { - expect_equal(check_formula_list(y ~ x + z), - list(y ~ x + z)) - expect_equal(check_formula_list(y ~ x + z | id), - list(y ~ x + z | id)) - expect_equal(check_formula_list(NULL), - NULL) - expect_equal(check_formula_list(list(y ~ x + z, NULL)), - list(y ~ x + z, NULL)) -}) - - -test_that('check_formula_list gives error', { - expect_error(check_formula_list("y ~ x + z")) - expect_error(check_formula_list(33)) - expect_error(check_formula_list(log(y))) - expect_error(check_formula_list(list(y ~ x + z, NULL, 33))) - expect_error(check_formula_list(list(y ~ x + z, NULL, "abc"))) - expect_error(check_formula_list(list(y ~ x + z, NULL, "y ~ abc"))) -}) - # extract_id-------------------------------------------------------------- @@ -125,24 +103,9 @@ test_that('extract_outcome works', { }) -# extract_lhs ------------------------------------------------------------------ -test_that('extract_lhs works', { - for (i in seq_along(ys)) { - expect_equal( extract_lhs(ys[[i]]$fixed), ys[[i]]$LHS) - } -}) -# remove_lhs ------------------------------------------------------------------- -test_that('remove_lhs works', { - for (i in seq_along(ys)) { - expect_equal(remove_lhs(ys[[i]]$fixed), ys[[i]]$RHS, - ignore_formula_env = TRUE) - } - for (i in seq_along(runs)) { - expect_equal(remove_lhs(runs[[i]]$random), runs[[i]]$RHS, - ignore_formula_env = TRUE) - } -}) + + # remove grouping -------------------------------------------------------------- diff --git a/tests/testthat/test-helpfunctions_formulas_general.R b/tests/testthat/test-helpfunctions_formulas_general.R new file mode 100644 index 00000000..6bab363d --- /dev/null +++ b/tests/testthat/test-helpfunctions_formulas_general.R @@ -0,0 +1,221 @@ +# check_formula_list ----------------------------------------------------------- + +test_that('check_formula_list works', { + # formula, default: convert to list + expect_equal(check_formula_list(y ~ x + z), list(y ~ x + z)) + expect_equal(check_formula_list(y ~ x + z | id), list(y ~ x + z | id)) + + # formula, do not convert to list + expect_equal(check_formula_list(y ~ x + z, convert = FALSE), y ~ x + z) + expect_equal(check_formula_list(y ~ x + z | id, convert = FALSE), + y ~ x + z | id) + + # NULL + expect_null(check_formula_list(NULL)) + + # list of formulas + expect_equal(check_formula_list(list(y = y ~ x + z, a = a ~ b + c)), + list(y = y ~ x + z, a = a ~ b + c)) + + # list of formulas and NULL elements + expect_equal(check_formula_list(list(y ~ x + z, NULL)), + list(y ~ x + z, NULL)) + + # list of only NULL elements + expect_equal(check_formula_list(list(NULL, NULL, NULL)), + list(NULL, NULL, NULL)) +}) + + +test_that('check_formula_list gives error', { + # other types of objects + expect_error(check_formula_list("y ~ x + z")) + expect_error(check_formula_list(33)) + expect_error(check_formula_list(TRUE)) + expect_error(check_formula_list(NA)) + expect_error(check_formula_list(expression(y ~ x + y))) + + # lists with non-formula, non-null elements + expect_error(check_formula_list(list(y ~ x + z, NULL, 33))) + expect_error(check_formula_list(list(y ~ x + z, NULL, "abc"))) + expect_error(check_formula_list(list(y ~ x + z, NULL, "y ~ abc"))) + expect_error(check_formula_list(list(y ~ x + z, NULL, NA))) + + # list of list of formulas + expect_error(check_formula_list(list(y ~ x + z, list(a ~ b + c, d ~ e + f)))) +}) + + + + +# combine formula lists -------------------------------------------------------- + +test_that("joining fixed and random effects formulas works", { + # no random effects + expect_equal(combine_formula_lists(y ~ a + b, NULL), + list(y ~ a + b), + ignore_attr = TRUE) + expect_equal(combine_formula_lists(list(y ~ a + b), NULL), + list(y ~ a + b), + ignore_attr = TRUE) + expect_equal(combine_formula_lists(list(y = y ~ a + b), NULL), + list(y ~ a + b), + ignore_attr = TRUE) + expect_equal( + combine_formula_lists(list(y ~ a + b, x ~ b + c), NULL), + list(y ~ a + b, x ~ b + c), + ignore_attr = TRUE) + + # single fixed and single random effects formula + expect_equal(combine_formula_lists(y ~ a + b, ~ 1 | id), + list(y ~ a + b + (1 | id)), + ignore_attr = TRUE) + + # more fixed effects than random effects (named lists) + expect_equal( + combine_formula_lists(list(y = y ~ a + b, x = x ~ b + c), + list(x = ~ 1 | id)), + list(y ~ a + b, x ~ b + c + (1 | id)), + ignore_attr = TRUE) + + + # more fixed effects than random effects (random unnamed) + expect_equal( + combine_formula_lists(list(y = y ~ a + b, x = x ~ b + c), + ~ 1 | id, warn = FALSE), + list(y ~ a + b + (1 | id), x ~ b + c), + ignore_attr = TRUE) + + expect_equal( + combine_formula_lists(list(y = y ~ a + b, x = x ~ b + c), + list(~ 1 | id), warn = FALSE), + list(y ~ a + b + (1 | id), x ~ b + c), + ignore_attr = TRUE) + + # equal length fixed and random (both unnamed) + expect_equal( + combine_formula_lists(list(y ~ a + b, x ~ b + c), + list(~1 | id, ~ 1 | id), warn = FALSE), + list(y ~ a + b + (1 | id), x ~ b + c + (1 | id)), + ignore_attr = TRUE) + + # equal length fixed and random (random unnamed) + expect_equal( + combine_formula_lists(list(y = y ~ a + b, x = x ~ b + c), + list(~1 | id, ~ 1 | id), warn = FALSE), + list(y = y ~ a + b + (1 | id), x = x ~ b + c + (1 | id)), + ignore_attr = TRUE) +}) + + +test_that("joining fixed and random effects gives warning", { + # more fixed effects than random effects (random unnamed) + expect_warning( + combine_formula_lists(list(y = y ~ a + b, x = x ~ b + c), + ~ 1 | id)) + expect_warning( + combine_formula_lists(list(y = y ~ a + b, x = x ~ b + c), + list(~ 1 | id))) + + # equal length fixed and random (both unnamed) + expect_warning( + combine_formula_lists(list(y ~ a + b, x ~ b + c), + list(~1 | id, ~ 1 | id))) + # equal length fixed and random (random unnamed) + expect_warning( + combine_formula_lists(list(y = y ~ a + b, x = x ~ b + c), + list(~1 | id, ~ 1 | id))) +}) + + +test_that("joining fixed and random effects formulas returns error", { + # random has names not present in fixed + expect_error( + combine_formula_lists(list(y ~ a + b, x ~ b + c), + list(y = ~ 1 | id))) + expect_error( + combine_formula_lists(list(y = y ~ a + b, x ~ b + c), + list(z = ~ 1 | id))) + expect_error( + combine_formula_lists(list(x ~ b + c), + list(x = ~1 | id, z = ~ 1 | id))) + + # random is longer than fixed + expect_error( + combine_formula_lists(list(x ~ b + c), + list(~1 | id, ~ 1 | id))) +}) + + +# remove_lhs() ---------------------------------------------------------------- + +test_that("remove_lhs() works", { + # single formula + expect_equal(remove_lhs(y ~ a + b), ~ a + b) + expect_equal(remove_lhs(y ~ a + b + (time | id)), ~ a + b + (time | id)) + expect_equal(remove_lhs(y ~ a + I(b^2/d)), ~ a + I(b^2/d)) + + # no response + expect_equal(remove_lhs(~ a + b), ~ a + b) + + # null + expect_null(remove_lhs(NULL)) + + # other type of object: covered by the tests for check_formula_list() + + # complex outcomes + expect_equal(remove_lhs(Surv(time, status) ~ x + y), ~ x + y) + expect_equal(remove_lhs(cbind(time, status) ~ x + y), ~ x + y) + expect_equal(remove_lhs(Surv(time, status == 3) ~ x + y), ~ x + y) + expect_equal(remove_lhs(log(a) ~ x + y), ~ x + y) + expect_equal(remove_lhs(I(max(a, b, na.rm = TRUE)) ~ x + y), ~ x + y) + + # formula list + expect_equal(remove_lhs(list(y ~ a + b, z ~ d + e)), + list(~ a + b, ~ d + e)) +}) + + + +# extract_lhs ------------------------------------------------------------------ +test_that('extract_lhs works', { + # simple response + expect_equal(extract_lhs(y ~ a + b), "y") + + # survival object + expect_equal(extract_lhs(Surv(time, status) ~ a + b), "Surv(time, status)") + expect_equal(extract_lhs(Surv(time, status == 3) ~ a + b), + "Surv(time, status == 3)") + + # cbind response + expect_equal(extract_lhs(cbind(a, b, c) ~ x), "cbind(a, b, c)") + + # function/trafo response + expect_equal(extract_lhs(I(x^2) ~ y), "I(x^2)") + expect_equal(extract_lhs(log(x^2) ~ y), "log(x^2)") + expect_equal(extract_lhs(a + b ~ y + z), "a + b") + + # null + expect_null(extract_lhs(NULL)) + +}) + + +test_that('extract_lhs returns error', { + # no response + expect_error(extract_lhs(~ y + z)) + + # not a formula + expect_error(extract_lhs("a ~ y + z")) + expect_error(extract_lhs(NA)) + + # a list of formulas + expect_error(extract_lhs(list(a ~ b + c, x ~ y + z))) +}) + +# y ~ a + b +# y ~ 1 +# Surv(a, b) ~ 1 +# Surv(a, b, d) ~ x + z +# cbind(a, b, d) ~ x + z +# y ~ C2 + ns(C1, df = 3, Boundary.knots = quantile(C1, c(0.025, 0.975))) From fb280b72584eb3931345db16eea278f9d5373c9d Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:39:55 +0100 Subject: [PATCH 033/176] the change due to the new version of remove_lhs() returning a formula instead of a list when the input is a formula object --- R/helpfunctions_divide_matrices.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/helpfunctions_divide_matrices.R b/R/helpfunctions_divide_matrices.R index 1d6d09d9..33bad0dc 100644 --- a/R/helpfunctions_divide_matrices.R +++ b/R/helpfunctions_divide_matrices.R @@ -403,7 +403,7 @@ model_matrix_combi <- function(fmla, data, terms_list, refs) { mats <- mapply(function(object, data, contr) { # get the subset of contrast matrices corresponding to the current formula # to avoid warning messages - covars <- cvapply(attr(terms(remove_lhs(object)[[1L]]), + covars <- cvapply(attr(terms(remove_lhs(object)), "variables")[-1L], deparse, width.cutoff = 500L) contr_list <- contr[intersect(covars, names(contr))] @@ -585,7 +585,7 @@ get_linpreds <- function(fixed, random, data, models, auxvars = NULL, # for each fixed effects (main model) formula, get the column names of the # design matrix of the fixed effects lp <- nlapply(fixed, function(fmla) { - covars <- cvapply(attr(terms(as.formula(remove_lhs(fmla)[[1L]])), + covars <- cvapply(attr(terms(remove_lhs(fmla)), "variables")[-1L], deparse, width.cutoff = 500L) From 0f6020f045e8a81a1174edcfb59a11ab31333902 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:40:37 +0100 Subject: [PATCH 034/176] bugfix to prevent the warning message that just the first element is used when adding samples a second time (because thin is then a vector) --- R/add_samples.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/add_samples.R b/R/add_samples.R index e5b1d5ad..283a2d69 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -58,7 +58,8 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, if (is.null(thin)) { thin <- object$mcmc_settings$thin[length(object$mcmc_settings$thin)] } else { - if (add & thin != object$mcmc_settings$thin) { + if (add & + thin != object$mcmc_settings$thin[length(object$mcmc_settings$thin)]) { thin <- object$mcmc_settings$thin[length(object$mcmc_settings$thin)] if (mess) From 4431351dc114cf035049a7b92b27ee7864612d8d Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:43:08 +0100 Subject: [PATCH 035/176] improvement of formula method. JointAI object now has the formula as an element. This is also automatically copied when using add_samples(). --- R/JointAIObject.R | 1 + R/model_imp.R | 9 ++++++++ R/summary.JointAI.R | 54 +++++++++++++++++++++++--------------------- man/JointAIObject.Rd | 1 + 4 files changed, 39 insertions(+), 26 deletions(-) diff --git a/R/JointAIObject.R b/R/JointAIObject.R index 6f57a48f..ae76b38e 100644 --- a/R/JointAIObject.R +++ b/R/JointAIObject.R @@ -11,6 +11,7 @@ #' \code{coxph} (with attributes #' \code{family} and \code{link} for GLM-type #' models} +#' \item{\code{formula}}{The formula used in the (analysis) model.} #' \item{\code{data}}{original (incomplete, but pre-processed) data} #' \item{\code{models}}{named vector specifying the the types of all sub-models} #' \item{\code{fixed}}{a list of the fixed effects formulas of the sub-model(s) diff --git a/R/model_imp.R b/R/model_imp.R index 349d7351..e7fed3a4 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -869,9 +869,18 @@ model_imp <- function(formula = NULL, fixed = NULL, data, random = NULL, inits = inits, seed = seed) + fmla <- if (is.null(formula) & !is.null(fixed)) { + combine_formula_lists(fixed, random, warn = warn) + } else { + formula + } + if (length(fmla) == 1L) { + fmla <- fmla[[1]] + } object <- structure( list(analysis_type = analysis_type, + formula = fmla, data = Mlist$data, models = Mlist$models, fixed = Mlist$fixed, diff --git a/R/summary.JointAI.R b/R/summary.JointAI.R index 7983d3df..7e5192ff 100644 --- a/R/summary.JointAI.R +++ b/R/summary.JointAI.R @@ -426,32 +426,34 @@ formula.JointAI <- function(x, ...) { if (!(inherits(x, "JointAI") | inherits(x, "JointAI_errored"))) errormsg("Use only %s with objects.", sQuote("JointAI")) - if (inherits(x$call, "call")) { - if (is.null(x$call$formula)) { - as.formula(x$call$fixed) - } else { - fmla <- eval(x$call$formula) - if (inherits(eval(x$call$formula), "list")) { - x$call$formula - } else { - as.formula(x$call$formula) - } - } - } else if (inherits(x$call, "list")) { - fmla_list <- lapply(x$call, function(k) { - if (!is.null(k$formula)) { - k$formula - } else { - k$fixed - } - }) - fmla_list <- lapply(fmla_list[lvapply(fmla_list, inherits, "call")], as.formula) - if (length(fmla_list) == 1L) { - fmla_list[[1]] - } else { - fmla_list - } - } + x$formula + + # if (inherits(x$call, "call")) { + # if (is.null(x$call$formula)) { + # as.formula(x$call$fixed) + # } else { + # fmla <- eval(x$call$formula) + # if (inherits(eval(x$call$formula), "list")) { + # x$call$formula + # } else { + # as.formula(x$call$formula) + # } + # } + # } else if (inherits(x$call, "list")) { + # fmla_list <- lapply(x$call, function(k) { + # if (!is.null(k$formula)) { + # k$formula + # } else { + # k$fixed + # } + # }) + # fmla_list <- lapply(fmla_list[lvapply(fmla_list, inherits, "call")], as.formula) + # if (length(fmla_list) == 1L) { + # fmla_list[[1]] + # } else { + # fmla_list + # } + # } } diff --git a/man/JointAIObject.Rd b/man/JointAIObject.Rd index 06341e60..252ffb2b 100644 --- a/man/JointAIObject.Rd +++ b/man/JointAIObject.Rd @@ -9,6 +9,7 @@ \code{coxph} (with attributes \code{family} and \code{link} for GLM-type models} +\item{\code{formula}}{The formula used in the (analysis) model.} \item{\code{data}}{original (incomplete, but pre-processed) data} \item{\code{models}}{named vector specifying the the types of all sub-models} \item{\code{fixed}}{a list of the fixed effects formulas of the sub-model(s) From 50dd5449b6c84610b404d656d1517f647cd7c619 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 5 Feb 2022 12:43:25 +0100 Subject: [PATCH 036/176] no longer exporting remove_lhs() --- NAMESPACE | 1 - 1 file changed, 1 deletion(-) diff --git a/NAMESPACE b/NAMESPACE index 95bd99ac..82349dd1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -60,7 +60,6 @@ export(plot_all) export(plot_imp_distr) export(predDF) export(rd_vcov) -export(remove_lhs) export(set_refcat) export(survreg_imp) export(traceplot) From e284261ce2075848b8de678fc07b21f87d866319 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 09:57:53 +0100 Subject: [PATCH 037/176] bugfix: call element of JointAI object was a nested list when using add_samples, now it is a list --- NEWS.md | 2 ++ R/add_samples.R | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index 28c86cc1..0a6ac590 100644 --- a/NEWS.md +++ b/NEWS.md @@ -7,6 +7,8 @@ ## Bug fixes * `formula()` did not return a formula when `add_samples()` was used. +* Use of `add_samples()` will now result in the `call` element of a `JointAI` + object being a `list` and no longer a nested list. ----------------------------------------------------------------------------- diff --git a/R/add_samples.R b/R/add_samples.R index 283a2d69..1810cd84 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -151,7 +151,7 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, newobject <- object newobject$sample <- newmcmc newobject$MCMC <- newMCMC - newobject$call <- list(object$call, match.call()) + newobject$call <- c(object$call, match.call()) newobject$mcmc_settings$variable.names <- var_names newobject$comp_info$future <- c(object$comp_info$future, future_info$call) From 5365cd0aa8ce089933a20d9fa4765eece10b4ce1 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 10:04:23 +0100 Subject: [PATCH 038/176] moved functions split_formula(), split_formula_list() and all_vars() to file helpfunctions_formulas_general.R --- R/helpfunctions_formulas.R | 64 ------------------------------ R/helpfunctions_formulas_general.R | 63 +++++++++++++++++++++++++++++ 2 files changed, 63 insertions(+), 64 deletions(-) diff --git a/R/helpfunctions_formulas.R b/R/helpfunctions_formulas.R index 0c20370e..10349687 100644 --- a/R/helpfunctions_formulas.R +++ b/R/helpfunctions_formulas.R @@ -161,70 +161,6 @@ remove_grouping <- function(fmla) { -# used in *_imp and help functions (2020-06-09) -split_formula <- function(formula) { - # split a lme4 type formula into fixed and random part - # - formula: formula of the form outcome ~ covars + (x | group) + (x | group2) - - # get all terms from the formula and identify which contain the vertical bar - # (= random effects) - term_labels <- attr(terms(formula), "term.labels") - which_ranef <- grepl("|", term_labels, fixed = TRUE) - - # build fixed effects formula by combining all non-random effects terms with - # a "+", and combine with the LHS - rhs <- paste(c(term_labels[!which_ranef], - if (attr(terms(formula), "intercept") == 0L) "0"), - collapse = " + ") - - fixed <- paste0(as.character(formula)[2L], " ~ ", - if (rhs == "") {1L} else {rhs} - ) - - # build random effects formula by pasting all random effects terms in brackets - # (to separate different random effects terms from each other), and combine - # them with "+" - rhs2 <- paste0("(", term_labels[which_ranef], ")", collapse = " + ") - # if there are random effects terms at all, combine with "~" and convert to - # formula object - random <- if (rhs2 != "()") as.formula(paste0(" ~ ", rhs2)) - - list(fixed = as.formula(fixed), - random = random) -} - - -# used in *_imp() (2020-06-09) -split_formula_list <- function(formulas) { - # split a list of formulas into a list with fixed effects formulas and a list - # with random effects formulas - - l <- lapply(formulas, split_formula) - names(l) <- cvapply(formulas, function(x) as.character(x)[2L]) - - list(fixed = lapply(l, "[[", "fixed"), - random = lapply(l, "[[", "random")) -} - - - -# used in various functions (2020-06-09) -#' Version of `all.vars()` that can handle lists of formulas -#' @param a formula or list of formulas -#' @export -#' @keywords internal -all_vars <- function(fmla) { - # extract all variables involved in a formula or list of formulas - # - fmla: formula or list of formulas - - if (is.list(fmla)) { - unique(unlist(lapply(fmla, all.vars))) - } else { - all.vars(fmla) - } -} - - # functions in formulas -------------------------------------------------------- diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R index 24f5a1bd..ceecbd1b 100644 --- a/R/helpfunctions_formulas_general.R +++ b/R/helpfunctions_formulas_general.R @@ -194,3 +194,66 @@ extract_lhs <- function(formula) { Formula is not of length 3.") } } + + +# used in various functions (2020-06-09) +#' Version of `all.vars()` that can handle lists of formulas +#' @param a formula or list of formulas +#' @export +#' @keywords internal +all_vars <- function(fmla) { + # extract all variables involved in a formula or list of formulas + # - fmla: formula or list of formulas + + if (is.list(fmla)) { + unique(unlist(lapply(fmla, all.vars))) + } else { + all.vars(fmla) + } +} + + +# used in *_imp and help functions (2020-06-09) +split_formula <- function(formula) { + # split a lme4 type formula into fixed and random part + # - formula: formula of the form outcome ~ covars + (x | group) + (x | group2) + + # get all terms from the formula and identify which contain the vertical bar + # (= random effects) + term_labels <- attr(terms(formula), "term.labels") + which_ranef <- grepl("|", term_labels, fixed = TRUE) + + # build fixed effects formula by combining all non-random effects terms with + # a "+", and combine with the LHS + rhs <- paste(c(term_labels[!which_ranef], + if (attr(terms(formula), "intercept") == 0L) "0"), + collapse = " + ") + + fixed <- paste0(as.character(formula)[2L], " ~ ", + if (rhs == "") {1L} else {rhs} + ) + + # build random effects formula by pasting all random effects terms in brackets + # (to separate different random effects terms from each other), and combine + # them with "+" + rhs2 <- paste0("(", term_labels[which_ranef], ")", collapse = " + ") + # if there are random effects terms at all, combine with "~" and convert to + # formula object + random <- if (rhs2 != "()") as.formula(paste0(" ~ ", rhs2)) + + list(fixed = as.formula(fixed), + random = random) +} + + +# used in *_imp() (2020-06-09) +split_formula_list <- function(formulas) { + # split a list of formulas into a list with fixed effects formulas and a list + # with random effects formulas + + l <- lapply(formulas, split_formula) + names(l) <- cvapply(formulas, function(x) as.character(x)[2L]) + + list(fixed = lapply(l, "[[", "fixed"), + random = lapply(l, "[[", "random")) +} From 1abb39667e5b54b6f23ed6a424fbd4e12e54dc73 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 10:46:49 +0100 Subject: [PATCH 039/176] add check_formula_list() to split_formula-list() --- R/helpfunctions_checks.R | 4 ++-- R/helpfunctions_formulas_general.R | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/R/helpfunctions_checks.R b/R/helpfunctions_checks.R index fa691d22..01659353 100644 --- a/R/helpfunctions_checks.R +++ b/R/helpfunctions_checks.R @@ -76,7 +76,7 @@ check_fixed_random <- function(arglist) { # if there is a "fixed" effects formula, but no "random" , check if "fixed" # contains the fixed and random effects if (!is.null(arglist$fixed) & is.null(arglist$random)) { - can_split <- try(split_formula_list(check_formula_list(arglist$fixed))) + can_split <- try(split_formula_list(arglist$fixed)) if (!inherits(can_split, 'try-error') & !is.null(can_split$random[[1]])) { arglist$formula <- arglist$fixed @@ -84,7 +84,7 @@ check_fixed_random <- function(arglist) { arglist$random <- NULL } } else if (!is.null(arglist$formula) & is.null(arglist$random)) { - can_split <- try(split_formula_list(check_formula_list(arglist$formula))) + can_split <- try(split_formula_list(arglist$formula)) if (inherits(can_split, 'try-error')) { errormsg("I cannot split the %s into a fixed and random effects part.", diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R index ceecbd1b..1d91d2b7 100644 --- a/R/helpfunctions_formulas_general.R +++ b/R/helpfunctions_formulas_general.R @@ -250,6 +250,7 @@ split_formula <- function(formula) { split_formula_list <- function(formulas) { # split a list of formulas into a list with fixed effects formulas and a list # with random effects formulas + formulas <- check_formula_list(formulas) l <- lapply(formulas, split_formula) names(l) <- cvapply(formulas, function(x) as.character(x)[2L]) From d47396ffe6c597b797d51f32989c0a97fc226e13 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 10:48:06 +0100 Subject: [PATCH 040/176] Add internal documentation for split_formula() and split_formula_list() --- R/helpfunctions_formulas_general.R | 37 ++++++++++++++++++++++-------- man/split_formula.Rd | 16 +++++++++++++ man/split_formula_list.Rd | 17 ++++++++++++++ 3 files changed, 61 insertions(+), 9 deletions(-) create mode 100644 man/split_formula.Rd create mode 100644 man/split_formula_list.Rd diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R index 1d91d2b7..ed3ffbfa 100644 --- a/R/helpfunctions_formulas_general.R +++ b/R/helpfunctions_formulas_general.R @@ -213,10 +213,17 @@ all_vars <- function(fmla) { } -# used in *_imp and help functions (2020-06-09) + +#' Split a formula into fixed and random effects parts +#' Split a lme4 style formula into nlme style formulas. +#' +#' Internal function, used in *_imp and help functions (2022-02-06) +#' +#' @param formula a `formula` object +#' @keywords internal +#' + split_formula <- function(formula) { - # split a lme4 type formula into fixed and random part - # - formula: formula of the form outcome ~ covars + (x | group) + (x | group2) # get all terms from the formula and identify which contain the vertical bar # (= random effects) @@ -230,14 +237,18 @@ split_formula <- function(formula) { collapse = " + ") fixed <- paste0(as.character(formula)[2L], " ~ ", - if (rhs == "") {1L} else {rhs} - ) + if (rhs == "") { + 1L + } else { + rhs + }) # build random effects formula by pasting all random effects terms in brackets # (to separate different random effects terms from each other), and combine # them with "+" rhs2 <- paste0("(", term_labels[which_ranef], ")", collapse = " + ") - # if there are random effects terms at all, combine with "~" and convert to + + # if there are random effect terms at all, combine with "~" and convert to a # formula object random <- if (rhs2 != "()") as.formula(paste0(" ~ ", rhs2)) @@ -246,10 +257,18 @@ split_formula <- function(formula) { } -# used in *_imp() (2020-06-09) +#' Split a list of formulas into fixed and random effects parts. +#' Calls `split_formula()` on each formula in a list to create one list of the +#' fixed effects formulas and one list containing the random effects formulas. +#' +#' Internal function, used in *_imp() (2022-02-06) +#' +#' @param formulas a `list` of `formula` objects +#' @keywords internal +#' + split_formula_list <- function(formulas) { - # split a list of formulas into a list with fixed effects formulas and a list - # with random effects formulas + formulas <- check_formula_list(formulas) l <- lapply(formulas, split_formula) diff --git a/man/split_formula.Rd b/man/split_formula.Rd new file mode 100644 index 00000000..4b1b6d0d --- /dev/null +++ b/man/split_formula.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_formulas_general.R +\name{split_formula} +\alias{split_formula} +\title{Split a formula into fixed and random effects parts +Split a lme4 style formula into nlme style formulas.} +\usage{ +split_formula(formula) +} +\arguments{ +\item{formula}{a \code{formula} object} +} +\description{ +Internal function, used in *_imp and help functions (2022-02-06) +} +\keyword{internal} diff --git a/man/split_formula_list.Rd b/man/split_formula_list.Rd new file mode 100644 index 00000000..f3303f89 --- /dev/null +++ b/man/split_formula_list.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_formulas_general.R +\name{split_formula_list} +\alias{split_formula_list} +\title{Split a list of formulas into fixed and random effects parts. +Calls \code{split_formula()} on each formula in a list to create one list of the +fixed effects formulas and one list containing the random effects formulas.} +\usage{ +split_formula_list(formulas) +} +\arguments{ +\item{formulas}{a \code{list} of \code{formula} objects} +} +\description{ +Internal function, used in *_imp() (2022-02-06) +} +\keyword{internal} From 33994da93b72870308081fd58cd683e309c65102 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 10:49:32 +0100 Subject: [PATCH 041/176] Added internal documentation for all_vars() and added error in case wrong object type. --- R/helpfunctions_formulas_general.R | 20 +++++++++++++------- man/all_vars.Rd | 8 +++++--- 2 files changed, 18 insertions(+), 10 deletions(-) diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R index ed3ffbfa..447c120f 100644 --- a/R/helpfunctions_formulas_general.R +++ b/R/helpfunctions_formulas_general.R @@ -185,7 +185,7 @@ extract_lhs <- function(formula) { if (length(formula) == 3L) { deparse(formula[[2L]], width.cutoff = 500L) - # } else if (length(formula) == 2L) { + # } else if (length(formula) == 2L) { # "" } else { # not sure this is ever needed... Can't come up with an example for a @@ -196,19 +196,25 @@ extract_lhs <- function(formula) { } -# used in various functions (2020-06-09) + +#' Extract names of variables from a (list of) formula(s) #' Version of `all.vars()` that can handle lists of formulas -#' @param a formula or list of formulas +#' +#' +#' @param fmla a formula or list of formulas #' @export +#' #' @keywords internal + all_vars <- function(fmla) { - # extract all variables involved in a formula or list of formulas - # - fmla: formula or list of formulas - if (is.list(fmla)) { + if (inherits(fmla, "list")) { unique(unlist(lapply(fmla, all.vars))) - } else { + } else if (inherits(fmla, "formula")) { all.vars(fmla) + } else { + errormsg("The provided object is not a %s nor a list of %s objects.", + dQuote("formula"), dQuote("formula")) } } diff --git a/man/all_vars.Rd b/man/all_vars.Rd index af8f27ed..64328491 100644 --- a/man/all_vars.Rd +++ b/man/all_vars.Rd @@ -1,15 +1,17 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/helpfunctions_formulas.R +% Please edit documentation in R/helpfunctions_formulas_general.R \name{all_vars} \alias{all_vars} -\title{Version of \code{all.vars()} that can handle lists of formulas} +\title{Extract names of variables from a (list of) formula(s) +Version of \code{all.vars()} that can handle lists of formulas} \usage{ all_vars(fmla) } \arguments{ -\item{a}{formula or list of formulas} +\item{fmla}{a formula or list of formulas} } \description{ +Extract names of variables from a (list of) formula(s) Version of \code{all.vars()} that can handle lists of formulas } \keyword{internal} From a404e7c8fe6acae8d31f713457f46ef56714f0f6 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 10:49:57 +0100 Subject: [PATCH 042/176] moved/improved tests for split_formula() and split_formula_list() --- tests/testthat/test-helpfunctions_formulas.R | 38 ------------- .../test-helpfunctions_formulas_general.R | 56 +++++++++++++++++-- 2 files changed, 50 insertions(+), 44 deletions(-) diff --git a/tests/testthat/test-helpfunctions_formulas.R b/tests/testthat/test-helpfunctions_formulas.R index 030bf2dd..27f44f77 100644 --- a/tests/testthat/test-helpfunctions_formulas.R +++ b/tests/testthat/test-helpfunctions_formulas.R @@ -122,44 +122,6 @@ test_that('remove_grouping works', { }) -# split_formula----------------------------------------------- -fmls <- list( - list(fmla = y ~ a + b + (b | id), - fixed = y ~ a + b, - random = ~ (b | id)), - list(fmla = y ~ (1|id), - fixed = y ~ 1, - random = ~ (1 | id)), - list(fmla = y ~ a + (a + b|id), - fixed = y ~ a, - random = ~ (a + b |id)), - list(fmla = y ~ a + I(a^2) + (a + I(a^2) | id), - fixed = y ~ a + I(a^2), - random = ~ (a + I(a^2) | id)), - list(fmla = y ~ x + (1| id/class), - fixed = y ~ x, - random = ~ (1 | id/class)), - list(fmla = y ~ x + (1|id) + (1|class), - fixed = y ~ x, - random = ~ (1|id) + (1|class))) - -test_that('split_formula works', { - for (i in seq_along(fmls)) { - expect_equal(split_formula(fmls[[i]]$fmla), - list(fixed = fmls[[i]]$fixed, random = fmls[[i]]$random), - ignore_formula_env = TRUE) - } -}) - -# split_formula_list -------------------------------------------------- -test_that('split_formula_list works', { - expect_equal( - unname(lapply(split_formula_list(lapply(fmls, "[[", "fmla")), unname)), - list(lapply(fmls, "[[", 'fixed'), - lapply(fmls, "[[", 'random')), - ignore_formula_env = TRUE) - -}) # identify_functions ----------------------------------------------------------- diff --git a/tests/testthat/test-helpfunctions_formulas_general.R b/tests/testthat/test-helpfunctions_formulas_general.R index 6bab363d..c5671c65 100644 --- a/tests/testthat/test-helpfunctions_formulas_general.R +++ b/tests/testthat/test-helpfunctions_formulas_general.R @@ -213,9 +213,53 @@ test_that('extract_lhs returns error', { expect_error(extract_lhs(list(a ~ b + c, x ~ y + z))) }) -# y ~ a + b -# y ~ 1 -# Surv(a, b) ~ 1 -# Surv(a, b, d) ~ x + z -# cbind(a, b, d) ~ x + z -# y ~ C2 + ns(C1, df = 3, Boundary.knots = quantile(C1, c(0.025, 0.975))) + + +# split_formula----------------------------------------------- +fmls <- list( + list(fmla = y ~ a + b + (b | id), + fixed = list(y = y ~ a + b), + random = list(y = ~ (b | id))), + list(fmla = y ~ (1|id), + fixed = list(y = y ~ 1), + random = list(y = ~ (1 | id))), + list(fmla = y ~ a + (a + b|id), + fixed = list(y = y ~ a), + random = list(y = ~ (a + b |id))), + list(fmla = y ~ a + I(a^2) + (a + I(a^2) | id), + fixed = list(y = y ~ a + I(a^2)), + random = list(y = ~ (a + I(a^2) | id))), + list(fmla = y ~ x + (1| id/class), + fixed = list(y = y ~ x), + random = list(y = ~ (1 | id/class))), + list(fmla = y ~ x + (1|id) + (1|class), + fixed = list(y = y ~ x), + random = list(y = ~ (1|id) + (1|class))), + list(fmla = y ~ a + b + (id | group1 + group2), + fixed = list(y = y ~ a + b), + random = list(y = ~ (id | group1 + group2))) +) + +test_that('split_formula works', { + for (i in seq_along(fmls)) { + expect_equal(split_formula(fmls[[i]]$fmla), + list(fixed = fmls[[i]]$fixed[[1]], + random = fmls[[i]]$random[[1]]), + ignore_formula_env = TRUE) + } + + expect_equal(split_formula(y ~ a + b), + list(fixed = y ~ a + b, + random = NULL), ignore_formula_env = TRUE) +}) + + + +# split_formula_list -------------------------------------------------- +test_that('split_formula_list works', { + expect_equal( + split_formula_list(lapply(fmls, "[[", "fmla")), + list(fixed = unlist(lapply(fmls, "[[", 'fixed')), + random = unlist(lapply(fmls, "[[", 'random'))), + ignore_formula_env = TRUE) +}) From 2e03441cc0a413a31c9947f8973798d433dd7f01 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 10:51:34 +0100 Subject: [PATCH 043/176] move function extract_id to helpfunctions_formulas_general.R --- R/helpfunctions_formulas.R | 57 ---------------------------- R/helpfunctions_formulas_general.R | 60 ++++++++++++++++++++++++++++++ 2 files changed, 60 insertions(+), 57 deletions(-) diff --git a/R/helpfunctions_formulas.R b/R/helpfunctions_formulas.R index 10349687..4a0631e7 100644 --- a/R/helpfunctions_formulas.R +++ b/R/helpfunctions_formulas.R @@ -1,61 +1,4 @@ -# used in divide_matrices, get_models, various help functions, -# predict (2020-06-09) -extract_id <- function(random, warn = TRUE) { - # extract all id variables involved in a random effects formula - - # if random is not a list, make it one - random <- check_formula_list(random) - - # check if random is a list of formulas - if (!all(lvapply(random, function(x) inherits(x, "formula") | is.null(x)))) - errormsg("At least one element of %s is not of class %s.", - dQuote("random"), dQuote("formula")) - - ids <- lapply(random, function(x) { - # match the vertical bar (...|...) - rdmatch <- gregexpr(pattern = "\\([^|]*\\|[^)]*\\)", - deparse(x, width.cutoff = 500L)) - - if (any(rdmatch[[1L]] > 0L)) { - # remove "(... | " from the formula - rd <- unlist(regmatches(deparse(x, width.cutoff = 500L), - rdmatch, invert = FALSE)) - rdid <- gregexpr(pattern = "[[:print:]]*\\|[[:space:]]*", rd) - - # extract and remove ) - id <- gsub(")", "", unlist(regmatches(rd, rdid, invert = TRUE))) - - # split by + * : / - id <- unique(unlist(strsplit(id[id != ""], - split = "[[:space:]]*[+*:/][[:space:]]*"))) - } else { - rdmatch <- gregexpr(pattern = "[[:print:]]*\\|[ ]*", - deparse(x, width.cutoff = 500L)) - - if (any(rdmatch[[1L]] > 0L)) { - # remove "... | " from the formula - id <- unlist(regmatches(deparse(x, width.cutoff = 500L), - rdmatch, invert = TRUE)) - id <- unique(unlist(strsplit(id[id != ""], - split = "[[:space:]]*[+*:/][[:space:]]*"))) - - } else { - id <- NULL - } - } - id - }) - - if (is.null(unlist(ids)) & !is.null(unlist(random))) - if (warn) - warnmsg("No %s variable could be identified. I will assume that all - observations are independent.", dQuote("id")) - - unique(unlist(ids)) -} - - # used in divide_matrices, get_models, and helpfunctions (2020-06-09) extract_outcome <- function(fixed) { diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R index 447c120f..229150d5 100644 --- a/R/helpfunctions_formulas_general.R +++ b/R/helpfunctions_formulas_general.R @@ -283,3 +283,63 @@ split_formula_list <- function(formulas) { list(fixed = lapply(l, "[[", "fixed"), random = lapply(l, "[[", "random")) } + + + + +# used in divide_matrices, get_models, various help functions, +# predict (2020-06-09) +extract_id <- function(random, warn = TRUE) { + # extract all id variables involved in a random effects formula + + # if random is not a list, make it one + random <- check_formula_list(random) + + # check if random is a list of formulas + if (!all(lvapply(random, function(x) inherits(x, "formula") | is.null(x)))) + errormsg("At least one element of %s is not of class %s.", + dQuote("random"), dQuote("formula")) + + ids <- lapply(random, function(x) { + # match the vertical bar (...|...) + rdmatch <- gregexpr(pattern = "\\([^|]*\\|[^)]*\\)", + deparse(x, width.cutoff = 500L)) + + if (any(rdmatch[[1L]] > 0L)) { + # remove "(... | " from the formula + rd <- unlist(regmatches(deparse(x, width.cutoff = 500L), + rdmatch, invert = FALSE)) + rdid <- gregexpr(pattern = "[[:print:]]*\\|[[:space:]]*", rd) + + # extract and remove ) + id <- gsub(")", "", unlist(regmatches(rd, rdid, invert = TRUE))) + + # split by + * : / + id <- unique(unlist(strsplit(id[id != ""], + split = "[[:space:]]*[+*:/][[:space:]]*"))) + } else { + rdmatch <- gregexpr(pattern = "[[:print:]]*\\|[ ]*", + deparse(x, width.cutoff = 500L)) + + if (any(rdmatch[[1L]] > 0L)) { + # remove "... | " from the formula + id <- unlist(regmatches(deparse(x, width.cutoff = 500L), + rdmatch, invert = TRUE)) + id <- unique(unlist(strsplit(id[id != ""], + split = "[[:space:]]*[+*:/][[:space:]]*"))) + + } else { + id <- NULL + } + } + id + }) + + if (is.null(unlist(ids)) & !is.null(unlist(random))) + if (warn) + warnmsg("No %s variable could be identified. I will assume that all + observations are independent.", dQuote("id")) + + unique(unlist(ids)) +} + From bf3723f662dd92f4d60f7a710244e0714c45c4c4 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 11:19:59 +0100 Subject: [PATCH 044/176] move functions from helpfunctions_JAGSmodel.R to helpfunctions_print-paste.R --- R/helpfunctions_JAGSmodel.R | 62 +-------------------------------- R/helpfunctions_print-paste.R | 64 +++++++++++++++++++++++++++++++++++ 2 files changed, 65 insertions(+), 61 deletions(-) create mode 100644 R/helpfunctions_print-paste.R diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 33374dff..7aaace7e 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -1,62 +1,4 @@ # help functions --------------------------------------------------------------- -tab <- function(times = 2L) { - # creates a vector of spaces to facilitate indentation - - tb <- " " - paste(rep(tb, times), collapse = "") -} - - -add_dashes <- function(x, width = 95L) { - # add separation lines between sub-models in JAGS model for readability - # - x: name of the sub-model - - paste(x, paste0(rep("-", 80L - nchar(x)), collapse = "")) -} - - -add_linebreaks <- function(string, indent, width = 90L) { - # add linebreaks to a string, breaking it after a "+" sign - # - string: the linear predictor string to be broken - # - indent: in case of a linebreak, how much should the new line be indented? - # - width: output width - - if (is.null(string)) { - return(NULL) - } - - # identify position of "+" - m <- gregexpr(" \\+ ", string)[[1L]] - - # if there is no "+", return the original string - if (all(m < 0L)) { - return(string) - } - - # calculate the lengths of the sub-strings - len <- c(as.numeric(m)[1L], diff(c(as.numeric(m), nchar(string)))) - - - # check how many sub-strings (and the indent) can be combined until reaching - # the maximal width, and create a string of " + " (no break) and - # " +\n" (break) to be pasted in afterwards - # (there is probably a more elegant way to do this) - i <- 1L - br <- character(0L) - while (i < length(len)) { - cs <- cumsum(len[i:length(len)]) - nfit <- max(1L, which(cs <= (width - indent))) - br <- c( - br, rep(" + ", nfit - 1L), - if ((i + nfit - 1L) < length(len)) paste0(" +\n", tab(indent)) - ) - i <- i + nfit - } - - paste0(strsplit(string, " \\+ ")[[1L]], c(br, ""), collapse = "") -} - - # linear predictors ------------------------------------------------------------ paste_linpred <- function(parname, parelmts, matnam, index, cols, scale_pars, @@ -1283,6 +1225,4 @@ paste_ps <- function(nr, env = parent.frame()) { paste0("psum", if (gk) "gk", "_", vn, "[", ind, ", ", nr, if (gk) ", k", "]") } -minmax <- function(x, max = "1-1e-10", min = "1e-10") { - paste0("max(", min, ", min(", max, ", ", x, "))") -} + diff --git a/R/helpfunctions_print-paste.R b/R/helpfunctions_print-paste.R new file mode 100644 index 00000000..1d71742e --- /dev/null +++ b/R/helpfunctions_print-paste.R @@ -0,0 +1,64 @@ + +# for JAGSmodel functions ------------------------------------------------------ + +add_dashes <- function(x, width = 95L) { + # add separation lines between sub-models in JAGS model for readability + # - x: name of the sub-model + + paste(x, paste0(rep("-", 80L - nchar(x)), collapse = "")) +} + + + +add_linebreaks <- function(string, indent, width = 90L) { + # add linebreaks to a string, breaking it after a "+" sign + # - string: the linear predictor string to be broken + # - indent: in case of a linebreak, how much should the new line be indented? + # - width: output width + + if (is.null(string)) { + return(NULL) + } + + # identify position of "+" + m <- gregexpr(" \\+ ", string)[[1L]] + + # if there is no "+", return the original string + if (all(m < 0L)) { + return(string) + } + + # calculate the lengths of the sub-strings + len <- c(as.numeric(m)[1L], diff(c(as.numeric(m), nchar(string)))) + + + # check how many sub-strings (and the indent) can be combined until reaching + # the maximal width, and create a string of " + " (no break) and + # " +\n" (break) to be pasted in afterwards + # (there is probably a more elegant way to do this) + i <- 1L + br <- character(0L) + while (i < length(len)) { + cs <- cumsum(len[i:length(len)]) + nfit <- max(1L, which(cs <= (width - indent))) + br <- c( + br, rep(" + ", nfit - 1L), + if ((i + nfit - 1L) < length(len)) paste0(" +\n", tab(indent)) + ) + i <- i + nfit + } + + paste0(strsplit(string, " \\+ ")[[1L]], c(br, ""), collapse = "") +} + +minmax <- function(x, max = "1-1e-10", min = "1e-10") { + # wrap a character string into max(min(...)); min-max-trick in JAGSmodel + paste0("max(", min, ", min(", max, ", ", x, "))") +} + +tab <- function(times = 2L) { + # creates a vector of spaces to facilitate indentation + tb <- " " + paste(rep(tb, times), collapse = "") +} + From cce1a8c349afb9a5afbaec4100294f3fb36d2a22 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 12:02:34 +0100 Subject: [PATCH 045/176] update documentation extract_id --- R/helpfunctions_formulas_general.R | 19 ++++++++++--------- man/extract_id.Rd | 18 ++++++++++++++++++ 2 files changed, 28 insertions(+), 9 deletions(-) create mode 100644 man/extract_id.Rd diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R index 229150d5..11ce3458 100644 --- a/R/helpfunctions_formulas_general.R +++ b/R/helpfunctions_formulas_general.R @@ -287,19 +287,20 @@ split_formula_list <- function(formulas) { -# used in divide_matrices, get_models, various help functions, -# predict (2020-06-09) +#' Extract all id variables from a list of random effects formulas +#' +#' Internal function, used in `divide_matrices()`, `get_models()`, +#' various help functions, `predict()` (2022-02-06) +#' +#' @param random a one-sided random effects formula or a list of such formulas +#' @param warn logical; should warnings be printed? +#' @keywords internal +#' +#' extract_id <- function(random, warn = TRUE) { - # extract all id variables involved in a random effects formula - # if random is not a list, make it one random <- check_formula_list(random) - # check if random is a list of formulas - if (!all(lvapply(random, function(x) inherits(x, "formula") | is.null(x)))) - errormsg("At least one element of %s is not of class %s.", - dQuote("random"), dQuote("formula")) - ids <- lapply(random, function(x) { # match the vertical bar (...|...) rdmatch <- gregexpr(pattern = "\\([^|]*\\|[^)]*\\)", diff --git a/man/extract_id.Rd b/man/extract_id.Rd new file mode 100644 index 00000000..c2393d74 --- /dev/null +++ b/man/extract_id.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_formulas_general.R +\name{extract_id} +\alias{extract_id} +\title{Extract all id variables from a list of random effects formulas} +\usage{ +extract_id(random, warn = TRUE) +} +\arguments{ +\item{random}{a one-sided random effects formula or a list of such formulas} + +\item{warn}{logical; should warnings be printed?} +} +\description{ +Internal function, used in \code{divide_matrices()}, \code{get_models()}, +various help functions, \code{predict()} (2022-02-06) +} +\keyword{internal} From b904f7c4b6958c9cf66b98716f7b0c385f563c6a Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 12:03:12 +0100 Subject: [PATCH 046/176] move tests for extract_id to file test-helpfunctions_formulas_general.R --- tests/testthat/test-helpfunctions_formulas.R | 48 -------------- .../test-helpfunctions_formulas_general.R | 64 +++++++++++++++++++ 2 files changed, 64 insertions(+), 48 deletions(-) diff --git a/tests/testthat/test-helpfunctions_formulas.R b/tests/testthat/test-helpfunctions_formulas.R index 27f44f77..fe564552 100644 --- a/tests/testthat/test-helpfunctions_formulas.R +++ b/tests/testthat/test-helpfunctions_formulas.R @@ -5,7 +5,6 @@ library("survival") -# extract_id-------------------------------------------------------------- runs <- list(list(random = ~ 1 | id, ids = 'id', RHS = list(~ 1 | id), nogroup = list(id = ~ 1)), list(random = ~ 0 | id, ids = 'id', RHS = list(~ 0 | id), @@ -19,53 +18,6 @@ runs <- list(list(random = ~ 1 | id, ids = 'id', RHS = list(~ 1 | id), nogroup = list(y ~ 0)) ) -test_that('extract_id works', { - for (i in setdiff(seq_along(runs), c(4, 6))) { - expect_equal(extract_id(runs[[i]]$random), runs[[i]]$ids) - } - - # test all together - expect_equal(extract_id(lapply(runs, "[[", 'random')), - unlist(unique(lapply(runs, "[[", 'ids')))) -}) - - -test_that('extract_id gives warning', { - for (i in c(4, 6)) { - expect_warning(extract_id(runs[[i]]$random), runs[[i]]$ids) - } - - # test all together - expect_equal(extract_id(lapply(runs, "[[", 'random')), - unlist(unique(lapply(runs, "[[", 'ids')))) -}) - - -test_that('extract_id results in error', { - err <- list( - "text", - NA, - TRUE, - mean, - list(random = ~ a | id/class, ids = c('id', 'class')), - list(random = ~ a | id + class, ids = c('id', 'class')), - list(random = list(~a | id, ~ b | id2), ids = c('id', 'id2')) - ) - - for (i in seq_along(err)) { - expect_error(extract_id(err[[i]])) - } -}) - - -test_that('extract_id results in warning', { - rd_warn <- list(~1, - ~a + b + c) - - for (i in seq_along(rd_warn)) { - expect_warning(extract_id(rd_warn[[i]])) - } -}) # extract_outcome ---------------------------------------------------------- diff --git a/tests/testthat/test-helpfunctions_formulas_general.R b/tests/testthat/test-helpfunctions_formulas_general.R index c5671c65..caf23320 100644 --- a/tests/testthat/test-helpfunctions_formulas_general.R +++ b/tests/testthat/test-helpfunctions_formulas_general.R @@ -263,3 +263,67 @@ test_that('split_formula_list works', { random = unlist(lapply(fmls, "[[", 'random'))), ignore_formula_env = TRUE) }) + + + +# extract_id-------------------------------------------------------------- +runs <- list(list(random = ~ 1 | id, ids = 'id', RHS = list(~ 1 | id), + nogroup = list(id = ~ 1)), + list(random = ~ 0 | id, ids = 'id', RHS = list(~ 0 | id), + nogroup = list(id = ~ 0)), + list(random = NULL, ids = NULL, RHS = NULL, nogroup = NULL), + list(random = y ~ a + b + c, ids = NULL, RHS = list(~a + b + c), + nogroup = list(y ~ a + b + c)), + list(random = y ~ time | id, ids = 'id', RHS = list(~time | id), + nogroup = list(id = y ~ time)), + list(random = y ~ 0, ids = NULL, RHS = list(~ 0), + nogroup = list(y ~ 0)) +) + +test_that('extract_id works', { + for (i in setdiff(seq_along(runs), c(4, 6))) { + expect_equal(extract_id(runs[[i]]$random), runs[[i]]$ids) + } + + # test all together + expect_equal(extract_id(lapply(runs, "[[", 'random')), + unlist(unique(lapply(runs, "[[", 'ids')))) +}) + + +test_that('extract_id gives warning', { + for (i in c(4, 6)) { + expect_warning(extract_id(runs[[i]]$random), runs[[i]]$ids) + } + + # test all together + expect_equal(extract_id(lapply(runs, "[[", 'random')), + unlist(unique(lapply(runs, "[[", 'ids')))) +}) + + +test_that('extract_id results in error', { + err <- list( + "text", + NA, + TRUE, + mean, + list(random = ~ a | id/class, ids = c('id', 'class')), + list(random = ~ a | id + class, ids = c('id', 'class')), + list(random = list(~a | id, ~ b | id2), ids = c('id', 'id2')) + ) + + for (i in seq_along(err)) { + expect_error(extract_id(err[[i]])) + } +}) + + +test_that('extract_id results in warning', { + rd_warn <- list(~1, + ~a + b + c) + + for (i in seq_along(rd_warn)) { + expect_warning(extract_id(rd_warn[[i]])) + } +}) From 6c9b21b0fc4f2f9b3325514e041c7656448ec365 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 12:03:54 +0100 Subject: [PATCH 047/176] move code to check/set the thinnning parameter and which variables to monitor to helper functions and add some comments --- R/add_samples.R | 83 +++++++++++++++++++++++++++++++------------------ 1 file changed, 53 insertions(+), 30 deletions(-) diff --git a/R/add_samples.R b/R/add_samples.R index 1810cd84..a1f4dc4d 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -54,38 +54,16 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, if (!inherits(object, "JointAI")) errormsg("Use only with 'JointAI' objects.") + # check/set settings ----------------------------------------------------- + thin <- check_add_thinning(object = object, thin = thin, add = add, + mess = mess) - if (is.null(thin)) { - thin <- object$mcmc_settings$thin[length(object$mcmc_settings$thin)] - } else { - if (add & - thin != object$mcmc_settings$thin[length(object$mcmc_settings$thin)]) { - thin <- object$mcmc_settings$thin[length(object$mcmc_settings$thin)] - - if (mess) - msg("When adding samples (%s) the thinning interval cannot be - changed. I will use the setting of the existing object - (%s).", dQuote("add = TRUE"), dQuote(paste0("thin = ", thin))) - } - } - - if (is.null(monitor_params)) { - var_names <- object$mcmc_settings$variable.names - } else { - var_names <- do.call(get_params, - c(list(Mlist = get_Mlist(object), - info_list = object$info_list, - mess = mess), - monitor_params)) - } - - - if (!identical(var_names, object$mcmc_settings$variable.names) & add) - errormsg("When %s it is not possible to monitor different parameters than - were monitored in the original model.", dQuote("add = TRUE")) + var_names <- check_add_varnames(object = object, + monitor_params = monitor_params, mess = mess) future_info <- get_future_info() + # run mcmc ---------------------------------------------------------------- t0 <- Sys.time() if (future_info$parallel) { if (mess) @@ -106,22 +84,26 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, t1 <- Sys.time() - + # process MCMC samples -------------------------------------------------------- MCMC <- mcmc if (!all(sapply(object$Mlist$scale_pars, is.null))) { coefs <- try(get_coef_names(object$info_list)) + for (k in seq_len(length(MCMC))) { MCMC[[k]] <- coda::as.mcmc( - rescale(MCMC[[k]], coefs = do.call(rbind, coefs), + rescale(MCMC[[k]], + coefs = do.call(rbind, coefs), scale_pars = do.call(rbind, unname(object$Mlist$scale_pars)), info_list = object$info_list, data_list = object$data_list, groups = object$Mlist$groups)) + attr(MCMC[[k]], "mcpar") <- attr(mcmc[[k]], "mcpar") } } + # combine with/replace original samples -------------------------------------- if (isTRUE(add)) { newmcmc <- if (!is.null(object$sample)) { coda::as.mcmc.list( @@ -148,6 +130,8 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, newMCMC <- MCMC } + + # create new JointAI object -------------------------------------------------- newobject <- object newobject$sample <- newmcmc newobject$MCMC <- newMCMC @@ -172,3 +156,42 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, return(newobject) } + + + + +check_add_thinning <- function(thin, object, add, mess = TRUE) { + + if (is.null(thin)) { + thin <- object$mcmc_settings$thin[length(object$mcmc_settings$thin)] + } else { + if (add & + thin != object$mcmc_settings$thin[length(object$mcmc_settings$thin)]) { + thin <- object$mcmc_settings$thin[length(object$mcmc_settings$thin)] + + if (mess) + msg("When adding samples (%s) the thinning interval cannot be + changed. I will use the setting of the existing object + (%s).", dQuote("add = TRUE"), dQuote(paste0("thin = ", thin))) + } + } +} + + + +check_add_varnames <- function(object, monitor_params, mess, add) { + + if (is.null(monitor_params)) { + var_names <- object$mcmc_settings$variable.names + } else { + if (!identical(var_names, object$mcmc_settings$variable.names) & add) + errormsg("When %s it is not possible to monitor different parameters than + were monitored in the original model.", dQuote("add = TRUE")) + + var_names <- do.call(get_params, + c(list(Mlist = get_Mlist(object), + info_list = object$info_list, + mess = mess), + monitor_params)) + } +} From 2185a6ccb3e9f9c0638913a4f0112da3306b2c77 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 12:04:17 +0100 Subject: [PATCH 048/176] add internal documentation for add_linebreaks() --- R/helpfunctions_print-paste.R | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/R/helpfunctions_print-paste.R b/R/helpfunctions_print-paste.R index 1d71742e..96f5510b 100644 --- a/R/helpfunctions_print-paste.R +++ b/R/helpfunctions_print-paste.R @@ -9,12 +9,18 @@ add_dashes <- function(x, width = 95L) { } +#' Add line breaks to a linear predictor string +#' +#' Adds line breaks to a string, breaking it after a "+" sign to not exceed a +#' given width of characters and taking into account indentation. +#' +#' @param string a character string (linear predictor) +#' @param indent integer; number of characters the new line should be indented +#' @param width integer; the maximum number of characters per line +#' +#' @keywords internal add_linebreaks <- function(string, indent, width = 90L) { - # add linebreaks to a string, breaking it after a "+" sign - # - string: the linear predictor string to be broken - # - indent: in case of a linebreak, how much should the new line be indented? - # - width: output width if (is.null(string)) { return(NULL) @@ -51,14 +57,20 @@ add_linebreaks <- function(string, indent, width = 90L) { paste0(strsplit(string, " \\+ ")[[1L]], c(br, ""), collapse = "") } + + + + minmax <- function(x, max = "1-1e-10", min = "1e-10") { # wrap a character string into max(min(...)); min-max-trick in JAGSmodel paste0("max(", min, ", min(", max, ", ", x, "))") } + + + tab <- function(times = 2L) { # creates a vector of spaces to facilitate indentation tb <- " " paste(rep(tb, times), collapse = "") } - From 8bed45369af8eff56b8bd889709e61d1445a23a3 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 13:09:06 +0100 Subject: [PATCH 049/176] add "--no-manual" build argument to rmd check action --- .github/workflows/R-CMD-check.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 7e3b0686..53d47773 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -109,7 +109,7 @@ jobs: if (os == "windows" & R.version$major == 3) {remotes::install_version("rjags", version = "4-10")} jagshome = if (os == "windows") {readRegistry("SOFTWARE\\JAGS", maxdepth = 2, view = "32-bit")} if (os == "windows"){Sys.setenv(JAGS_HOME = try(jagshome[["JAGS-4.3.0"]][["InstallDir"]]))} - rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check") + rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), build_args = "--no-manual", error_on = "warning", check_dir = "check") shell: Rscript {0} - name: Show testthat output From b478ec5762e16c7e8c1852b629794522d51fbc17 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 13:15:43 +0100 Subject: [PATCH 050/176] bugfix: helper functions need to return the value --- R/add_samples.R | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/R/add_samples.R b/R/add_samples.R index a1f4dc4d..8fb3ae31 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -175,6 +175,7 @@ check_add_thinning <- function(thin, object, add, mess = TRUE) { (%s).", dQuote("add = TRUE"), dQuote(paste0("thin = ", thin))) } } + thin } @@ -182,16 +183,16 @@ check_add_thinning <- function(thin, object, add, mess = TRUE) { check_add_varnames <- function(object, monitor_params, mess, add) { if (is.null(monitor_params)) { - var_names <- object$mcmc_settings$variable.names + object$mcmc_settings$variable.names } else { if (!identical(var_names, object$mcmc_settings$variable.names) & add) errormsg("When %s it is not possible to monitor different parameters than were monitored in the original model.", dQuote("add = TRUE")) - var_names <- do.call(get_params, - c(list(Mlist = get_Mlist(object), - info_list = object$info_list, - mess = mess), - monitor_params)) + do.call(get_params, + c(list(Mlist = get_Mlist(object), + info_list = object$info_list, + mess = mess), + monitor_params)) } } From becbdba685453ff1a18f116f69ab2173913c6c0c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 13:16:10 +0100 Subject: [PATCH 051/176] bugfix: all_vars needs to return NULL if the input is NULL --- R/helpfunctions_formulas_general.R | 3 +++ 1 file changed, 3 insertions(+) diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R index 11ce3458..9797eff8 100644 --- a/R/helpfunctions_formulas_general.R +++ b/R/helpfunctions_formulas_general.R @@ -208,6 +208,9 @@ extract_lhs <- function(formula) { all_vars <- function(fmla) { + if (is.null(fmla)) + return(NULL) + if (inherits(fmla, "list")) { unique(unlist(lapply(fmla, all.vars))) } else if (inherits(fmla, "formula")) { From d51b2d349fed2f6003bd1c009e8ff799d6095779 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 13:34:57 +0100 Subject: [PATCH 052/176] bugfix --- R/add_samples.R | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/R/add_samples.R b/R/add_samples.R index 8fb3ae31..e60c41c4 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -183,16 +183,17 @@ check_add_thinning <- function(thin, object, add, mess = TRUE) { check_add_varnames <- function(object, monitor_params, mess, add) { if (is.null(monitor_params)) { - object$mcmc_settings$variable.names + var_names <- object$mcmc_settings$variable.names } else { - if (!identical(var_names, object$mcmc_settings$variable.names) & add) - errormsg("When %s it is not possible to monitor different parameters than + var_names <- do.call(get_params, + c(list(Mlist = get_Mlist(object), + info_list = object$info_list, + mess = mess), + monitor_params)) + } + if (!identical(var_names, object$mcmc_settings$variable.names) & add) + errormsg("When %s it is not possible to monitor different parameters than were monitored in the original model.", dQuote("add = TRUE")) - do.call(get_params, - c(list(Mlist = get_Mlist(object), - info_list = object$info_list, - mess = mess), - monitor_params)) - } + var_names } From 01804bc0e892818a76d8bcef85a8abb9e4666961 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 13:35:20 +0100 Subject: [PATCH 053/176] exclude set_refcat function from codecov Signed-off-by: Nicole Erler --- R/get_refs.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/get_refs.R b/R/get_refs.R index 940bf4d3..1b6fb298 100644 --- a/R/get_refs.R +++ b/R/get_refs.R @@ -145,7 +145,7 @@ get_refs <- function(fmla, data, refcats = NULL, warn = TRUE) { #' #' @export -set_refcat <- function(data, formula, covars, auxvars = NULL) { +set_refcat <- function(data, formula, covars, auxvars = NULL) {# nocov start if (missing(formula) & missing(covars) & is.null(auxvars)) { covars <- colnames(data) @@ -191,5 +191,5 @@ set_refcat <- function(data, formula, covars, auxvars = NULL) { function.", out) return(invisible(q2)) -} +} # nocov end From e64fef91ce6d69c7036869975b9f36835704e11a Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 13:39:36 +0100 Subject: [PATCH 054/176] added internal documentation for add_linebreaks --- man/add_linebreaks.Rd | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 man/add_linebreaks.Rd diff --git a/man/add_linebreaks.Rd b/man/add_linebreaks.Rd new file mode 100644 index 00000000..335629a0 --- /dev/null +++ b/man/add_linebreaks.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_print-paste.R +\name{add_linebreaks} +\alias{add_linebreaks} +\title{Add line breaks to a linear predictor string} +\usage{ +add_linebreaks(string, indent, width = 90L) +} +\arguments{ +\item{string}{a character string (linear predictor)} + +\item{indent}{integer; number of characters the new line should be indented} + +\item{width}{integer; the maximum number of characters per line} +} +\description{ +Adds line breaks to a string, breaking it after a "+" sign to not exceed a +given width of characters and taking into account indentation. +} +\keyword{internal} From e75742d034ecf62f3ccf1f5154837435b6b65de7 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 13:52:50 +0100 Subject: [PATCH 055/176] bugfix, forgotten argument "add" --- R/add_samples.R | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/R/add_samples.R b/R/add_samples.R index e60c41c4..a9032b1d 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -59,7 +59,8 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, mess = mess) var_names <- check_add_varnames(object = object, - monitor_params = monitor_params, mess = mess) + monitor_params = monitor_params, + add = add, mess = mess) future_info <- get_future_info() @@ -180,7 +181,7 @@ check_add_thinning <- function(thin, object, add, mess = TRUE) { -check_add_varnames <- function(object, monitor_params, mess, add) { +check_add_varnames <- function(object, monitor_params, mess = TRUE, add) { if (is.null(monitor_params)) { var_names <- object$mcmc_settings$variable.names From 8c06f8c5f9812fa616064e78558433228c2d2d4c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 14:28:27 +0100 Subject: [PATCH 056/176] moved functions to new file helpfunctions_vcov.R --- R/helpfunctions_divide_matrices.R | 149 ----------------------------- R/helpfunctions_vcov.R | 150 ++++++++++++++++++++++++++++++ 2 files changed, 150 insertions(+), 149 deletions(-) create mode 100644 R/helpfunctions_vcov.R diff --git a/R/helpfunctions_divide_matrices.R b/R/helpfunctions_divide_matrices.R index 33bad0dc..ea33f42c 100644 --- a/R/helpfunctions_divide_matrices.R +++ b/R/helpfunctions_divide_matrices.R @@ -719,155 +719,6 @@ get_nonprop_lp <- function(nonprop, dsgn_mat_lvls, data, refs, fixed) { -#' First validation for rd_vcov -#' -#' Check if rd_vcov is a list with elements for all grouping levels or does -#' not specify a grouping level. If valid, make sure it is a list per grouping -#' level by duplicating the contents if necessary. -#' -#' @param rd_vcov the random effects variance covariance structure provided by -#' the user -#' @param idvar vector with the names of the grouping variables -#' (without "lvlone") -#' @keywords internal -#' @return A named list per grouping level where each elements contains -#' information on how the random effects variance-covariance matrices on -#' that level are structured. Per level it can be either a character -#' string (e.g. `"full"`) or a list specifying structures per (groups) of -#' variable(s) (e.g. `list(full = c("a", "b"), indep = "c")`) -check_rd_vcov_list <- function(rd_vcov, idvar) { - - if (!inherits(rd_vcov, "list") | all(!idvar %in% names(rd_vcov))) { - nlapply(idvar, function(x) rd_vcov) - } else if (inherits(rd_vcov, "list") & any(!idvar %in% names(rd_vcov))) { - errormsg("Please provide information on the variance-covariance structure - of the random effects for all levels.") - } else { - rd_vcov - } -} - - -#' Expand rd_vcov using variable names in case "full" is used -#' -#' -#' @param rd_vcov the random effects variance covariance structure provided by -#' the user (`check_rd_vcov_list()` is called internally) -#' @param rd_outnam list by grouping level of the names of the outcome variables -#' that have random effects on this level -#' @keywords internal -#' @return A named list per grouping level where each elements contains -#' information on how the random effects variance-covariance matrices on -#' that level are structured. Per level there is a list of grouping structures -#' containing the names of variables in each structure -#' (e.g. `list(full = c("a", "b"), indep = "c")`) - -expand_rd_vcov_full <- function(rd_vcov, rd_outnam) { - idvar <- names(rd_outnam) - - rd_vcov <- check_rd_vcov_list(rd_vcov, idvar) - - nlapply(idvar, function(lvl) { - if (is.character(rd_vcov[[lvl]]) & length(rd_vcov[[lvl]]) == 1) { - - setNames(list(rd_outnam[[lvl]]), rd_vcov[[lvl]]) - - } else if (inherits(rd_vcov[[lvl]], "list")) { - - if (setequal(unlist(rd_vcov[[lvl]]), rd_outnam[[lvl]])) { - rd_vcov[[lvl]] - } else { - errormsg("According to the random effects formulas, there are - random effects on the level %s for the models for %s but in - the structure specified for the random effects - variance-covariance matrices the variables %s have random - effects on this level.", - dQuote(lvl), paste_and(dQuote(rd_outnam[[lvl]])), - paste_and(dQuote(unlist(rd_vcov[[lvl]]))) - ) - } - - } else { - errormsg("%s should be a character string or a list.", - dQuote("rd_vcov[[lvl]]")) - } - }) -} - - -#' Replace a full with a block-diagonal variance covariance matrix -#' Check if a full random effects variance covariance matrix is specified -#' for a single variable. In that case, it is identical to a block-diagonal -#' matrix. Change the `rd_vcov` specification to `blockdiag` for clarity -#' (because then the variable name is used in the name of `b`, `D`, `invD`, ...) -#' -#' @param rd_vcov a valid random effects variance-covariance structure -#' specification (i.e., checked using `expand_rd_vcov_full()`) -#' @return a valid random effects variance-covariance structure specification -#' @keywords internal - -check_full_blockdiag <- function(rd_vcov) { - - if (!inherits(rd_vcov, "list") | any(!lvapply(rd_vcov, inherits, "list"))) { - errormsg("%s should be a list (by grouping level) of lists - (per covariance matrix).", dQuote("rd_vcov")) - } - - nlapply(names(rd_vcov), function(lvl) { - bd <- names(rd_vcov[[lvl]]) == "full" & - ivapply(rd_vcov[[lvl]], length) == 1 - names(rd_vcov[[lvl]])[bd] <- "blockdiag" - rd_vcov[[lvl]] - }) -} - - -#' Check / create the random effects variance-covariance matrix specification -#' @param rd_vcov variance covariance specification provided by the user -#' @param nranef list by level with named vectors of number of random effects -#' per variable (obtained by `get_nranef()`) -#' @keywords internal - -check_rd_vcov <- function(rd_vcov, nranef) { - - idvar <- names(nranef) - - rd_vcov <- expand_rd_vcov_full(rd_vcov, - rd_outnam = nlapply(nranef, function(r) { - names(r)[r > 0L]})) - - rd_vcov <- check_full_blockdiag(rd_vcov) - - - if (any(unlist(lapply(rd_vcov, names)) == "full")) { - for (lvl in idvar) { - - ## if a full vcov is used, determine the number of random effects - for (k in which(names(rd_vcov[[lvl]]) == "full")) { - - nrd <- nranef[[lvl]][rd_vcov[[lvl]][[k]]] - - ranef_nr <- print_seq( - min = cumsum(c(1, nrd))[-(length(nrd) + 1)], - max = cumsum(nrd) - ) - - attr(rd_vcov[[lvl]][[k]], "ranef_index") <- - setNames(ranef_nr, rd_vcov[[lvl]][[k]]) - } - - ## if there is more than one full vcov, number them - if (sum(names(rd_vcov[[lvl]]) %in% "full") > 1) { - rd_full <- which(names(rd_vcov[[lvl]]) %in% "full") - for (k in seq_along(rd_full)) { - attr(rd_vcov[[lvl]][[rd_full[k]]], "name") <- k - } - } - } - } - rd_vcov -} - #' Extract the number of random effects #' @param idvar vector of the names of id variables diff --git a/R/helpfunctions_vcov.R b/R/helpfunctions_vcov.R new file mode 100644 index 00000000..9128a9f1 --- /dev/null +++ b/R/helpfunctions_vcov.R @@ -0,0 +1,150 @@ + +#' First validation for rd_vcov +#' +#' Check if rd_vcov is a list with elements for all grouping levels or does +#' not specify a grouping level. If valid, make sure it is a list per grouping +#' level by duplicating the contents if necessary. +#' +#' @param rd_vcov the random effects variance covariance structure provided by +#' the user +#' @param idvar vector with the names of the grouping variables +#' (without "lvlone") +#' @keywords internal +#' @return A named list per grouping level where each elements contains +#' information on how the random effects variance-covariance matrices on +#' that level are structured. Per level it can be either a character +#' string (e.g. `"full"`) or a list specifying structures per (groups) of +#' variable(s) (e.g. `list(full = c("a", "b"), indep = "c")`) +check_rd_vcov_list <- function(rd_vcov, idvar) { + + if (!inherits(rd_vcov, "list") | all(!idvar %in% names(rd_vcov))) { + nlapply(idvar, function(x) rd_vcov) + } else if (inherits(rd_vcov, "list") & any(!idvar %in% names(rd_vcov))) { + errormsg("Please provide information on the variance-covariance structure + of the random effects for all levels.") + } else { + rd_vcov + } +} + + +#' Expand rd_vcov using variable names in case "full" is used +#' +#' +#' @param rd_vcov the random effects variance covariance structure provided by +#' the user (`check_rd_vcov_list()` is called internally) +#' @param rd_outnam list by grouping level of the names of the outcome variables +#' that have random effects on this level +#' @keywords internal +#' @return A named list per grouping level where each elements contains +#' information on how the random effects variance-covariance matrices on +#' that level are structured. Per level there is a list of grouping structures +#' containing the names of variables in each structure +#' (e.g. `list(full = c("a", "b"), indep = "c")`) + +expand_rd_vcov_full <- function(rd_vcov, rd_outnam) { + idvar <- names(rd_outnam) + + rd_vcov <- check_rd_vcov_list(rd_vcov, idvar) + + nlapply(idvar, function(lvl) { + if (is.character(rd_vcov[[lvl]]) & length(rd_vcov[[lvl]]) == 1) { + + setNames(list(rd_outnam[[lvl]]), rd_vcov[[lvl]]) + + } else if (inherits(rd_vcov[[lvl]], "list")) { + + if (setequal(unlist(rd_vcov[[lvl]]), rd_outnam[[lvl]])) { + rd_vcov[[lvl]] + } else { + errormsg("According to the random effects formulas, there are + random effects on the level %s for the models for %s but in + the structure specified for the random effects + variance-covariance matrices the variables %s have random + effects on this level.", + dQuote(lvl), paste_and(dQuote(rd_outnam[[lvl]])), + paste_and(dQuote(unlist(rd_vcov[[lvl]]))) + ) + } + + } else { + errormsg("%s should be a character string or a list.", + dQuote("rd_vcov[[lvl]]")) + } + }) +} + + +#' Replace a full with a block-diagonal variance covariance matrix +#' Check if a full random effects variance covariance matrix is specified +#' for a single variable. In that case, it is identical to a block-diagonal +#' matrix. Change the `rd_vcov` specification to `blockdiag` for clarity +#' (because then the variable name is used in the name of `b`, `D`, `invD`, ...) +#' +#' @param rd_vcov a valid random effects variance-covariance structure +#' specification (i.e., checked using `expand_rd_vcov_full()`) +#' @return a valid random effects variance-covariance structure specification +#' @keywords internal + +check_full_blockdiag <- function(rd_vcov) { + + if (!inherits(rd_vcov, "list") | any(!lvapply(rd_vcov, inherits, "list"))) { + errormsg("%s should be a list (by grouping level) of lists + (per covariance matrix).", dQuote("rd_vcov")) + } + + nlapply(names(rd_vcov), function(lvl) { + bd <- names(rd_vcov[[lvl]]) == "full" & + ivapply(rd_vcov[[lvl]], length) == 1 + names(rd_vcov[[lvl]])[bd] <- "blockdiag" + rd_vcov[[lvl]] + }) +} + + +#' Check / create the random effects variance-covariance matrix specification +#' +#' @param rd_vcov variance covariance specification provided by the user +#' @param nranef list by level with named vectors of number of random effects +#' per variable (obtained by `get_nranef()`) +#' @keywords internal + +check_rd_vcov <- function(rd_vcov, nranef) { + + idvar <- names(nranef) + + rd_vcov <- expand_rd_vcov_full(rd_vcov, + rd_outnam = nlapply(nranef, function(r) { + names(r)[r > 0L]})) + + rd_vcov <- check_full_blockdiag(rd_vcov) + + + if (any(unlist(lapply(rd_vcov, names)) == "full")) { + for (lvl in idvar) { + + ## if a full vcov is used, determine the number of random effects + for (k in which(names(rd_vcov[[lvl]]) == "full")) { + + nrd <- nranef[[lvl]][rd_vcov[[lvl]][[k]]] + + ranef_nr <- print_seq( + min = cumsum(c(1, nrd))[-(length(nrd) + 1)], + max = cumsum(nrd) + ) + + attr(rd_vcov[[lvl]][[k]], "ranef_index") <- + setNames(ranef_nr, rd_vcov[[lvl]][[k]]) + } + + ## if there is more than one full vcov, number them + if (sum(names(rd_vcov[[lvl]]) %in% "full") > 1) { + rd_full <- which(names(rd_vcov[[lvl]]) %in% "full") + for (k in seq_along(rd_full)) { + attr(rd_vcov[[lvl]][[rd_full[k]]], "name") <- k + } + } + } + } + rd_vcov +} From b01022c5db21c7f7b72962a7fbb05bef2d5c65c5 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 14:35:06 +0100 Subject: [PATCH 057/176] renamed file --- ...est-helpfun_divide_matrices.R => test-helpfun_vcov.R} | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) rename tests/testthat/{test-helpfun_divide_matrices.R => test-helpfun_vcov.R} (97%) diff --git a/tests/testthat/test-helpfun_divide_matrices.R b/tests/testthat/test-helpfun_vcov.R similarity index 97% rename from tests/testthat/test-helpfun_divide_matrices.R rename to tests/testthat/test-helpfun_vcov.R index 0d6bbbaf..98e520f5 100644 --- a/tests/testthat/test-helpfun_divide_matrices.R +++ b/tests/testthat/test-helpfun_vcov.R @@ -1,8 +1,5 @@ -context("help functions divide_matrices") -library("JointAI") - test_that('check_rd_vcov_list', { - # rd_vcov is string + # rd_vcov is a string expect_equal( check_rd_vcov_list(rd_vcov = "full", idvar = "id"), list(id = "full")) @@ -16,7 +13,7 @@ test_that('check_rd_vcov_list', { list(id = "indep", center = "indep")) - # rd_vcov is list of model structures (no level specified) + # rd_vcov is a list of model structures (no level specified) expect_equal( check_rd_vcov_list(rd_vcov = list(full = c("a", "b")), idvar = "id"), list(id = list(full = c("a", "b"))) @@ -62,7 +59,7 @@ test_that('check_rd_vcov_list', { }) -test_that('expand_rd_vcov_full', { +test_that('expand_rd_vcov_full works', { expect_equal( expand_rd_vcov_full(rd_vcov = "full", rd_outnam = list(id = c("a", "b"))), From 474610787e8863b6d252dce84129f046a64981d4 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:26:54 +0100 Subject: [PATCH 058/176] minor fix in all_vars() to make sure an error is returned is the provided formula has non-formula elements. --- R/helpfunctions_formulas_general.R | 8 +++++--- man/all_vars.Rd | 6 ++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/R/helpfunctions_formulas_general.R b/R/helpfunctions_formulas_general.R index 9797eff8..d32d99fd 100644 --- a/R/helpfunctions_formulas_general.R +++ b/R/helpfunctions_formulas_general.R @@ -198,7 +198,8 @@ extract_lhs <- function(formula) { #' Extract names of variables from a (list of) formula(s) -#' Version of `all.vars()` that can handle lists of formulas +#' +#' Version of `all.vars()` that can handle lists of formulas. #' #' #' @param fmla a formula or list of formulas @@ -212,7 +213,7 @@ all_vars <- function(fmla) { return(NULL) if (inherits(fmla, "list")) { - unique(unlist(lapply(fmla, all.vars))) + unique(unlist(lapply(fmla, all_vars))) } else if (inherits(fmla, "formula")) { all.vars(fmla) } else { @@ -224,6 +225,7 @@ all_vars <- function(fmla) { #' Split a formula into fixed and random effects parts +#' #' Split a lme4 style formula into nlme style formulas. #' #' Internal function, used in *_imp and help functions (2022-02-06) @@ -267,6 +269,7 @@ split_formula <- function(formula) { #' Split a list of formulas into fixed and random effects parts. +#' #' Calls `split_formula()` on each formula in a list to create one list of the #' fixed effects formulas and one list containing the random effects formulas. #' @@ -346,4 +349,3 @@ extract_id <- function(random, warn = TRUE) { unique(unlist(ids)) } - diff --git a/man/all_vars.Rd b/man/all_vars.Rd index 64328491..2787f6f1 100644 --- a/man/all_vars.Rd +++ b/man/all_vars.Rd @@ -2,8 +2,7 @@ % Please edit documentation in R/helpfunctions_formulas_general.R \name{all_vars} \alias{all_vars} -\title{Extract names of variables from a (list of) formula(s) -Version of \code{all.vars()} that can handle lists of formulas} +\title{Extract names of variables from a (list of) formula(s)} \usage{ all_vars(fmla) } @@ -11,7 +10,6 @@ all_vars(fmla) \item{fmla}{a formula or list of formulas} } \description{ -Extract names of variables from a (list of) formula(s) -Version of \code{all.vars()} that can handle lists of formulas +Version of \code{all.vars()} that can handle lists of formulas. } \keyword{internal} From fc013671d0fc057265dd5184be6596e7662d5c30 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:27:32 +0100 Subject: [PATCH 059/176] simplify helpfunction get_resp_mat() --- R/get_model_info.R | 9 +++++++-- R/helpfunctions_info_list.R | 23 +++++------------------ 2 files changed, 12 insertions(+), 20 deletions(-) diff --git a/R/get_model_info.R b/R/get_model_info.R index 226c1e90..c499ecc8 100644 --- a/R/get_model_info.R +++ b/R/get_model_info.R @@ -29,8 +29,13 @@ get_model1_info <- function(k, Mlist, par_index_main, par_index_other, } # response matrix and column(s) -------------------------------------------- - resp_mat <- get_resp_mat(resp = k, Mlvls = Mlist$Mlvls, - outnames = names(Mlist$outcomes$outcomes[[k]])) + resp_mat <- get_resp_mat( + resp = k, Mlvls = Mlist$Mlvls, + outnames = if (!is.null(Mlist$outcomes$outcomes[[k]])) { + names(Mlist$outcomes$outcomes[[k]]) + } else { + k + }) # resp_mat <- if (k %in% names(Mlist$Mlvls)) { # # if the variable is a column of one of the design matrices, use the level # # of that matrix diff --git a/R/helpfunctions_info_list.R b/R/helpfunctions_info_list.R index 1feb43b5..bc8728c4 100644 --- a/R/helpfunctions_info_list.R +++ b/R/helpfunctions_info_list.R @@ -11,26 +11,13 @@ #' @return character string; the name(s) of the data matrix/matrices of the #' response variable(s) #' -#' @noRd +#' @keywords internal get_resp_mat <- function(resp, Mlvls, outnames) { - if (resp %in% names(Mlvls)) { - # if the variable is a column of one of the design matrices, use the level - # of that matrix - Mlvls[resp] - } else if (grepl("^Surv\\(", resp)) { - # if the model is a survival model (variable name is the survival expression - # and not a single variable name) get the levels of the separate variables - # involved in the survival expression - if (all(outnames %in% names(Mlvls))) { - Mlvls[outnames] - } else { - errormsg("I have identified %s as a survival outcome, but I cannot find - some of its elements in any of the data matrices.", - dQuote(resp)) - } + if (any(!outnames %in% names(Mlvls))) { + errormsg("I cannot find the variable(s) %s in any of the data matrices.", + paste_and(dQuote(outnames[!outnames %in% names(Mlvls)]))) } else { - errormsg("I cannot find the variable %s in any of the data matrices.", - dQuote(resp)) + Mlvls[outnames] } } From b5f6dd00c2c1188df4c93315eeb9c7a6f1ce65a7 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:28:18 +0100 Subject: [PATCH 060/176] update tests for extract_id() and add tests for all_vars() --- .../test-helpfunctions_formulas_general.R | 87 +++++++++---------- 1 file changed, 43 insertions(+), 44 deletions(-) diff --git a/tests/testthat/test-helpfunctions_formulas_general.R b/tests/testthat/test-helpfunctions_formulas_general.R index caf23320..fcef53d2 100644 --- a/tests/testthat/test-helpfunctions_formulas_general.R +++ b/tests/testthat/test-helpfunctions_formulas_general.R @@ -267,63 +267,62 @@ test_that('split_formula_list works', { # extract_id-------------------------------------------------------------- -runs <- list(list(random = ~ 1 | id, ids = 'id', RHS = list(~ 1 | id), - nogroup = list(id = ~ 1)), - list(random = ~ 0 | id, ids = 'id', RHS = list(~ 0 | id), - nogroup = list(id = ~ 0)), - list(random = NULL, ids = NULL, RHS = NULL, nogroup = NULL), - list(random = y ~ a + b + c, ids = NULL, RHS = list(~a + b + c), - nogroup = list(y ~ a + b + c)), - list(random = y ~ time | id, ids = 'id', RHS = list(~time | id), - nogroup = list(id = y ~ time)), - list(random = y ~ 0, ids = NULL, RHS = list(~ 0), - nogroup = list(y ~ 0)) -) test_that('extract_id works', { - for (i in setdiff(seq_along(runs), c(4, 6))) { - expect_equal(extract_id(runs[[i]]$random), runs[[i]]$ids) - } + # single formula + expect_equal(extract_id(~ 1 | id), "id") + expect_equal(extract_id(~ 0 | id), "id") + expect_equal(extract_id(~ time | id), "id") + expect_equal(extract_id(~ 1 | id/center), c("id", "center")) + expect_equal(extract_id(~ 1 | id + center), c("id", "center")) + expect_equal(extract_id(~ (1 | id) + (time | center)), c("id", "center")) + + + expect_null(extract_id(NULL)) + expect_null(extract_id(~ a + b, warn = FALSE), NULL) + + + # list of formulas + expect_equal(extract_id(list(a = ~ time | id, + b = y ~ (time | id) + (1 | center), + d = NULL, + e = ~ 1 | group)), + c("id", "center", "group")) + - # test all together - expect_equal(extract_id(lapply(runs, "[[", 'random')), - unlist(unique(lapply(runs, "[[", 'ids')))) }) test_that('extract_id gives warning', { - for (i in c(4, 6)) { - expect_warning(extract_id(runs[[i]]$random), runs[[i]]$ids) - } - - # test all together - expect_equal(extract_id(lapply(runs, "[[", 'random')), - unlist(unique(lapply(runs, "[[", 'ids')))) + expect_warning(extract_id(~ a + b + c)) + expect_warning(extract_id(~ 0)) + expect_warning(extract_id(~ 1)) }) -test_that('extract_id results in error', { - err <- list( - "text", - NA, - TRUE, - mean, - list(random = ~ a | id/class, ids = c('id', 'class')), - list(random = ~ a | id + class, ids = c('id', 'class')), - list(random = list(~a | id, ~ b | id2), ids = c('id', 'id2')) - ) - for (i in seq_along(err)) { - expect_error(extract_id(err[[i]])) - } +test_that('extract_id gives in error', { + expect_error(extract_id("~ 1 | id")) + expect_error(extract_id(NA)) }) -test_that('extract_id results in warning', { - rd_warn <- list(~1, - ~a + b + c) +# all_vars --------------------------------------------------------------------- +test_that("all_vars works", { + expect_null(all_vars(NULL)) - for (i in seq_along(rd_warn)) { - expect_warning(extract_id(rd_warn[[i]])) - } + expect_equal(all_vars(y ~ a + B + I(c/d^2) + ns(time, df = 3) + + (1 | id/center)), + c("y", "a", "B", "c", "d", "time", "id", "center")) + expect_equal(all_vars(list(y ~ a + B + I(c/d^2), + a ~ c + ns(time, df = 3) + (1 | id/center))), + c("y", "a", "B", "c", "d", "time", "id", "center")) +}) + +test_that("all_vars gives an error", { + expect_error(all_vars(NA)) + expect_error(all_vars(1)) + expect_error(all_vars(list(NULL, 1, "abc", ~ b + c))) + expect_error(all_vars(c("a", "b", "c"))) + expect_error(all_vars("abc")) }) From 53b2b6d2ebfdd20dcb94fdcb1c61b4ed48e511af Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:28:32 +0100 Subject: [PATCH 061/176] added internal documentation for get_resp_mat() --- man/get_resp_mat.Rd | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 man/get_resp_mat.Rd diff --git a/man/get_resp_mat.Rd b/man/get_resp_mat.Rd new file mode 100644 index 00000000..91144deb --- /dev/null +++ b/man/get_resp_mat.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_info_list.R +\name{get_resp_mat} +\alias{get_resp_mat} +\title{Identify the data matrix containing a given response variable} +\usage{ +get_resp_mat(resp, Mlvls, outnames) +} +\arguments{ +\item{resp}{character string; name of the response variable} + +\item{Mlvls}{named vector where the names are all column names of all data +matrices, and the values are the names of the corresponding +data matrices} + +\item{outnames}{character vector; names of the columns in the data matrices +that contain the response variable (or multiple columns in +case of a survival outcome)} +} +\value{ +character string; the name(s) of the data matrix/matrices of the +response variable(s) +} +\description{ +Identify the data matrix containing a given response variable +} +\keyword{internal} From e201ac11596114ffcfc32b06d09bb56421652b26 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:29:06 +0100 Subject: [PATCH 062/176] minor changes in internal documentation split_formula and split_formula list --- man/split_formula.Rd | 6 ++++-- man/split_formula_list.Rd | 8 +++++--- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/man/split_formula.Rd b/man/split_formula.Rd index 4b1b6d0d..86f052c8 100644 --- a/man/split_formula.Rd +++ b/man/split_formula.Rd @@ -2,8 +2,7 @@ % Please edit documentation in R/helpfunctions_formulas_general.R \name{split_formula} \alias{split_formula} -\title{Split a formula into fixed and random effects parts -Split a lme4 style formula into nlme style formulas.} +\title{Split a formula into fixed and random effects parts} \usage{ split_formula(formula) } @@ -11,6 +10,9 @@ split_formula(formula) \item{formula}{a \code{formula} object} } \description{ +Split a lme4 style formula into nlme style formulas. +} +\details{ Internal function, used in *_imp and help functions (2022-02-06) } \keyword{internal} diff --git a/man/split_formula_list.Rd b/man/split_formula_list.Rd index f3303f89..38c805d0 100644 --- a/man/split_formula_list.Rd +++ b/man/split_formula_list.Rd @@ -2,9 +2,7 @@ % Please edit documentation in R/helpfunctions_formulas_general.R \name{split_formula_list} \alias{split_formula_list} -\title{Split a list of formulas into fixed and random effects parts. -Calls \code{split_formula()} on each formula in a list to create one list of the -fixed effects formulas and one list containing the random effects formulas.} +\title{Split a list of formulas into fixed and random effects parts.} \usage{ split_formula_list(formulas) } @@ -12,6 +10,10 @@ split_formula_list(formulas) \item{formulas}{a \code{list} of \code{formula} objects} } \description{ +Calls \code{split_formula()} on each formula in a list to create one list of the +fixed effects formulas and one list containing the random effects formulas. +} +\details{ Internal function, used in *_imp() (2022-02-06) } \keyword{internal} From 4e5c5a50b9d8fd0f233689d8e27c9b601d9e606c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:30:14 +0100 Subject: [PATCH 063/176] renamed file --- ...helpfun_vcov.R => test-helpfunctions_vcov.R} | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) rename tests/testthat/{test-helpfun_vcov.R => test-helpfunctions_vcov.R} (94%) diff --git a/tests/testthat/test-helpfun_vcov.R b/tests/testthat/test-helpfunctions_vcov.R similarity index 94% rename from tests/testthat/test-helpfun_vcov.R rename to tests/testthat/test-helpfunctions_vcov.R index 98e520f5..faef4b40 100644 --- a/tests/testthat/test-helpfun_vcov.R +++ b/tests/testthat/test-helpfunctions_vcov.R @@ -1,4 +1,6 @@ -test_that('check_rd_vcov_list', { + +# check_rd_vcov_list ----------------------------------------------------------- +test_that("check_rd_vcov_list works", { # rd_vcov is a string expect_equal( check_rd_vcov_list(rd_vcov = "full", idvar = "id"), @@ -59,7 +61,18 @@ test_that('check_rd_vcov_list', { }) -test_that('expand_rd_vcov_full works', { + +test_that("check_rd_vcov_list returns error", { + expect_error(check_rd_vcov_list("abc")) + expect_error(check_rd_vcov_list(NULL)) + expect_error(check_rd_vcov_list(NULL, idvar = "id")) + expect_error(check_rd_vcov_list(NA)) + # expect_error(check_rd_vcov_list(list(NULL), idvar = "id")) +}) + + + +test_that("expand_rd_vcov_full works", { expect_equal( expand_rd_vcov_full(rd_vcov = "full", rd_outnam = list(id = c("a", "b"))), From d7be4141b2161cf72f4302e0a5b8fa6b6e8f6647 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:30:47 +0100 Subject: [PATCH 064/176] minor changes in get_nranef() --- R/helpfunctions_divide_matrices.R | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/R/helpfunctions_divide_matrices.R b/R/helpfunctions_divide_matrices.R index ea33f42c..09b49398 100644 --- a/R/helpfunctions_divide_matrices.R +++ b/R/helpfunctions_divide_matrices.R @@ -721,34 +721,46 @@ get_nonprop_lp <- function(nonprop, dsgn_mat_lvls, data, refs, fixed) { #' Extract the number of random effects -#' @param idvar vector of the names of id variables +#' @param idvar vector of the names of all id variables #' @param random a random effect formula or list of random effects formulas #' @param data a `data.frame` #' @return a list by grouping level (`idvar`) with a named vector of the number #' of random effects per variable (=names). #' @keywords internal +#' + get_nranef <- function(idvar, random, data) { + nlapply(idvar, function(lvl) { + if (inherits(random, "formula")) { + rm_gr <- remove_grouping(random) + if (lvl %in% names(rm_gr)) { ncol(model.matrix(remove_grouping(random)[[lvl]], data = data)) } else 0L + } else if (inherits(random, "list")) { + if (length(random) == 1L) { rm_gr <- remove_grouping(random) nrd <- if (lvl %in% names(rm_gr)) { - ncol(model.matrix(remove_grouping(random)[[lvl]], data = data)) + ncol(model.matrix(rm_gr(random)[[lvl]], data = data)) } else 0L + } else { + nrd <- ivapply(remove_grouping(random), function(x) { if (lvl %in% names(x)) { ncol(model.matrix(x[[lvl]], data = data)) } else {0L} }) } + names(nrd) <- names(random) nrd + } else { errormsg("I expected either a formula or list of formulas.") } From 01360f8d6293cf6d7c94a5528a05f2d19d344744 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:31:36 +0100 Subject: [PATCH 065/176] Improvements in readability and documentation of check_rd_vcov_list --- R/helpfunctions_vcov.R | 51 ++++++++++++++++++++++++++++++++---------- 1 file changed, 39 insertions(+), 12 deletions(-) diff --git a/R/helpfunctions_vcov.R b/R/helpfunctions_vcov.R index 9128a9f1..dc0603a6 100644 --- a/R/helpfunctions_vcov.R +++ b/R/helpfunctions_vcov.R @@ -1,29 +1,56 @@ #' First validation for rd_vcov #' -#' Check if rd_vcov is a list with elements for all grouping levels or does -#' not specify a grouping level. If valid, make sure it is a list per grouping +#' Checks if `rd_vcov` is a `list` with elements for all grouping levels or does +#' not specify a grouping level. +#' If valid, this function also make sure that `rd_vcov` is a list per grouping #' level by duplicating the contents if necessary. #' -#' @param rd_vcov the random effects variance covariance structure provided by -#' the user -#' @param idvar vector with the names of the grouping variables -#' (without "lvlone") +#' @param rd_vcov a character string or a list describing the the random effects +#' variance covariance structure (provided by the user) +#' @param idvar vector with the names of all grouping variables +#' (except "lvlone") +#' #' @keywords internal +#' #' @return A named list per grouping level where each elements contains #' information on how the random effects variance-covariance matrices on -#' that level are structured. Per level it can be either a character -#' string (e.g. `"full"`) or a list specifying structures per (groups) of +#' that level are structured. +#' Per level it can be either a character string (e.g. `"full"`) or a +#' list specifying structures per (groups) of #' variable(s) (e.g. `list(full = c("a", "b"), indep = "c")`) + + check_rd_vcov_list <- function(rd_vcov, idvar) { - if (!inherits(rd_vcov, "list") | all(!idvar %in% names(rd_vcov))) { + if (inherits(rd_vcov, "character")) { + if (!rd_vcov %in% c("blockdiag", "full", "indep")) { + errormsg("The variance-covariance matrix for the random effects of the + different models (supplied to the argument %s) can only be of + type \"blockdiag\", \"indep\", or \"full\". + To specify different structures for different models or levels, + provide a list (details see documentation).", dQuote("rd_vcov")) + } + nlapply(idvar, function(x) rd_vcov) - } else if (inherits(rd_vcov, "list") & any(!idvar %in% names(rd_vcov))) { - errormsg("Please provide information on the variance-covariance structure + + } else if (inherits(rd_vcov, "list")) { + + if (all(names(rd_vcov) %in% c("blockdiag", "full", "indep"))) { + nlapply(idvar, function(x) rd_vcov) + } else if (length(idvar) > 1L & any(!idvar %in% names(rd_vcov))) { + errormsg("Please provide information on the variance-covariance structure of the random effects for all levels.") + } else if (all(idvar %in% names(rd_vcov))) { + rd_vcov + } else { + errormsg("You provided %s in a way I didn't anticipate. Please contact + the package maintainer.", dQuote("rd_vcov")) + } + } else { - rd_vcov + errormsg("The argument %s should be specified as character string + or a list.", dQuote("rd_vcov")) } } From 475fc134217d4f5a619eba1f98c5c2121baeac76 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:32:22 +0100 Subject: [PATCH 066/176] minor improvments in documentation and error message expand_rd_vcov_full --- R/helpfunctions_vcov.R | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/R/helpfunctions_vcov.R b/R/helpfunctions_vcov.R index dc0603a6..49b608cd 100644 --- a/R/helpfunctions_vcov.R +++ b/R/helpfunctions_vcov.R @@ -55,6 +55,8 @@ check_rd_vcov_list <- function(rd_vcov, idvar) { } + + #' Expand rd_vcov using variable names in case "full" is used #' #' @@ -65,8 +67,8 @@ check_rd_vcov_list <- function(rd_vcov, idvar) { #' @keywords internal #' @return A named list per grouping level where each elements contains #' information on how the random effects variance-covariance matrices on -#' that level are structured. Per level there is a list of grouping structures -#' containing the names of variables in each structure +#' that level are structured. Per level there is a list of grouping +#' structures containing the names of variables in each structure #' (e.g. `list(full = c("a", "b"), indep = "c")`) expand_rd_vcov_full <- function(rd_vcov, rd_outnam) { @@ -75,7 +77,7 @@ expand_rd_vcov_full <- function(rd_vcov, rd_outnam) { rd_vcov <- check_rd_vcov_list(rd_vcov, idvar) nlapply(idvar, function(lvl) { - if (is.character(rd_vcov[[lvl]]) & length(rd_vcov[[lvl]]) == 1) { + if (is.character(rd_vcov[[lvl]]) & length(rd_vcov[[lvl]]) == 1L) { setNames(list(rd_outnam[[lvl]]), rd_vcov[[lvl]]) @@ -96,7 +98,7 @@ expand_rd_vcov_full <- function(rd_vcov, rd_outnam) { } else { errormsg("%s should be a character string or a list.", - dQuote("rd_vcov[[lvl]]")) + dQuote(paste0("rd_vcov[[\"", lvl, "\"]]"))) } }) } From b9c08c8db5f9e301fc64d476b4c1d80846887dff Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:33:59 +0100 Subject: [PATCH 067/176] moved function get_nranef from one file to another --- R/helpfunctions_divide_matrices.R | 52 ------------------------------- R/helpfunctions_vcov.R | 49 +++++++++++++++++++++++++++++ 2 files changed, 49 insertions(+), 52 deletions(-) diff --git a/R/helpfunctions_divide_matrices.R b/R/helpfunctions_divide_matrices.R index 09b49398..bc0819e1 100644 --- a/R/helpfunctions_divide_matrices.R +++ b/R/helpfunctions_divide_matrices.R @@ -715,55 +715,3 @@ get_nonprop_lp <- function(nonprop, dsgn_mat_lvls, data, refs, fixed) { }) } - - - - - -#' Extract the number of random effects -#' @param idvar vector of the names of all id variables -#' @param random a random effect formula or list of random effects formulas -#' @param data a `data.frame` -#' @return a list by grouping level (`idvar`) with a named vector of the number -#' of random effects per variable (=names). -#' @keywords internal -#' - -get_nranef <- function(idvar, random, data) { - - nlapply(idvar, function(lvl) { - - if (inherits(random, "formula")) { - - rm_gr <- remove_grouping(random) - - if (lvl %in% names(rm_gr)) { - ncol(model.matrix(remove_grouping(random)[[lvl]], data = data)) - } else 0L - - } else if (inherits(random, "list")) { - - if (length(random) == 1L) { - rm_gr <- remove_grouping(random) - nrd <- if (lvl %in% names(rm_gr)) { - ncol(model.matrix(rm_gr(random)[[lvl]], data = data)) - } else 0L - - } else { - - nrd <- ivapply(remove_grouping(random), function(x) { - if (lvl %in% names(x)) { - ncol(model.matrix(x[[lvl]], data = data)) - } else {0L} - }) - } - - names(nrd) <- names(random) - nrd - - } else { - errormsg("I expected either a formula or list of formulas.") - } - }) -} - diff --git a/R/helpfunctions_vcov.R b/R/helpfunctions_vcov.R index 49b608cd..4d012374 100644 --- a/R/helpfunctions_vcov.R +++ b/R/helpfunctions_vcov.R @@ -177,3 +177,52 @@ check_rd_vcov <- function(rd_vcov, nranef) { } rd_vcov } + + + +#' Extract the number of random effects +#' @param idvar vector of the names of all id variables +#' @param random a random effect formula or list of random effects formulas +#' @param data a `data.frame` +#' @return a list by grouping level (`idvar`) with a named vector of the number +#' of random effects per variable (=names). +#' @keywords internal +#' + +get_nranef <- function(idvar, random, data) { + + nlapply(idvar, function(lvl) { + + if (inherits(random, "formula")) { + + rm_gr <- remove_grouping(random) + + if (lvl %in% names(rm_gr)) { + ncol(model.matrix(remove_grouping(random)[[lvl]], data = data)) + } else 0L + + } else if (inherits(random, "list")) { + + if (length(random) == 1L) { + rm_gr <- remove_grouping(random) + nrd <- if (lvl %in% names(rm_gr)) { + ncol(model.matrix(rm_gr(random)[[lvl]], data = data)) + } else 0L + + } else { + + nrd <- ivapply(remove_grouping(random), function(x) { + if (lvl %in% names(x)) { + ncol(model.matrix(x[[lvl]], data = data)) + } else {0L} + }) + } + + names(nrd) <- names(random) + nrd + + } else { + errormsg("I expected either a formula or list of formulas.") + } + }) +} From a94315d9cbe0a4090604af8c45ec5c356bcac1f5 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:47:52 +0100 Subject: [PATCH 068/176] update documentation --- man/check_full_blockdiag.Rd | 2 +- man/check_rd_vcov.Rd | 2 +- man/check_rd_vcov_list.Rd | 20 +++++++++++--------- man/expand_rd_vcov_full.Rd | 6 +++--- man/get_nranef.Rd | 4 ++-- 5 files changed, 18 insertions(+), 16 deletions(-) diff --git a/man/check_full_blockdiag.Rd b/man/check_full_blockdiag.Rd index 208591c3..fa524dad 100644 --- a/man/check_full_blockdiag.Rd +++ b/man/check_full_blockdiag.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/helpfunctions_divide_matrices.R +% Please edit documentation in R/helpfunctions_vcov.R \name{check_full_blockdiag} \alias{check_full_blockdiag} \title{Replace a full with a block-diagonal variance covariance matrix diff --git a/man/check_rd_vcov.Rd b/man/check_rd_vcov.Rd index b4ad4c13..9e532cb7 100644 --- a/man/check_rd_vcov.Rd +++ b/man/check_rd_vcov.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/helpfunctions_divide_matrices.R +% Please edit documentation in R/helpfunctions_vcov.R \name{check_rd_vcov} \alias{check_rd_vcov} \title{Check / create the random effects variance-covariance matrix specification} diff --git a/man/check_rd_vcov_list.Rd b/man/check_rd_vcov_list.Rd index 0244fa18..7c8bd9bc 100644 --- a/man/check_rd_vcov_list.Rd +++ b/man/check_rd_vcov_list.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/helpfunctions_divide_matrices.R +% Please edit documentation in R/helpfunctions_vcov.R \name{check_rd_vcov_list} \alias{check_rd_vcov_list} \title{First validation for rd_vcov} @@ -7,22 +7,24 @@ check_rd_vcov_list(rd_vcov, idvar) } \arguments{ -\item{rd_vcov}{the random effects variance covariance structure provided by -the user} +\item{rd_vcov}{a character string or a list describing the the random effects +variance covariance structure (provided by the user)} -\item{idvar}{vector with the names of the grouping variables -(without "lvlone")} +\item{idvar}{vector with the names of all grouping variables +(except "lvlone")} } \value{ A named list per grouping level where each elements contains information on how the random effects variance-covariance matrices on -that level are structured. Per level it can be either a character -string (e.g. \code{"full"}) or a list specifying structures per (groups) of +that level are structured. +Per level it can be either a character string (e.g. \code{"full"}) or a +list specifying structures per (groups) of variable(s) (e.g. \code{list(full = c("a", "b"), indep = "c")}) } \description{ -Check if rd_vcov is a list with elements for all grouping levels or does -not specify a grouping level. If valid, make sure it is a list per grouping +Checks if \code{rd_vcov} is a \code{list} with elements for all grouping levels or does +not specify a grouping level. +If valid, this function also make sure that \code{rd_vcov} is a list per grouping level by duplicating the contents if necessary. } \keyword{internal} diff --git a/man/expand_rd_vcov_full.Rd b/man/expand_rd_vcov_full.Rd index 7e65fdfd..059ca8a4 100644 --- a/man/expand_rd_vcov_full.Rd +++ b/man/expand_rd_vcov_full.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/helpfunctions_divide_matrices.R +% Please edit documentation in R/helpfunctions_vcov.R \name{expand_rd_vcov_full} \alias{expand_rd_vcov_full} \title{Expand rd_vcov using variable names in case "full" is used} @@ -16,8 +16,8 @@ that have random effects on this level} \value{ A named list per grouping level where each elements contains information on how the random effects variance-covariance matrices on -that level are structured. Per level there is a list of grouping structures -containing the names of variables in each structure +that level are structured. Per level there is a list of grouping +structures containing the names of variables in each structure (e.g. \code{list(full = c("a", "b"), indep = "c")}) } \description{ diff --git a/man/get_nranef.Rd b/man/get_nranef.Rd index f7298710..4a518fef 100644 --- a/man/get_nranef.Rd +++ b/man/get_nranef.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/helpfunctions_divide_matrices.R +% Please edit documentation in R/helpfunctions_vcov.R \name{get_nranef} \alias{get_nranef} \title{Extract the number of random effects} @@ -7,7 +7,7 @@ get_nranef(idvar, random, data) } \arguments{ -\item{idvar}{vector of the names of id variables} +\item{idvar}{vector of the names of all id variables} \item{random}{a random effect formula or list of random effects formulas} From b1271c72fefb60a2a5472f5ebb76e41b5676616c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:48:10 +0100 Subject: [PATCH 069/176] minor change in test naming --- tests/testthat/test-helpfunctions_vcov.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test-helpfunctions_vcov.R b/tests/testthat/test-helpfunctions_vcov.R index faef4b40..e036610c 100644 --- a/tests/testthat/test-helpfunctions_vcov.R +++ b/tests/testthat/test-helpfunctions_vcov.R @@ -227,8 +227,8 @@ test_that("check_rd_vcov", { }) - -test_that("get_nranef", { +# get_nranef ------------------------------------------------------------------- +test_that("get_nranef works", { expect_equal( get_nranef(idvar = "id", random = ~ 1 | id, data = longDF), From 9ac1cc98016496f9daeaf318a27038874e9d0bb3 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:48:16 +0100 Subject: [PATCH 070/176] minor change --- R/helpfunctions_vcov.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/helpfunctions_vcov.R b/R/helpfunctions_vcov.R index 4d012374..365be522 100644 --- a/R/helpfunctions_vcov.R +++ b/R/helpfunctions_vcov.R @@ -214,7 +214,7 @@ get_nranef <- function(idvar, random, data) { nrd <- ivapply(remove_grouping(random), function(x) { if (lvl %in% names(x)) { ncol(model.matrix(x[[lvl]], data = data)) - } else {0L} + } else 0L }) } From 4d42c3db98d804acaea04be9b2f2c00b6ab3bab7 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 15:55:49 +0100 Subject: [PATCH 071/176] usethis changes to Rproj --- .Rbuildignore | 1 + JointAI.Rproj | 6 +++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index 2a27ed37..b1257db1 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -44,3 +44,4 @@ sim_mldata.R ^\.covrignore$ ^\.github$ ^codecov\.yml$ +^JointAI\.Rproj$ diff --git a/JointAI.Rproj b/JointAI.Rproj index 2439e3da..766b3b21 100644 --- a/JointAI.Rproj +++ b/JointAI.Rproj @@ -1,7 +1,7 @@ Version: 1.0 -RestoreWorkspace: Default -SaveWorkspace: Default +RestoreWorkspace: No +SaveWorkspace: No AlwaysSaveHistory: Default EnableCodeIndexing: Yes @@ -14,9 +14,9 @@ LaTeX: pdfLaTeX AutoAppendNewline: Yes StripTrailingWhitespace: Yes +LineEndingConversion: Posix BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source -PackageCheckArgs: --as-cran --no-stop-on-test-error PackageRoxygenize: rd,collate,namespace From 6691f7415795778b3b2539cb5c3357517f9e6872 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 6 Feb 2022 16:05:25 +0100 Subject: [PATCH 072/176] bugfix --- R/helpfunctions_vcov.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/helpfunctions_vcov.R b/R/helpfunctions_vcov.R index 365be522..9e8872a3 100644 --- a/R/helpfunctions_vcov.R +++ b/R/helpfunctions_vcov.R @@ -206,7 +206,7 @@ get_nranef <- function(idvar, random, data) { if (length(random) == 1L) { rm_gr <- remove_grouping(random) nrd <- if (lvl %in% names(rm_gr)) { - ncol(model.matrix(rm_gr(random)[[lvl]], data = data)) + ncol(model.matrix(rm_gr[[lvl]], data = data)) } else 0L } else { From 06f28dbf7302fcd4bdd4d4ef6e5ee95efef250fa Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:18:15 +0100 Subject: [PATCH 073/176] ignore sh stackdump --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 11d980e1..6b95b451 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,4 @@ docs tests/testthat/*.pdf bash.exe.stackdump +sh.exe.stackdump From d9d456ae052292733d9cb8c928252257c6eb3d54 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:26:26 +0100 Subject: [PATCH 074/176] use parallel computing with only package future (no longer need doFuture and foreach) This also avoids the duplicate use of function name "run_jags", and avoids having to use different functions for running the original model and running the samples when using add_samples(). also: no need to "change" future to detect whether parallel computing is used (this is part of the result of the output of future) Time is now recorded separately for the adaptive phase and sampling phase. --- DESCRIPTION | 5 +- R/JointAI.R | 1 - R/add_samples.R | 40 ++++----- R/helpfunctions_JAGS.R | 196 ++++++++++++++++++++++------------------- R/model_imp.R | 28 +++--- 5 files changed, 138 insertions(+), 132 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index e8a46205..11c4a5a4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -19,7 +19,7 @@ BugReports: https://github.com/nerler/JointAI/issues/ LazyData: TRUE RoxygenNote: 7.1.2 Roxygen: list(old_usage = TRUE, markdown = TRUE) -Imports: rjags, mcmcse, coda, rlang, future, foreach, mathjaxr, survival, MASS +Imports: rjags, mcmcse, coda, rlang, future, mathjaxr, survival, MASS SystemRequirements: JAGS (https://mcmc-jags.sourceforge.io/) Suggests: knitr, @@ -29,8 +29,7 @@ Suggests: ggplot2, ggpubr, testthat, - covr, - doFuture + covr VignetteBuilder: knitr Encoding: UTF-8 RdMacros: mathjaxr diff --git a/R/JointAI.R b/R/JointAI.R index 0844287f..1a28c1ca 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -155,7 +155,6 @@ #' @importFrom rjags coda.samples jags.model #' @import future #' @import mathjaxr -#' @importFrom foreach foreach %dopar% #' @importFrom splines bs ns #' #' @docType package diff --git a/R/add_samples.R b/R/add_samples.R index a9032b1d..8ecc3c1e 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -62,27 +62,19 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, monitor_params = monitor_params, add = add, mess = mess) - future_info <- get_future_info() # run mcmc ---------------------------------------------------------------- - t0 <- Sys.time() - if (future_info$parallel) { - if (mess) - msg("Parallel sampling with %s workers started (%s).", - eval(future_info$workers), Sys.time()) - - res <- foreach::`%dopar%`(foreach::foreach(i = seq_along(object$model)), - run_samples(object$model[[i]], n_iter = n.iter, - thin = thin, var_names = var_names) - ) - mcmc <- coda::as.mcmc.list(lapply(res, function(x) x$mcmc[[1]])) - adapt <- lapply(res, function(x) x$adapt) - } else { - mcmc <- rjags::coda.samples(object$model, variable.names = var_names, - n.iter = n.iter, thin = thin, - progress.bar = progress.bar) - } - t1 <- Sys.time() + + jags_res <- run_parallel(n_adapt = NULL, n_iter = n.iter, + n_chains = object$mcmc_settings$n.chains, + inits = NULL, thin = thin, + data_list = NULL, var_names = var_names, + modelfile = NULL, quiet = TRUE, + progress_bar = progress.bar, mess = mess, + warn = TRUE, add_samples = TRUE, + models = object$model) + adapt <- jags_res$adapt + mcmc <- jags_res$mcmc # process MCMC samples -------------------------------------------------------- @@ -138,9 +130,9 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, newobject$MCMC <- newMCMC newobject$call <- c(object$call, match.call()) newobject$mcmc_settings$variable.names <- var_names - newobject$comp_info$future <- c(object$comp_info$future, - future_info$call) - newobject$model <- if (future_info$parallel) { + newobject$comp_info$future <- c(object$comp_info$parallel, + jags_res$parallel) + newobject$model <- if (isTRUE(jags_res$parallel)) { adapt } else { object$model @@ -153,7 +145,9 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, coda::thin(newMCMC)) # add computational time to JointAI object - newobject$comp_info$duration <- c(object$comp_info$duration, difftime(t1, t0)) + newobject$comp_info$duration <- c(object$comp_info$duration, + list("adapt" = jags_res$time_adapt, + "sample" = jags_res$time_sample)) return(newobject) } diff --git a/R/helpfunctions_JAGS.R b/R/helpfunctions_JAGS.R index 6dae790a..8b2b76aa 100644 --- a/R/helpfunctions_JAGS.R +++ b/R/helpfunctions_JAGS.R @@ -33,78 +33,18 @@ get_rng <- function(seed, n_chains) { # functions for parallel computation ------------------------------------------- -run_jags <- function(i, data_list, modelfile, n_adapt, n_iter, var_names, - thin) { - adapt <- rjags::jags.model( - file = modelfile, - n.adapt = n_adapt, - n.chains = 1L, - inits = i, - data = data_list, - quiet = TRUE - ) - - mcmc <- rjags::coda.samples(adapt, - n.iter = n_iter, - variable.names = var_names, - thin = thin, progress.bar = "none" - ) - - list(adapt = adapt, mcmc = mcmc) -} - - - -run_samples <- function(adapt, n_iter, var_names, thin) { - sink(tempfile()) - adapt$recompile() - sink() - - mcmc <- rjags::coda.samples(adapt, - n.iter = n_iter, - variable.names = var_names, - progress.bar = "none", thin = thin - ) - - list(adapt = adapt, mcmc = mcmc) -} - - - - -run_parallel <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, - data_list, var_names, modelfile, mess = TRUE, - n_workers, ...) { - - if (any(n_adapt > 0L, n_iter > 0L)) { - - if (mess) - msg("Parallel sampling with %s workers started (%s).", - eval(n_workers), Sys.time()) - - res <- foreach::`%dopar%`(foreach::foreach(i = seq_along(inits)), - run_jags(inits[[i]], data_list = data_list, - modelfile = modelfile, - n_adapt = n_adapt, n_iter = n_iter, - thin = thin, - var_names = var_names) - ) - - mcmc <- coda::as.mcmc.list(lapply(res, function(x) x$mcmc[[1L]])) - adapt <- lapply(res, function(x) x$adapt) - - list(adapt = adapt, mcmc = mcmc) - } -} - - - -run_seq <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, - data_list, var_names, modelfile, quiet = TRUE, - progress_bar = "text", mess = TRUE, warn = TRUE, ...) { - - adapt <- if (any(n_adapt > 0L, n_iter > 0L)) { - if (warn == FALSE) { +run_jags <- function(inits, data_list, modelfile, n_chains, n_adapt, n_iter, + var_names, thin, quiet, warn, mess, progress_bar, + add_samples = FALSE, adapt = NULL) { + + + t0 <- Sys.time() + if (isTRUE(add_samples)) { + sink(tempfile()) + adapt$recompile() + sink() + } else { + adapt <- if (isFALSE(warn)) { suppressWarnings({ try(rjags::jags.model(file = modelfile, data = data_list, inits = inits, quiet = quiet, @@ -116,10 +56,14 @@ run_seq <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, n.chains = n_chains, n.adapt = n_adapt)) } } + + t1 <- Sys.time() + mcmc <- if (n_iter > 0L & !inherits(adapt, "try-error")) { - if (mess == FALSE) { + if (isFALSE(mess)) { sink(tempfile()) on.exit(sink()) + force(suppressMessages( try(rjags::coda.samples(adapt, n.iter = n_iter, thin = thin, variable.names = var_names, @@ -132,29 +76,101 @@ run_seq <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, } } + t2 <- Sys.time() - list(adapt = adapt, mcmc = mcmc) + list(adapt = adapt, mcmc = mcmc, time_adapt = t1 - t0, time_sample = t2 - t1) } -get_future_info <- function() { - oplan <- future::plan(future::sequential) - theplan <- attr(oplan[[1L]], "call") - future::plan(oplan) +# +# run_samples <- function(adapt, n_iter, var_names, thin) { +# sink(tempfile()) +# adapt$recompile() +# sink() +# +# mcmc <- rjags::coda.samples(adapt, +# n.iter = n_iter, +# variable.names = var_names, +# progress.bar = "none", thin = thin +# ) +# +# list(adapt = adapt, mcmc = mcmc) +# } + + + + +run_parallel <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, + data_list, var_names, modelfile, progress_bar, + quiet = TRUE, mess = TRUE, warn = TRUE, + add_samples = FALSE, models = NULL, ...) { + + if (any(n_adapt > 0L, n_iter > 0L)) { + + f <- future::future({}) + parallel <- f$asynchronous + + fit <- if (isTRUE(parallel) | + (isTRUE(add_samples) & inherits(models, "list"))) { + + if (isTRUE(mess) & isTRUE(parallel)) + msg("Parallel sampling with %s workers started (%s).", + length(f$workers), Sys.time()) + + if (isTRUE(mess) & !isTRUE(parallel)) + msg("Note: the original model was run in parallel.") + + if (isTRUE(parallel) & isTRUE(add_samples) & inherits(models, "jags")) + errormsg("It is not possible to run %s in parallel when the input + %s object was run squentially.", dQuote("add_samples()"), + dQuote("JointAI")) + + out <- lapply(seq_len(n_chains), function(i) { + future::future({ + run_jags(inits = inits[[i]], data_list = data_list, + modelfile = modelfile, + n_chains = 1L, + n_adapt = n_adapt, n_iter = n_iter, + thin = thin, + var_names = var_names, quiet = quiet, warn = warn, + mess = mess, progress_bar = progress_bar, + add_samples = add_samples, adapt = models[[i]]) + }) + }) + + res <- lapply(out, future::value) + + mcmc <- coda::as.mcmc.list(lapply(res, function(x) x$mcmc[[1L]])) + time_adapt <- do.call(c, lapply(res, "[[", "time_sample")) + time_sample <- do.call(c, lapply(res, "[[", "time_sample")) - strategies <- vapply(oplan, function(o) { - setdiff(class(o), c("tweaked", "function"))[1L] - }, FUN.VALUE = character(1L)) + list(adapt = lapply(res, "[[", "adapt"), + mcmc = mcmc, + time_adapt = reformat_difftime(time_adapt), + time_sample = reformat_difftime(time_sample)) - if (length(strategies) > 1L) { - warnmsg("There is a list of future strategies. - I will use the first element, %s.", - strategies[1L]) + } else { + + run_jags(inits = inits, data_list = data_list, + modelfile = modelfile, + n_chains = n_chains, + n_adapt = n_adapt, n_iter = n_iter, + thin = thin, + var_names = var_names, quiet = quiet, warn = warn, + mess = mess, progress_bar = progress_bar, + add_samples = add_samples, adapt = models) + } + fit$parallel <- parallel + fit$workers <- length(f$workers) + fit } +} + - list(strategy = strategies[1L], - parallel = !strategies[1L] %in% c("sequential", "transparent"), - workers = formals(oplan[[1L]])$workers, - call = theplan - ) +reformat_difftime <- function(dt) { + units(dt) <- "secs" + w <- which(min(dt)/c(secs = 1, mins = 60, hours = 3600, days = 86400) > 1L) + if (any(w)) + units(dt) <- names(w)[length(w)] + dt } diff --git a/R/model_imp.R b/R/model_imp.R index e7fed3a4..07f96c93 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -818,21 +818,15 @@ model_imp <- function(formula = NULL, fixed = NULL, data, random = NULL, msg("Note: No MCMC sample will be created when n.iter is set to 0.") } - future_info <- get_future_info() - - run_jags <- ifelse(future_info$parallel, run_parallel, run_seq) - - t0 <- Sys.time() - jags_res <- run_jags(n_adapt = n.adapt, n_iter = n.iter, n_chains = n.chains, - inits = inits, thin = thin, - n_workers = future_info$workers, - data_list = data_list, var_names = var_names, - modelfile = modelfile, quiet = quiet, - progress_bar = progress.bar, mess = mess, warn = warn) + jags_res <- run_parallel(n_adapt = n.adapt, n_iter = n.iter, + n_chains = n.chains, inits = inits, thin = thin, + data_list = data_list, var_names = var_names, + modelfile = modelfile, quiet = quiet, + progress_bar = progress.bar, mess = mess, + warn = warn) adapt <- jags_res$adapt mcmc <- jags_res$mcmc - t1 <- Sys.time() if (n.iter > 0 & class(mcmc) != "mcmc.list") warnmsg("There is no mcmc sample. Something went wrong.") @@ -904,10 +898,14 @@ model_imp <- function(formula = NULL, fixed = NULL, data, random = NULL, model = if (n.adapt > 0) adapt, sample = if (n.iter > 0 & !is.null(mcmc) & keep_scaled_mcmc) mcmc, MCMC = if (n.iter > 0 & !is.null(mcmc)) coda::as.mcmc.list(MCMC), - comp_info = list(start_time = t0, - duration = t1 - t0, + comp_info = list(start_time = Sys.time(), + duration = if (!is.null(jags_res)) + list("adapt" = jags_res$time_adapt, + "sample" = jags_res$time_sample), JointAI_version = packageVersion("JointAI"), - future = future_info$call), + parallel = if (!is.null(jags_res)) jags_res$parallel, + workers = if (isTRUE(jags_res$parallel)) + jags_res$workers), call = modimpcall$thecall ), class = "JointAI") From 2fce6284298014d127eaa90da7ae42b3e669d7c0 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:26:44 +0100 Subject: [PATCH 075/176] move re-setting of seed --- R/helpfunctions_model_imp.R | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/R/helpfunctions_model_imp.R b/R/helpfunctions_model_imp.R index e7a181d6..b587836f 100644 --- a/R/helpfunctions_model_imp.R +++ b/R/helpfunctions_model_imp.R @@ -54,12 +54,6 @@ make_filename <- function(modeldir, modelname, keep_model, overwrite, mess) { get_initial_values <- function(inits, seed, n_chains, warn) { # check if initial values are supplied or should be generated - - oldseed <- .Random.seed - on.exit({ - .Random.seed <<- oldseed - }) - if (is.null(inits)) { inits <- get_rng(seed, n_chains) @@ -77,6 +71,13 @@ get_initial_values <- function(inits, seed, n_chains, warn) { if (inherits(inits, "function")) { # if the initial values are supplied as a function, evaluate the # function + + oldseed <- .Random.seed + on.exit({ + .Random.seed <<- oldseed + }) + + if (!is.null(seed)) { set_seed(seed) } From 5e71629343962c233ce4406e8381d3a311d23387 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:27:03 +0100 Subject: [PATCH 076/176] fix typo --- R/helpfunctions_JAGSmodel.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 7aaace7e..6d9bbd9a 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -34,7 +34,7 @@ paste_data <- function(matnam, index, col, isgk = FALSE) { # - matnam: the name of the design matrix # - index: the index to be used, e.g. "i" or "ii" # - col: the column (or vector of columns) of the design matrix - # - isgk: is this whithin the Gauss-Kronrod quadrature? + # - isgk: is this within the Gauss-Kronrod quadrature? paste0(matnam, if (isgk) {"gk"} else {""}, "[", index, ", ", col, From b23280826152c537ede9d861be0295a382efdd4b Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:28:12 +0100 Subject: [PATCH 077/176] bugfix in jm with ordinal longitudinal outcome: need to use etagk in quadrature procedure instead of eta --- R/helpfunctions_JAGSmodel.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 6d9bbd9a..7d839cd3 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -1170,7 +1170,8 @@ write_logits <- function(info, index, nonprop = FALSE, isgk = FALSE, .isgk = isgk) { paste0(tab(indent), "logit(", paste_ps(k), ") <- gamma_", info$varname, "[", k, "]", - " + eta_", info$varname, "[", index, "]", + " + eta", if (isgk) "gk", + "_", info$varname, "[", index, if (isgk) ", k", "]", if (nonprop) { paste0(" + eta_", info$varname, "_", k, "[", index, "]") From 5da849cf804ef638f23ddf94c9304f40d20eeec2 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:29:43 +0100 Subject: [PATCH 078/176] started working on functionality to avoid loop over quadrature points; but this needs more work on the existing functions to handle different link functions etc. --- R/helpfunctions_JAGSmodel.R | 69 ++++++++++++++++++++++++++++++++++++- 1 file changed, 68 insertions(+), 1 deletion(-) diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 7d839cd3..755129b5 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -1136,7 +1136,13 @@ write_probs <- function(info, index, isgk = FALSE, indent = 4L) { }) ) }) - + # if (isTRUE(isgk)) { + # paste0(c( + # write_pgk(info, index, indent), "", + # write_psum_expit(info, index, indent) + # ), collapse = "\n") + # + # } else { paste0(tab(indent), paste_p(1L), " <- ", @@ -1179,8 +1185,69 @@ write_logits <- function(info, index, nonprop = FALSE, isgk = FALSE, }) paste0(logits, collapse = "\n") + # if (isTRUE(isgk)) { + # paste0(write_exp_lp(info, index = index, indent = indent, nonprop = nonprop), + # collapse = "\n") + # + # } else { + } +# write the part that calculates the exponential function of the linear predictor +# in the quadrature part for ordinal longitudinal outcomes in joint models +write_exp_lp <- function(info, index, nonprop = FALSE, indent = 4L) { + paste0(tab(indent), + "exp_lp_", info$varname, "[", index, ", ", 1L:(info$ncat - 1L), + ", 1:15] <- exp(1)^(gamma_", info$varname, "[", 1L:(info$ncat - 1L), "]", + " + etagk", + "_", info$varname, "[", index, ", 1:15", "]", + if (nonprop) { + paste0(" + eta_", info$varname, "_", 1L:(info$ncat - 1L), + "[", index, "]") + }, ")") +} + + +write_psum_expit <- function(info, index, indent = 4L) { + cvapply(1:(info$ncat - 1), function(r) { + paste0(tab(indent), + "psumgk_", info$varname, "[", index, ", ", r, + ", 1:15] <- exp_lp_", info$varname, "[", index, ", ", r, + ", ]/(1 + exp_lp_", info$varname, "[", index, ", ", r, ", ])", + if (r < info$ncat - 1) { + paste0(" - psumgk_", info$varname, "[", index, ", ", r + 1, ", 1:15]") + } + ) + }) +} + + +write_pgk <- function(info, index, indent = 4L) { + c( + paste0(tab(indent), + "pgk_", info$varname, "[", index, ", 1, 1:15] <- 1 - ifelse(", + "probsumgk_", info$varname, "[", index, ", ] > 1 - 1e-10, 1-1e-10, ", "\n", + tab(indent + 4 + nchar(info$varname) + nchar(index) + 15), + "ifelse(probsumgk_", info$varname, "[", index, ", ] < 1e-10, 1e-10, ", + "probsumgk_", info$varname, "[", index, ", ]))" + ), + + paste0(tab(indent), + "pgk_", info$varname, "[", index, ", 2:", info$ncat, ", 1:15] <- ifelse(", + "psumgk_", info$varname, "[", index, ", , ] > 1 - 1e-10, 1-1e-10, ", "\n", + tab(indent + 4 + nchar(info$varname) + nchar(index) + 17), + "ifelse(psumgk_", info$varname, "[", index, ", , ] < 1e-10, 1e-10, ", + "psumgk_", info$varname, "[", index, ", , ]))" + ), + + paste0("\n", tab(indent), + "probsumgk_", info$varname, "[", index, ", 1:15] <- ", + paste0("pgk_", info$varname, "[", index, ", ", 2:info$ncat, ", ]", + collapse = " + ") + )) +} + + write_priors_clm <- function(info) { From 4d1e270575f601fa76b9657ed3286bc0d0779c0e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:30:25 +0100 Subject: [PATCH 079/176] indentation in syntax --- R/JAGSmodel_clmm.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/JAGSmodel_clmm.R b/R/JAGSmodel_clmm.R index c58de5be..1fce64cc 100644 --- a/R/JAGSmodel_clmm.R +++ b/R/JAGSmodel_clmm.R @@ -34,9 +34,9 @@ jagsmodel_clmm <- function(info) { nonprop <- lapply(write_nonprop(info), add_linebreaks, indent = indent + 2) paste0("\n\n", - paste0(tab(4), "eta_", info$varname, "_", seq_along(nonprop), - "[", index, "] <- ", nonprop, collapse = "\n") - )} + paste0(tab(4), "eta_", info$varname, "_", seq_along(nonprop), + "[", index, "] <- ", nonprop, collapse = "\n") + )} # syntax to set values of dummy variables, From df5746b6990ec4a49d2f794c69f4d4bae6114bff Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:31:36 +0100 Subject: [PATCH 080/176] get association structure info into sub-list for longitudinal outcomes in jm --- R/get_model_info.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/R/get_model_info.R b/R/get_model_info.R index c499ecc8..e4e5091f 100644 --- a/R/get_model_info.R +++ b/R/get_model_info.R @@ -225,6 +225,8 @@ get_model1_info <- function(k, Mlist, par_index_main, par_index_other, Mlist$models, assoc_type, Mlist$refs) } else if (modeltype %in% "coxph") { "obs.value" + } else if (isTRUE(isgk)) { + get_assoc_type(k, Mlist$models, assoc_type, Mlist$refs) } # collect all info --------------------------------------------------------- From f20fb146995a902e5a7148b3bde1bd05af2c5267 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:47:15 +0100 Subject: [PATCH 081/176] update documentation wrt different use of parallel computation --- NAMESPACE | 2 -- R/JointAI.R | 2 +- R/model_imp.R | 1 - 3 files changed, 1 insertion(+), 4 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 82349dd1..e3651e51 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -68,8 +68,6 @@ import(graphics) import(mathjaxr) import(stats) import(utils) -importFrom(foreach,"%dopar%") -importFrom(foreach,foreach) importFrom(rjags,coda.samples) importFrom(rjags,jags.model) importFrom(rlang,.data) diff --git a/R/JointAI.R b/R/JointAI.R index 1a28c1ca..8ea82ce5 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -46,7 +46,7 @@ #' \href{https://CRAN.R-project.org/package=survival}{\strong{survival}}). #' #' Computations can be performed in parallel to reduce computational time, -#' using the packages \pkg{future} (and \pkg{doFuture}), +#' using the package \pkg{future}, #' the argument \code{shrinkage} allows the user to impose a penalty on the #' regression coefficients of some or all models involved, #' and hyper-parameters can be changed via the argument \code{hyperpars}. diff --git a/R/model_imp.R b/R/model_imp.R index 07f96c93..b3b3264c 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -686,7 +686,6 @@ #' # MCMC chains are run sequentially. #' # To run MCMC chains in parallel, a strategy can be specified using the #' # package \pkg{future} (see ?future::plan), for example: -#' doFuture::registerDoFuture() #' future::plan(future::multisession, workers = 4) #' mod8 <- lm_imp(y ~ C1 + C2 + B2, data = wideDF, n.iter = 500, n.chains = 8) #' mod8$comp_info$future From d7a9c129caec1a23f593c9a4fb9042fff17756a8 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:49:40 +0100 Subject: [PATCH 082/176] try improve efficiency of JM by reducing syntax run in quadrature loop --- R/JAGSmodel_clmm.R | 25 +++++++++++++----------- R/JAGSmodel_glmm.R | 14 +++++++------ R/JAGSmodel_surv.R | 39 ++++++++++++++++++------------------- R/get_data_list.R | 10 +++++++--- R/helpfunctions_JAGSmodel.R | 4 ++-- 5 files changed, 50 insertions(+), 42 deletions(-) diff --git a/R/JAGSmodel_clmm.R b/R/JAGSmodel_clmm.R index 1fce64cc..0deece40 100644 --- a/R/JAGSmodel_clmm.R +++ b/R/JAGSmodel_clmm.R @@ -152,24 +152,27 @@ clmm_in_jm <- function(info) { # syntax to set values of dummy variables, # e.g. "M_lvlone[i, 8] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)" dummies <- if (!is.null(info$dummy_cols)) { - paste0(tab(), - paste_dummies(resp_mat = paste0(info$resp_mat, "gk"), - resp_col = paste0(info$resp_col, ', k'), - dummy_cols = paste0(info$dummy_cols, ', k'), - index = index, refs = info$refs), - collapse = "\n") + paste0( + paste_dummies(resp_mat = paste0(info$resp_mat, "gk"), + resp_col = paste0(info$resp_col, ', 1:15'), + dummy_cols = paste0(info$dummy_cols, ', 1:15'), + index = index, refs = info$refs), + collapse = "\n") } + # write model ---------------------------------------------------------------- - paste0(tab(6), info$resp_mat, "gk[", index, ", ", info$resp_col, + paste0(tab(4), "# calculate ", info$varname, " at the event times\n", + tab(4), "for (k in 1:15) {\n", + tab(6), info$resp_mat, "gk[", index, ", ", info$resp_col, ", k] ~ dcat(pgk_", info$varname, "[", index, ", 1:", info$ncat, ", k])", "\n", - tab(6), 'etagk_', info$varname, "[", index, ", k] <- ", - add_linebreaks(Z_predictor, indent = 12 + nchar(info$varname) + 10), + add_linebreaks(Z_predictor, indent = 10 + nchar(info$varname) + 14), "\n\n", - write_probs(info, index, isgk = TRUE, indent = 6), "\n\n", - write_logits(info, index, isgk = TRUE, indent = 6), "\n\n", + write_probs(info, index, isgk = TRUE, indent = 6L), "\n\n", + write_logits(info, index, isgk = TRUE, indent = 6L), "\n\n", + tab(4), "}\n", dummies, "\n" ) diff --git a/R/JAGSmodel_glmm.R b/R/JAGSmodel_glmm.R index c4f5f0ee..1553e26b 100644 --- a/R/JAGSmodel_glmm.R +++ b/R/JAGSmodel_glmm.R @@ -146,26 +146,28 @@ glmm_in_jm <- function(info) { } dummies <- if (!is.null(info$dummy_cols)) { - paste0('\n',tab(), + paste0('\n', paste_dummies(resp_mat = paste0(info$resp_mat, "gk"), - resp_col = paste0(info$resp_col, ', k'), - dummy_cols = paste0(info$dummy_cols, ', k'), + resp_col = paste0(info$resp_col, ', '), + dummy_cols = paste0(info$dummy_cols, ', 1:15'), index = index, refs = info$refs), collapse = "\n") } # write model ---------------------------------------------------------------- - paste0(tab(6), info$resp_mat, "gk[", index, ", ", info$resp_col, ", k] ~ ", + paste0(tab(4), "# calculate ", info$varname, " at the event times\n", + tab(4), "for (k in 1:15) {\n", + tab(6), info$resp_mat, "gk[", index, ", ", info$resp_col, ", k] ~ ", distr, trunc, "\n", repar, tab(6), linkfun(paste0("mugk_", info$varname, "[", index, ", k]")), " <- ", - add_linebreaks(Z_predictor, indent = linkindent + 11 + + add_linebreaks(Z_predictor, indent = linkindent + 12 + nchar(info$varname) + 9 + nchar(index)), "\n", + tab(4), "}\n", dummies, info$trafos, "\n" ) } - diff --git a/R/JAGSmodel_surv.R b/R/JAGSmodel_surv.R index 22445ee2..a893699f 100644 --- a/R/JAGSmodel_surv.R +++ b/R/JAGSmodel_surv.R @@ -184,7 +184,7 @@ jagsmodel_coxph <- function(info) { # survival surv_predictor <- paste0( paste0( - c(paste0("gkw[k] * exp(logh0s_", info$varname, "[", index, ", k]"), + c(paste0("gkw[] * exp(1)^(logh0s_", info$varname, "[", index, ", ]"), if (info$resp_mat[2L] != "M_lvlone" & !is.null(info$parelmts$M_lvlone)) { paste_linpred_jm(varname = info$varname, @@ -219,14 +219,11 @@ jagsmodel_coxph <- function(info) { "\n\n", # Gauss-Kronrod quadrature - tab(4L), "for (k in 1:15) {", "\n", - tab(6L), "logh0s_", info$varname, "[", index, ", k] <- inprod(", - info$parname, - "_Bh0_", info$varname, "[], Bsh0_", info$varname, "[15 * (", index, - " - 1) + k, ])", "\n", - tab(6L), "Surv_", info$varname, "[", index, ", k] <- ", - add_linebreaks(surv_predictor, indent = indent + 6L), "\n", - tab(4L), "}", "\n\n", + tab(4L), "logh0s_", info$varname, "[", index, ", 1:15] <- ", + "Bsh0_", info$varname, "[, ", index, ", ] %*% ", info$parname, + "_Bh0_", info$varname, "[]", "\n", + tab(4L), "Surv_", info$varname, "[", index, ", 1:15] <- ", + add_linebreaks(surv_predictor, indent = indent + 4L), "\n\n", # integration tab(4L), "log.surv_", info$varname, "[", index, "] <- -exp(eta_", @@ -344,7 +341,7 @@ jagsmodel_jm <- function(info) { # survival surv_predictor <- paste0( paste0( - c(paste0("gkw[k] * exp(logh0s_", info$varname, "[", index, ", k]"), + c(paste0("gkw[] * exp(1)^(logh0s_", info$varname, "[", index, ", ]"), if (info$resp_mat[2L] != "M_lvlone") { paste_linpred_jm(varname = info$varname, parname = info$parname, @@ -376,17 +373,13 @@ jagsmodel_jm <- function(info) { "\n\n", # Gauss-Kronrod quadrature - tab(4L), "for (k in 1:15) {", "\n", - tab(6L), "logh0s_", info$varname, "[", index, ", k] <- inprod(", - info$parname, - "_Bh0_", info$varname, "[], Bsh0_", info$varname, "[15 * (", - index, " - 1) + k, ])", "\n", - tab(6L), "Surv_", info$varname, "[", index, ", k] <- ", - add_linebreaks(surv_predictor, indent = indent + 6L), + "\n", tab(4L), "# Gauss-Kronrod quadrature\n", + tab(4L), "logh0s_", info$varname, "[", index, ", 1:15] <- ", + "Bsh0_", info$varname, "[, ", index, ", ] %*% ", info$parname, + "_Bh0_", info$varname, "[]", "\n", + tab(4L), "Surv_", info$varname, "[", index, ", 1:15] <- ", + add_linebreaks(surv_predictor, indent = indent + 4L), "\n\n", - paste0(unlist(lapply(info$tv_vars, gkmodel_in_jm, index = index)), - collapse = "\n"), - tab(4L), "}", "\n\n", # integration tab(4L), "log.surv_", info$varname, "[", index, "] <- -exp(eta_", @@ -401,6 +394,12 @@ jagsmodel_jm <- function(info) { tab(4L), "zeros_", info$varname, "[", index, "] ~ dpois(phi_", info$varname, "[", index, "])", "\n", + + # longitudinal variables + "\n", + paste0(unlist(lapply(info$tv_vars, gkmodel_in_jm, index = index)), + collapse = "\n"), + "\n", tab(), "}\n\n", # random effects diff --git a/R/get_data_list.R b/R/get_data_list.R index 5b3a9f38..918974cd 100644 --- a/R/get_data_list.R +++ b/R/get_data_list.R @@ -212,9 +212,13 @@ get_data_list <- function(Mlist, info_list, hyperpars, l[[paste0("Bh0_", x$varname)]] <- splines::splineDesign(h0knots, x$survtime, ord = 4L) - l[[paste0("Bsh0_", x$varname)]] <- - splines::splineDesign(h0knots, c(t(outer(x$survtime / 2L, gkx + 1L))), - ord = 4L) + + Bsh0 <- splines::splineDesign(h0knots, c(t(outer(x$survtime / 2L, gkx + 1L))), + ord = 4L) + l[[paste0("Bsh0_", x$varname)]] <- array( + dim = c(15, nrow(Bsh0)/15, ncol(Bsh0)), + data = Bsh0) + # vector of zeros for the "zeros trick" in JAGS l[[paste0("zeros_", x$varname)]] <- numeric(length(x$survtime)) diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 755129b5..3f8a81ae 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -765,7 +765,7 @@ paste_obsvalue <- function(varname, matname, index, column, isgk, ...) { # functions if (isgk) # "M_lvlonegk[i, 4, k]" in the GK-quadrature - paste0(matname, "gk[", index, ", ", column, ", k]") + paste0(matname, "gk[", index, ", ", column, ", 1:15]") else # "M_lvlone[srow_varname[i, k]" outside the quadrature paste0(matname, "[srow_", varname, "[", index, "], ", column, "]") } @@ -783,7 +783,7 @@ paste_underlvalue <- function(varname, covname, index, isgk, ...) { # functions if (isgk) - paste0("mugk_", covname, "[", index, ", k]") + paste0("mugk_", covname, "[", index, ", 1:15]") else paste0("mu_", covname, "[srow_", varname, "[", index, "]]") } From 7cb9aa4162825d98df9eba4289b78a3a0a322d19 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:50:02 +0100 Subject: [PATCH 083/176] remove doFuture from test for add_samples --- tests/testthat/test-add_samples.R | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/testthat/test-add_samples.R b/tests/testthat/test-add_samples.R index 04a3d204..e19e4679 100644 --- a/tests/testthat/test-add_samples.R +++ b/tests/testthat/test-add_samples.R @@ -10,7 +10,6 @@ test_that('add_samples works in simple setting',{ test_that('add_samples works in parallel', { skip_on_os(c("windows", "mac")) - doFuture::registerDoFuture() future::plan(future::cluster, workers = 2) lm2 <- lm_imp(y ~ C1 + C2 + B2, data = wideDF, n.iter = 50) expect_s3_class(add_samples(lm2, add = TRUE, n.iter = 50), class = "JointAI") From 00aa9aa4a66183849a16f37413ad15ccacf050ce Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 12:50:58 +0100 Subject: [PATCH 084/176] change in way the Gauss-Kronrod quadrature is written in the JAGS model --- .../coxph_lapply.models.jagsmodel..txt | 84 +++++++------------ 1 file changed, 32 insertions(+), 52 deletions(-) diff --git a/tests/testthat/testout/coxph_lapply.models.jagsmodel..txt b/tests/testthat/testout/coxph_lapply.models.jagsmodel..txt index ef09a99a..1a4b6107 100644 --- a/tests/testthat/testout/coxph_lapply.models.jagsmodel..txt +++ b/tests/testthat/testout/coxph_lapply.models.jagsmodel..txt @@ -7,10 +7,8 @@ model { eta_Srv_ftm_stts_cn[i] <- 0 logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[i, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (i - 1) + k, ]) - Surv_Srv_ftm_stts_cn[i, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[i, k]) - } + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) @@ -35,10 +33,8 @@ model { M_lvlone[i, 5] * beta[2] logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[i, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (i - 1) + k, ]) - Surv_Srv_ftm_stts_cn[i, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[i, k]) - } + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) @@ -67,10 +63,8 @@ model { M_lvlone[i, 5] * beta[2] logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[i, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (i - 1) + k, ]) - Surv_Srv_ftm_stts_cn[i, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[i, k]) - } + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) @@ -98,10 +92,8 @@ model { eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[i, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (i - 1) + k, ]) - Surv_Srv_ftm_stts_cn[i, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[i, k]) - } + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) @@ -149,10 +141,8 @@ model { (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * beta[5] logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[i, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (i - 1) + k, ]) - Surv_Srv_ftm_stts_cn[i, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[i, k]) - } + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) @@ -229,10 +219,8 @@ model { beta[5] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[i, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (i - 1) + k, ]) - Surv_Srv_ftm_stts_cn[i, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[i, k]) - } + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) @@ -329,15 +317,13 @@ model { M_lvlone[srow_Srv_ftm_stts_cn[ii], 4] * beta[7] + M_lvlone[srow_Srv_ftm_stts_cn[ii], 5] * beta[8] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[ii, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (ii - 1) + k, ]) - Surv_Srv_ftm_stts_cn[ii, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[ii, k] + - (M_lvlonegk[ii, 1, k] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] + - (M_lvlonegk[ii, 2, k] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + - M_lvlonegk[ii, 3, k] * beta[6] + - M_lvlonegk[ii, 4, k] * beta[7] + - M_lvlonegk[ii, 5, k] * beta[8]) - } + logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + + (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] + + (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + + M_lvlonegk[ii, 3, 1:15] * beta[6] + + M_lvlonegk[ii, 4, 1:15] * beta[7] + + M_lvlonegk[ii, 5, 1:15] * beta[8]) log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) @@ -369,12 +355,10 @@ model { (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[ii, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (ii - 1) + k, ]) - Surv_Srv_ftm_stts_cn[ii, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[ii, k] + - (M_lvlonegk[ii, 1, k] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] + - (M_lvlonegk[ii, 2, k] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]) - } + logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + + (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] + + (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]) log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) @@ -406,12 +390,10 @@ model { (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[ii, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (ii - 1) + k, ]) - Surv_Srv_ftm_stts_cn[ii, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[ii, k] + - (M_lvlonegk[ii, 1, k] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + - (M_lvlonegk[ii, 2, k] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]) - } + logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + + (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + + (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]) log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) @@ -451,13 +433,11 @@ model { (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5] - for (k in 1:15) { - logh0s_Srv_ftm_stts_cn[ii, k] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bsh0_Srv_ftm_stts_cn[15 * (ii - 1) + k, ]) - Surv_Srv_ftm_stts_cn[ii, k] <- gkw[k] * exp(logh0s_Srv_ftm_stts_cn[ii, k] + - (M_lvlonegk[ii, 1, k] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + - (M_lvlonegk[ii, 2, k] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + - (M_lvlonegk[ii, 3, k] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5]) - } + logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + + (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + + (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + + (M_lvlonegk[ii, 3, 1:15] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5]) log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) From 2430bb553b60fa862629567df7b5ade602a58953 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 13:18:13 +0100 Subject: [PATCH 085/176] bugfix in recording computational time (adapt instead of twice sample) --- R/helpfunctions_JAGS.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/helpfunctions_JAGS.R b/R/helpfunctions_JAGS.R index 8b2b76aa..84a2293f 100644 --- a/R/helpfunctions_JAGS.R +++ b/R/helpfunctions_JAGS.R @@ -141,8 +141,8 @@ run_parallel <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, res <- lapply(out, future::value) mcmc <- coda::as.mcmc.list(lapply(res, function(x) x$mcmc[[1L]])) - time_adapt <- do.call(c, lapply(res, "[[", "time_sample")) - time_sample <- do.call(c, lapply(res, "[[", "time_sample")) + time_adapt <- max(do.call(c, lapply(res, "[[", "time_adapt"))) + time_sample <- max(do.call(c, lapply(res, "[[", "time_sample"))) list(adapt = lapply(res, "[[", "adapt"), mcmc = mcmc, From f4b4fb9ae14e5b96cdceb4422fb01ad4dc69118d Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 16:23:49 +0100 Subject: [PATCH 086/176] added internal documentation --- R/helpfunctions_JAGSmodel.R | 203 ++++++++++++++++++++---------------- man/paste_coef.Rd | 20 ++++ man/paste_data.Rd | 25 +++++ man/paste_linpred.Rd | 33 ++++++ man/paste_scale.Rd | 24 +++++ man/paste_scaling.Rd | 28 +++++ 6 files changed, 243 insertions(+), 90 deletions(-) create mode 100644 man/paste_coef.Rd create mode 100644 man/paste_data.Rd create mode 100644 man/paste_linpred.Rd create mode 100644 man/paste_scale.Rd create mode 100644 man/paste_scaling.Rd diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 3f8a81ae..5d454020 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -1,99 +1,122 @@ # help functions --------------------------------------------------------------- # linear predictors ------------------------------------------------------------ +#' Write a linear predictor +#' +#' Construct a linear predictor from parameter names and indices, the name of +#' the data matrix and corresponding columns, and apply scaling to the data +#' if necessary. +#' +#' @param parname character string; name fo the parameter (e.g., "beta") +#' @param parlemts integer vector; indices of the parameter vector to be used; +#' should have the same length as `cols` +#' @param matname character string; name of the data matrix +#' @param index character string; name of the index (e.g., "i" or "ii") +#' @param cols integer vector; indices of the columns of `matname`, should have +#' the same length as `parlemts` +#' @param scale_pars matrix with row names according to the column names of +#' `matname` and columns "center" and "scale"; or NULL +#' @param isgk logical; is this linear predictor within the Gauss-Kronrod +#' quadrature? +#' +#' @keywords internal paste_linpred <- function(parname, parelmts, matnam, index, cols, scale_pars, isgk = FALSE) { - # paste a regular linear predictor - # - parname: name of the parameter, e.g. "beta" - # - parelmts: vector specifying which elements of the parameter vector are - # to be used, e.g. c(1,2,3,6,8,4) - # - matnam: name of the design matrix, e.g. "M_lvlone" or "M_ID" - # - index: character sting specifying the index to be used, e.g. "i" or "ii" - # - cols: index of the columns of the design matrix to be used, - # e.g. c(1, 4, 2, 10) - # - scale_pars: a matrix with row names according to the columns of the - # design matrix and columns "center" and "scale". - # Contains NA if a variable should not be scaled - # (could also be NULL instead of a matrix) - # - isgk: logical indicator of this is for within the Gauss-Kronrod quadrature - - paste( - paste_scaling(x = paste_data(matnam, index, cols, isgk), - rows = cols, - scale_pars = list(scale_pars)[rep(1, length(cols))], - scalemat = rep(paste0("sp", matnam), length(cols)) - ), - paste_coef(parname, parelmts), - sep = " * ", collapse = " + ") + scaled_data <- paste_scaling( + x = paste_data(matnam, index, cols, isgk), + rows = cols, + scale_pars = list(scale_pars)[rep(1, length(cols))], + scalemat = rep(paste0("sp", matnam), length(cols))) + + paste(scaled_data, paste_coef(parname, parelmts), + sep = " * ", collapse = " + ") } # * linpred help functions ----------------------------------------------------- -paste_data <- function(matnam, index, col, isgk = FALSE) { - # create a (vector of) data element(s) of a linear predictor, e.g. "X[i, 3]" - # if isgk = TRUE, the suffix "gk" will be added to "matname" - # - matnam: the name of the design matrix - # - index: the index to be used, e.g. "i" or "ii" - # - col: the column (or vector of columns) of the design matrix - # - isgk: is this within the Gauss-Kronrod quadrature? - paste0(matnam, if (isgk) {"gk"} else {""}, - "[", index, ", ", col, - if (isgk) {", k]"} else {"]"}) +#' Write the data element of a linear predictor +#' +#' @param matnam characters string; name of the data matrix +#' @param index character string; the index (e.g., "i", or "ii") +#' @param col integer vector; the indices of the columns in `matnam` +#' @param isgk logical; is this for within the Gauss-Kronrod quadrature? +#' +#' @return A vector of character strings of the form +#' `M_id[i, 3]` or `M_id[i, 3, k]`. +#' @keywords internal +#' +paste_data <- function(matnam, index, col, isgk = FALSE) { + if (isTRUE(isgk)) { + paste0(matnam, "gk[", index, ", ", col, ", k]") + } else { + paste0(matnam, "[", index, ", ", col, "]") + } } - +#' Write the coefficient part of a linear predictor +#' +#' @param parname character string; name of the coefficient (e.g., "beta") +#' @param parlemts vector of integers; the index of the parameter vector +#' +#' @return A vector of character strings of the form `beta[3]`. +#' +#' @keywords internal paste_coef <- function(parname, parelmts) { - # create a (vector of) coefficient element(s) of a linear predictor, - # e.g. beta[3] - # - parname: the name of the parameter, e.g. "alpha" or "beta" - # - parelmts: vector of integers giving the elements of the parameter to be - # used - paste0(parname, "[", parelmts, "]") } +#' Wrap a data element of a linear predictor in scaling syntax +#' +#' Identifies if a data element of a linear predictor should be scaled (based +#' on whether scaling parameters are given) and then calls `paste_scale()`. +#' +#' Calls `paste_scale()` on each element of `x`. +#' +#' @param x vector of character strings; to be scaled, typically matrix columns +#' @param rows integer vector; row numbers of the matrix containing the scaling +#' information +#' @param scale_pars matrix containing the scalign information, with columns +#' "center" and "scale" +#' @param scalemat the name of the scaling matrix in the JAGS model +#' (e.g. "spM_id") +#' +#' @keywords internal paste_scaling <- function(x, rows, scale_pars, scalemat) { - # identify if a data element of a linear predictor should be scaled (based - # on whether scaling parameters are given) and obtain the scaling trafo - # string - # - x: vector of expressions to scale - # - row: the row number(s) of the matrix containing the scaling parameters - # - scale_pars: scaling matrix - # - scalemat: name of the scaling matrix in JAGS, e.g. "spM_ID" + # if there is no scaling info at all, return x if (is.null(unlist(scale_pars))) { - x - } else { - cvapply(seq_along(x), function(k) { - if (rowSums(is.na(scale_pars[[k]][rows[k], , drop = FALSE])) > 0L) { - x[k] - } else { - paste_scale(x[[k]], row = rows[k], scalemat = scalemat[k]) - } - }) + return(x) } + + cvapply(seq_along(x), function(k) { + missing_scaleinfo <- any(is.na(scale_pars[[k]][rows[k], , drop = FALSE])) + if (missing_scaleinfo) { + x[k] + } else { + paste_scale(x[[k]], row = rows[k], scalemat = scalemat[k]) + } + }) } +#' Create the scaling in a data element of a linear predictor +#' +#' @param x a character string +#' @param row integer; indicating the row of `scalemat` to be used +#' @param scalemat character string; name of the matrix containing the scaling +#' information (e.g., "spM_lvlone"). This matrix is assumed to +#' have columns "center" and "scale". +#' @keywords internal +#' @return a character string of the form `(x - center)/scale`. +#' paste_scale <- function(x, row, scalemat) { - # create a (vector of) scaling transformation(s) for the data element(s) of a - # linear predictor - # - x: term that will be scaled (or vector of terms) - # - row: the row number(s) of the matrix containing the scaling parameters - # - scalemat: the name of the matrix containing the scaling parameters, - # e.g. "spM_lvlone" or "spM_ID" - # The matrix is assumed to have columns "center" and "scale". - paste0("(", x, " - ", scalemat, "[", row, ", 1])/", scalemat, "[", row, ", 2]") } - - - # paste rd. effects into jagsmodel --------------------------------------------- # used in jagsmodels that use random effects (2020-06-10) @@ -1197,26 +1220,26 @@ write_logits <- function(info, index, nonprop = FALSE, isgk = FALSE, # in the quadrature part for ordinal longitudinal outcomes in joint models write_exp_lp <- function(info, index, nonprop = FALSE, indent = 4L) { paste0(tab(indent), - "exp_lp_", info$varname, "[", index, ", ", 1L:(info$ncat - 1L), - ", 1:15] <- exp(1)^(gamma_", info$varname, "[", 1L:(info$ncat - 1L), "]", - " + etagk", - "_", info$varname, "[", index, ", 1:15", "]", - if (nonprop) { - paste0(" + eta_", info$varname, "_", 1L:(info$ncat - 1L), - "[", index, "]") - }, ")") + "exp_lp_", info$varname, "[", index, ", ", 1L:(info$ncat - 1L), + ", 1:15] <- exp(1)^(gamma_", info$varname, "[", 1L:(info$ncat - 1L), "]", + " + etagk", + "_", info$varname, "[", index, ", 1:15", "]", + if (nonprop) { + paste0(" + eta_", info$varname, "_", 1L:(info$ncat - 1L), + "[", index, "]") + }, ")") } write_psum_expit <- function(info, index, indent = 4L) { cvapply(1:(info$ncat - 1), function(r) { paste0(tab(indent), - "psumgk_", info$varname, "[", index, ", ", r, - ", 1:15] <- exp_lp_", info$varname, "[", index, ", ", r, - ", ]/(1 + exp_lp_", info$varname, "[", index, ", ", r, ", ])", - if (r < info$ncat - 1) { - paste0(" - psumgk_", info$varname, "[", index, ", ", r + 1, ", 1:15]") - } + "psumgk_", info$varname, "[", index, ", ", r, + ", 1:15] <- exp_lp_", info$varname, "[", index, ", ", r, + ", ]/(1 + exp_lp_", info$varname, "[", index, ", ", r, ", ])", + if (r < info$ncat - 1) { + paste0(" - psumgk_", info$varname, "[", index, ", ", r + 1, ", 1:15]") + } ) }) } @@ -1225,19 +1248,19 @@ write_psum_expit <- function(info, index, indent = 4L) { write_pgk <- function(info, index, indent = 4L) { c( paste0(tab(indent), - "pgk_", info$varname, "[", index, ", 1, 1:15] <- 1 - ifelse(", - "probsumgk_", info$varname, "[", index, ", ] > 1 - 1e-10, 1-1e-10, ", "\n", - tab(indent + 4 + nchar(info$varname) + nchar(index) + 15), - "ifelse(probsumgk_", info$varname, "[", index, ", ] < 1e-10, 1e-10, ", - "probsumgk_", info$varname, "[", index, ", ]))" + "pgk_", info$varname, "[", index, ", 1, 1:15] <- 1 - ifelse(", + "probsumgk_", info$varname, "[", index, ", ] > 1 - 1e-10, 1-1e-10, ", "\n", + tab(indent + 4 + nchar(info$varname) + nchar(index) + 15), + "ifelse(probsumgk_", info$varname, "[", index, ", ] < 1e-10, 1e-10, ", + "probsumgk_", info$varname, "[", index, ", ]))" ), paste0(tab(indent), - "pgk_", info$varname, "[", index, ", 2:", info$ncat, ", 1:15] <- ifelse(", - "psumgk_", info$varname, "[", index, ", , ] > 1 - 1e-10, 1-1e-10, ", "\n", - tab(indent + 4 + nchar(info$varname) + nchar(index) + 17), - "ifelse(psumgk_", info$varname, "[", index, ", , ] < 1e-10, 1e-10, ", - "psumgk_", info$varname, "[", index, ", , ]))" + "pgk_", info$varname, "[", index, ", 2:", info$ncat, ", 1:15] <- ifelse(", + "psumgk_", info$varname, "[", index, ", , ] > 1 - 1e-10, 1-1e-10, ", "\n", + tab(indent + 4 + nchar(info$varname) + nchar(index) + 17), + "ifelse(psumgk_", info$varname, "[", index, ", , ] < 1e-10, 1e-10, ", + "psumgk_", info$varname, "[", index, ", , ]))" ), paste0("\n", tab(indent), diff --git a/man/paste_coef.Rd b/man/paste_coef.Rd new file mode 100644 index 00000000..70798546 --- /dev/null +++ b/man/paste_coef.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGSmodel.R +\name{paste_coef} +\alias{paste_coef} +\title{Write the coefficient part of a linear predictor} +\usage{ +paste_coef(parname, parelmts) +} +\arguments{ +\item{parname}{character string; name of the coefficient (e.g., "beta")} + +\item{parlemts}{vector of integers; the index of the parameter vector} +} +\value{ +A vector of character strings of the form \code{beta[3]}. +} +\description{ +Write the coefficient part of a linear predictor +} +\keyword{internal} diff --git a/man/paste_data.Rd b/man/paste_data.Rd new file mode 100644 index 00000000..a764c0c9 --- /dev/null +++ b/man/paste_data.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGSmodel.R +\name{paste_data} +\alias{paste_data} +\title{Write the data element of a linear predictor} +\usage{ +paste_data(matnam, index, col, isgk = FALSE) +} +\arguments{ +\item{matnam}{characters string; name of the data matrix} + +\item{index}{character string; the index (e.g., "i", or "ii")} + +\item{col}{integer vector; the indices of the columns in \code{matnam}} + +\item{isgk}{logical; is this for within the Gauss-Kronrod quadrature?} +} +\value{ +A vector of character strings of the form +\code{M_id[i, 3]} or \code{M_id[i, 3, k]}. +} +\description{ +Write the data element of a linear predictor +} +\keyword{internal} diff --git a/man/paste_linpred.Rd b/man/paste_linpred.Rd new file mode 100644 index 00000000..1f9008f0 --- /dev/null +++ b/man/paste_linpred.Rd @@ -0,0 +1,33 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGSmodel.R +\name{paste_linpred} +\alias{paste_linpred} +\title{Write a linear predictor} +\usage{ +paste_linpred(parname, parelmts, matnam, index, cols, scale_pars, isgk = FALSE) +} +\arguments{ +\item{parname}{character string; name fo the parameter (e.g., "beta")} + +\item{index}{character string; name of the index (e.g., "i" or "ii")} + +\item{cols}{integer vector; indices of the columns of \code{matname}, should have +the same length as \code{parlemts}} + +\item{scale_pars}{matrix with row names according to the column names of +\code{matname} and columns "center" and "scale"; or NULL} + +\item{isgk}{logical; is this linear predictor within the Gauss-Kronrod +quadrature?} + +\item{parlemts}{integer vector; indices of the parameter vector to be used; +should have the same length as \code{cols}} + +\item{matname}{character string; name of the data matrix} +} +\description{ +Construct a linear predictor from parameter names and indices, the name of +the data matrix and corresponding columns, and apply scaling to the data +if necessary. +} +\keyword{internal} diff --git a/man/paste_scale.Rd b/man/paste_scale.Rd new file mode 100644 index 00000000..5c30c122 --- /dev/null +++ b/man/paste_scale.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGSmodel.R +\name{paste_scale} +\alias{paste_scale} +\title{Create the scaling in a data element of a linear predictor} +\usage{ +paste_scale(x, row, scalemat) +} +\arguments{ +\item{x}{a character string} + +\item{row}{integer; indicating the row of \code{scalemat} to be used} + +\item{scalemat}{character string; name of the matrix containing the scaling +information (e.g., "spM_lvlone"). This matrix is assumed to +have columns "center" and "scale".} +} +\value{ +a character string of the form \code{(x - center)/scale}. +} +\description{ +Create the scaling in a data element of a linear predictor +} +\keyword{internal} diff --git a/man/paste_scaling.Rd b/man/paste_scaling.Rd new file mode 100644 index 00000000..d3f6237c --- /dev/null +++ b/man/paste_scaling.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGSmodel.R +\name{paste_scaling} +\alias{paste_scaling} +\title{Wrap a data element of a linear predictor in scaling syntax} +\usage{ +paste_scaling(x, rows, scale_pars, scalemat) +} +\arguments{ +\item{x}{vector of character strings; to be scaled, typically matrix columns} + +\item{rows}{integer vector; row numbers of the matrix containing the scaling +information} + +\item{scale_pars}{matrix containing the scalign information, with columns +"center" and "scale"} + +\item{scalemat}{the name of the scaling matrix in the JAGS model +(e.g. "spM_id")} +} +\description{ +Identifies if a data element of a linear predictor should be scaled (based +on whether scaling parameters are given) and then calls \code{paste_scale()}. +} +\details{ +Calls \code{paste_scale()} on each element of \code{x}. +} +\keyword{internal} From 718c891c511fe4ef7db6f08c94f2a1363aa9c9f3 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 13 Feb 2022 16:24:04 +0100 Subject: [PATCH 087/176] remove doFuture from documentation --- man/JointAI.Rd | 2 +- man/model_imp.Rd | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/man/JointAI.Rd b/man/JointAI.Rd index e3b80ea9..058dbb5f 100644 --- a/man/JointAI.Rd +++ b/man/JointAI.Rd @@ -51,7 +51,7 @@ such as \href{https://CRAN.R-project.org/package=survival}{\strong{survival}}). Computations can be performed in parallel to reduce computational time, -using the packages \pkg{future} (and \pkg{doFuture}), +using the package \pkg{future}, the argument \code{shrinkage} allows the user to impose a penalty on the regression coefficients of some or all models involved, and hyper-parameters can be changed via the argument \code{hyperpars}. diff --git a/man/model_imp.Rd b/man/model_imp.Rd index ca6c8f0d..80d6207e 100644 --- a/man/model_imp.Rd +++ b/man/model_imp.Rd @@ -827,7 +827,6 @@ mod7 <- coxph_imp(Surv(futime, status != 'censored') ~ age + sex + copper + # MCMC chains are run sequentially. # To run MCMC chains in parallel, a strategy can be specified using the # package \pkg{future} (see ?future::plan), for example: -doFuture::registerDoFuture() future::plan(future::multisession, workers = 4) mod8 <- lm_imp(y ~ C1 + C2 + B2, data = wideDF, n.iter = 500, n.chains = 8) mod8$comp_info$future From 0af75ec78214143a877f7ec21154d4f187762f6e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 15 Feb 2022 14:58:08 +0100 Subject: [PATCH 088/176] change description of parallel computing in vignette --- vignettes/MCMCsettings.Rmd | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/vignettes/MCMCsettings.Rmd b/vignettes/MCMCsettings.Rmd index 973fb4a2..65356ec7 100644 --- a/vignettes/MCMCsettings.Rmd +++ b/vignettes/MCMCsettings.Rmd @@ -407,16 +407,11 @@ and `beta`, the random effects `b__` (e.g. `b_bmi_ID` and ## Parallel sampling To reduce computational time, it is possible to perform sampling of multiple MCMC chains in parallel on multiple cores. This can be achieved with the help -of the packages [**future**](https://CRAN.R-project.org/package=future) and -[**doFuture**](https://CRAN.R-project.org/package=doFuture). +of the package [**future**](https://CRAN.R-project.org/package=future). -To perform parallel sampling, a parallel adapter has to be registered -```{r, eval = FALSE} -doFuture::registerDoFuture() -``` -and a plan how to "resolve futures" needs to be specified; this is the -specification that determines if sampling is performed sequentially or in -parallel. For example, +To perform parallel sampling, a plan how to "resolve futures" needs to be +specified; this is the specification that determines if sampling is performed +sequentially or in parallel. For example, ```{r, eval = FALSE} future::plan(future::multisession, workers = 4) ``` From f351849ae5990ab04af9df9827a6124ebd3396cd Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 15 Feb 2022 15:02:13 +0100 Subject: [PATCH 089/176] minor numerical changes in results after re-running vignettes --- vignettes/AfterFitting.Rmd | 92 ++++++++++++++++--------------- vignettes/MinimalExample.Rmd | 26 ++++----- vignettes/SelectingParameters.Rmd | 78 +++++++++++++------------- 3 files changed, 99 insertions(+), 97 deletions(-) diff --git a/vignettes/AfterFitting.Rmd b/vignettes/AfterFitting.Rmd index 7a757a8b..c955e95d 100644 --- a/vignettes/AfterFitting.Rmd +++ b/vignettes/AfterFitting.Rmd @@ -198,16 +198,16 @@ summary(mod13a) #> #> #> Posterior summary: -#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 81.550 10.084 61.559 101.577 0.00000 1.00 0.0255 -#> genderfemale 0.377 2.567 -4.576 5.328 0.86667 1.00 0.0258 -#> WC 0.304 0.075 0.152 0.447 0.00000 1.00 0.0258 -#> alc>=1 6.298 2.392 1.601 10.743 0.00667 1.02 0.0309 -#> creat 7.451 7.713 -7.000 21.888 0.34133 1.00 0.0307 +#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD +#> (Intercept) 81.077 9.6921 61.66 99.602 0.000 1.011 0.0258 +#> genderfemale 0.368 2.6138 -4.74 5.594 0.871 0.999 0.0258 +#> WC 0.306 0.0736 0.16 0.448 0.000 1.012 0.0259 +#> alc>=1 6.365 2.4692 1.38 10.897 0.016 1.006 0.0291 +#> creat 7.747 7.5949 -7.19 22.496 0.299 1.003 0.0264 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_SBP 14.4 0.755 13.1 16 1 0.0279 +#> sigma_SBP 14.4 0.779 13 16 1.02 0.0278 #> #> #> MCMC settings: @@ -244,22 +244,24 @@ summary(mod13b, missinfo = TRUE) #> #> #> Posterior summary: -#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 17.69623 2.41136 12.9250 22.4174 0.00000 1.00 0.0285 -#> GESTBIR -0.05154 0.04741 -0.1468 0.0406 0.27200 1.01 0.0264 -#> ETHNother 0.01861 0.15346 -0.2821 0.3164 0.90133 1.01 0.0320 -#> HEIGHT_M 0.00072 0.00959 -0.0179 0.0196 0.94400 1.01 0.0288 -#> ns(age, df = 3)1 -0.23948 0.07651 -0.3907 -0.0885 0.00267 1.01 0.0258 -#> ns(age, df = 3)2 1.93595 0.11370 1.7147 2.1529 0.00000 1.01 0.0258 -#> ns(age, df = 3)3 -1.25001 0.05938 -1.3668 -1.1291 0.00000 1.00 0.0258 +#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD +#> (Intercept) 17.567738 2.30929 12.9758 21.9295 0.000 1.01 0.0271 +#> GESTBIR -0.049258 0.04749 -0.1427 0.0472 0.312 1.00 0.0258 +#> ETHNother 0.022619 0.15328 -0.2763 0.3213 0.883 1.00 0.0269 +#> HEIGHT_M 0.000925 0.00962 -0.0174 0.0195 0.936 1.02 0.0274 +#> ns(age, df = 3)1 -0.245192 0.07574 -0.3947 -0.0950 0.000 1.00 0.0258 +#> ns(age, df = 3)2 1.939673 0.11262 1.7100 2.1489 0.000 1.00 0.0258 +#> ns(age, df = 3)3 -1.247972 0.05842 -1.3656 -1.1326 0.000 1.01 0.0258 +#> #> #> Posterior summary of random effects covariance matrix: #> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> D_bmi_ID[1,1] 0.701 0.0775 0.566 0.867 1 0.0294 +#> D_bmi_ID[1,1] 0.696 0.0781 0.552 0.858 1.02 0.0278 +#> #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_bmi 0.684 0.0117 0.661 0.708 1 0.0278 +#> sigma_bmi 0.684 0.0119 0.662 0.709 1 0.0269 #> #> #> MCMC settings: @@ -333,12 +335,12 @@ GR_crit(mod13a) #> Potential scale reduction factors: #> #> Point est. Upper C.I. -#> (Intercept) 1.000 1.00 -#> genderfemale 1.000 1.00 -#> WC 0.999 1.00 -#> alc>=1 1.011 1.04 -#> creat 1.004 1.01 -#> sigma_SBP 1.001 1.01 +#> (Intercept) 1.01 1.02 +#> genderfemale 1.00 1.01 +#> WC 1.00 1.01 +#> alc>=1 1.00 1.01 +#> creat 1.00 1.00 +#> sigma_SBP 1.02 1.05 #> #> Multivariate psrf #> @@ -363,13 +365,13 @@ takes the arguments of `mcmcse.mat()`. ```r MC_error(mod13a) -#> est MCSE SD MCSE/SD -#> (Intercept) 81.55 0.2573 10.084 0.026 -#> genderfemale 0.38 0.0663 2.567 0.026 -#> WC 0.30 0.0019 0.075 0.026 -#> alc>=1 6.30 0.0738 2.392 0.031 -#> creat 7.45 0.2370 7.713 0.031 -#> sigma_SBP 14.41 0.0211 0.755 0.028 +#> est MCSE SD MCSE/SD +#> (Intercept) 81.08 0.2502 9.692 0.026 +#> genderfemale 0.37 0.0675 2.614 0.026 +#> WC 0.31 0.0019 0.074 0.026 +#> alc>=1 6.37 0.0718 2.469 0.029 +#> creat 7.75 0.2007 7.595 0.026 +#> sigma_SBP 14.40 0.0217 0.779 0.028 ``` `MC_error()` returns an object of class `MCElist`, which is a list containing @@ -438,10 +440,10 @@ summary(mod13c, subset = c(analysis_main = FALSE, other_models = TRUE)) #> #> Posterior summary: #> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 0.45047 1.6388 -2.6234 3.6352 0.8027 1.02 0.0804 -#> genderfemale -0.88047 0.4280 -1.7266 -0.0523 0.0453 1.07 0.0997 -#> WC 0.00683 0.0116 -0.0168 0.0286 0.5480 1.00 0.0388 -#> creat -1.47351 1.2420 -3.9374 0.9389 0.2360 1.02 0.0741 +#> (Intercept) 0.51390 1.5325 -2.6068 3.4078 0.7080 1.01 0.0535 +#> genderfemale -0.88236 0.3995 -1.6322 -0.0498 0.0373 1.04 0.0762 +#> WC 0.00632 0.0115 -0.0169 0.0298 0.5627 1.01 0.0375 +#> creat -1.48151 1.2238 -3.9056 0.9785 0.2213 1.00 0.0603 #> #> #> # --------------------------------------------------------------------- # @@ -449,14 +451,14 @@ summary(mod13c, subset = c(analysis_main = FALSE, other_models = TRUE)) #> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # #> #> Posterior summary: -#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 0.842494 0.07482 0.695583 0.98859 0.000 1.003 0.0267 -#> genderfemale -0.177470 0.02284 -0.222692 -0.13586 0.000 0.999 0.0267 -#> WC 0.000894 0.00075 -0.000618 0.00236 0.231 1.005 0.0269 +#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD +#> (Intercept) 0.844704 0.076409 0.694938 0.99127 0.000 1 0.0258 +#> genderfemale -0.178815 0.022122 -0.223627 -0.13699 0.000 1 0.0258 +#> WC 0.000877 0.000772 -0.000612 0.00243 0.256 1 0.0258 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_creat 0.146 0.00775 0.131 0.162 1 0.0286 +#> sigma_creat 0.145 0.00769 0.132 0.161 1.01 0.0277 #> #> #> # --------------------------------------------------------------------- # @@ -464,13 +466,13 @@ summary(mod13c, subset = c(analysis_main = FALSE, other_models = TRUE)) #> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # #> #> Posterior summary: -#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 97.52 1.45 94.77 100.42 0.0000 1.02 0.0258 -#> genderfemale -5.25 2.11 -9.35 -1.16 0.0133 1.00 0.0255 +#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD +#> (Intercept) 97.41 1.52 94.48 100.469 0.0000 1.00 0.0258 +#> genderfemale -5.16 2.21 -9.38 -0.971 0.0147 1.01 0.0258 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_WC 14.5 0.777 13 16.1 1 0.0269 +#> sigma_WC 14.5 0.785 13.2 16.3 1 0.0258 #> #> #> # ----------------------------------------------------------- # @@ -610,11 +612,11 @@ predict(mod13a, newdata = NHANES[27, ]) #> SBP gender age race WC alc educ creat albu #> 392 126.6667 male 32 Mexican American 94.1 <1 low 0.83 4.2 #> uricacid bili occup smoke fit 2.5% 97.5% -#> 392 8.7 1 former 116.3687 112.6749 120.4215 +#> 392 8.7 1 former 116.3273 112.4343 120.1817 #> #> $fitted #> fit 2.5% 97.5% -#> 1 116.3687 112.6749 120.4215 +#> 1 116.3273 112.4343 120.1817 ``` `predict()` returns a list with elements `dat`, `fit` and `quantiles`, diff --git a/vignettes/MinimalExample.Rmd b/vignettes/MinimalExample.Rmd index 11d67fc9..78d93dbd 100644 --- a/vignettes/MinimalExample.Rmd +++ b/vignettes/MinimalExample.Rmd @@ -102,22 +102,22 @@ summary(lm1) #> #> Posterior summary: #> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 62.175 22.7300 17.3968 109.167 0.00533 1.00 0.0258 -#> genderfemale -3.169 2.3143 -7.7968 1.230 0.15467 1.00 0.0281 -#> age 0.361 0.0726 0.2200 0.503 0.00000 1.00 0.0258 -#> raceOther Hispanic 0.419 4.9972 -9.6118 10.125 0.93733 1.00 0.0258 -#> raceNon-Hispanic White -1.491 3.0729 -7.2896 4.572 0.61200 1.00 0.0292 -#> raceNon-Hispanic Black 8.797 3.5617 2.1012 15.660 0.01467 1.00 0.0258 -#> raceother 3.588 3.4812 -3.2597 10.519 0.29600 1.00 0.0258 -#> WC 0.237 0.0845 0.0759 0.410 0.00667 1.00 0.0258 -#> alc>=1 7.175 2.2572 2.7547 11.546 0.00533 1.00 0.0349 -#> educhigh -3.361 2.1779 -7.6632 1.026 0.12800 1.01 0.0268 -#> albu 5.100 3.9600 -2.8385 12.710 0.20667 1.01 0.0270 -#> bili -5.739 5.0829 -15.8350 4.136 0.25333 1.00 0.0336 +#> (Intercept) 60.468 23.2853 14.5276 106.086 0.00933 1.012 0.0294 +#> genderfemale -3.111 2.2576 -7.8405 1.301 0.16933 1.006 0.0258 +#> age 0.361 0.0707 0.2250 0.496 0.00000 1.002 0.0267 +#> raceOther Hispanic 0.606 5.0592 -9.1456 10.972 0.91733 1.009 0.0258 +#> raceNon-Hispanic White -1.414 3.1084 -7.7117 4.190 0.66133 1.004 0.0258 +#> raceNon-Hispanic Black 8.992 3.5962 2.1122 16.088 0.01200 0.999 0.0258 +#> raceother 3.744 3.5421 -3.2277 10.644 0.30000 1.012 0.0258 +#> WC 0.241 0.0829 0.0852 0.411 0.00133 1.013 0.0267 +#> alc>=1 7.282 2.2352 2.8051 11.519 0.00133 1.014 0.0315 +#> educhigh -3.390 2.1778 -7.6542 1.039 0.12533 1.001 0.0258 +#> albu 5.312 4.2827 -3.0474 13.791 0.21600 1.009 0.0294 +#> bili -5.456 4.9605 -14.8959 4.258 0.26000 1.005 0.0266 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_SBP 13.2 0.706 11.9 14.7 1 0.0295 +#> sigma_SBP 13.2 0.719 11.9 14.7 1.01 0.0273 #> #> #> MCMC settings: diff --git a/vignettes/SelectingParameters.Rmd b/vignettes/SelectingParameters.Rmd index 0dbf8121..15395403 100644 --- a/vignettes/SelectingParameters.Rmd +++ b/vignettes/SelectingParameters.Rmd @@ -626,16 +626,16 @@ summary(lm5) #> #> #> Posterior summary: -#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 80.218 9.6712 61.82 97.670 0.00000 1.01 0.0230 -#> genderfemale 0.457 2.6764 -4.63 5.985 0.88667 1.03 0.0577 -#> WC 0.313 0.0712 0.18 0.458 0.00000 1.02 0.0569 -#> alc>=1 6.400 2.4072 1.94 10.885 0.00667 1.00 0.0774 -#> creat 7.935 7.3761 -5.01 21.334 0.26667 1.09 0.0598 +#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD +#> (Intercept) 81.737 9.9071 62.317 100.78 0.0000 1.023 0.0577 +#> genderfemale 0.213 2.5267 -4.562 4.93 0.9133 1.050 0.0577 +#> WC 0.301 0.0755 0.136 0.45 0.0000 0.999 0.0577 +#> alc>=1 6.157 2.5434 1.197 11.01 0.0333 1.009 0.0671 +#> creat 7.781 7.5670 -5.807 23.68 0.2933 1.052 0.0634 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_SBP 14.4 0.735 13.1 15.8 1.03 0.0577 +#> sigma_SBP 14.4 0.756 13.1 15.9 1.05 0.0577 #> #> #> MCMC settings: @@ -667,26 +667,26 @@ GR_crit(lm5) #> Potential scale reduction factors: #> #> Point est. Upper C.I. -#> (Intercept) 0.999 1.01 -#> genderfemale 1.009 1.03 -#> WC 1.005 1.02 -#> alc>=1 0.996 1.00 -#> creat 1.024 1.09 -#> sigma_SBP 1.011 1.03 +#> (Intercept) 1.003 1.023 +#> genderfemale 1.012 1.050 +#> WC 0.997 0.999 +#> alc>=1 1.000 1.009 +#> creat 1.018 1.052 +#> sigma_SBP 1.012 1.055 #> #> Multivariate psrf #> -#> 1.02 +#> 1.03 # Monte Carlo Error of the MCMC sample MC_error(lm5) #> est MCSE SD MCSE/SD -#> (Intercept) 80.22 0.2226 9.671 0.023 -#> genderfemale 0.46 0.1545 2.676 0.058 -#> WC 0.31 0.0041 0.071 0.057 -#> alc>=1 6.40 0.1862 2.407 0.077 -#> creat 7.93 0.4412 7.376 0.060 -#> sigma_SBP 14.37 0.0424 0.735 0.058 +#> (Intercept) 81.74 0.5720 9.907 0.058 +#> genderfemale 0.21 0.1459 2.527 0.058 +#> WC 0.30 0.0044 0.075 0.058 +#> alc>=1 6.16 0.1707 2.543 0.067 +#> creat 7.78 0.4798 7.567 0.063 +#> sigma_SBP 14.38 0.0437 0.756 0.058 ``` When `analysis_main` was not switched on the default behaviour is that all @@ -713,21 +713,21 @@ GR_crit(lm5, subset = c(analysis_main = FALSE, other_models = TRUE)) #> Potential scale reduction factors: #> #> Point est. Upper C.I. -#> alc: (Intercept) 1.034 1.12 -#> alc: genderfemale 1.073 1.24 -#> alc: WC 1.004 1.01 -#> alc: creat 1.028 1.09 -#> creat: (Intercept) 0.999 1.00 -#> creat: genderfemale 0.999 1.00 -#> creat: WC 0.997 1.00 -#> WC: (Intercept) 0.998 1.00 -#> WC: genderfemale 1.008 1.02 -#> sigma_creat 1.005 1.03 -#> sigma_WC 1.007 1.03 +#> alc: (Intercept) 1.096 1.306 +#> alc: genderfemale 1.076 1.254 +#> alc: WC 1.045 1.143 +#> alc: creat 1.023 1.089 +#> creat: (Intercept) 1.004 1.024 +#> creat: genderfemale 1.005 1.017 +#> creat: WC 1.001 1.014 +#> WC: (Intercept) 0.997 0.997 +#> WC: genderfemale 1.001 1.001 +#> sigma_creat 1.009 1.029 +#> sigma_WC 1.020 1.081 #> #> Multivariate psrf #> -#> 1.08 +#> 1.13 ``` To select only some of the parameters, they can be specified directly by @@ -744,16 +744,16 @@ summary(lm5, subset = list(other = c('creat', 'alc>=1'))) #> #> #> Posterior summary: -#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 80.218 9.6712 61.82 97.670 0.00000 1.01 0.0230 -#> genderfemale 0.457 2.6764 -4.63 5.985 0.88667 1.03 0.0577 -#> WC 0.313 0.0712 0.18 0.458 0.00000 1.02 0.0569 -#> alc>=1 6.400 2.4072 1.94 10.885 0.00667 1.00 0.0774 -#> creat 7.935 7.3761 -5.01 21.334 0.26667 1.09 0.0598 +#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD +#> (Intercept) 81.737 9.9071 62.317 100.78 0.0000 1.023 0.0577 +#> genderfemale 0.213 2.5267 -4.562 4.93 0.9133 1.050 0.0577 +#> WC 0.301 0.0755 0.136 0.45 0.0000 0.999 0.0577 +#> alc>=1 6.157 2.5434 1.197 11.01 0.0333 1.009 0.0671 +#> creat 7.781 7.5670 -5.807 23.68 0.2933 1.052 0.0634 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_SBP 14.4 0.735 13.1 15.8 1.03 0.0577 +#> sigma_SBP 14.4 0.756 13.1 15.9 1.05 0.0577 #> #> #> MCMC settings: From 316b07a235f253352af81422b89629e86698fd05 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 15 Feb 2022 15:03:46 +0100 Subject: [PATCH 090/176] re-ran pre-compiled vignettes --- vignettes/figures_AfterFitting/MCE15a-1.png | Bin 3292 -> 3205 bytes .../figures_AfterFitting/densplot15a-1.png | Bin 8791 -> 8877 bytes .../figures_AfterFitting/ggdens15a-1.png | Bin 7983 -> 8037 bytes .../figures_AfterFitting/ggtrace15a-1.png | Bin 10463 -> 10820 bytes 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zhw_uJ|Jc731}iq29jkuwYx|lPEc#B-eo>?Fpumm+w{6dP$$b%-m}XQ zxpVZTJZUd}iQB~&Q)dRDLe#52S{|?K-j=99UMgQTZux4UGB}z@Pt~|b%x_~YuS&D&nyOTLS&ja*c%U0nPJDL{)N}rN zS*pl&rPUUN1224IkbaO?_xZ*~uh>sjkWc#CzNU&rOYpTdjUzJE0J!TkT%vB!HN{D* zV8 Date: Tue, 15 Feb 2022 15:05:25 +0100 Subject: [PATCH 091/176] change in output of list_models() after re-running vignettes after bugfix (quite a while ago...) --- vignettes/ModelSpecification.Rmd | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/vignettes/ModelSpecification.Rmd b/vignettes/ModelSpecification.Rmd index 9856cbf0..560df899 100644 --- a/vignettes/ModelSpecification.Rmd +++ b/vignettes/ModelSpecification.Rmd @@ -425,7 +425,7 @@ list_models(mod3e, priors = FALSE, regcoef = FALSE, otherpars = FALSE) #> family: gaussian #> link: identity #> * Predictor variables: -#> (Intercept), age, genderfemale +#> (Intercept), age, genderfemale, I(WC^2) #> #> #> Linear model for "WC" @@ -454,7 +454,7 @@ list_models(mod3f, priors = FALSE, regcoef = FALSE, otherpars = FALSE) #> family: gaussian #> link: identity #> * Predictor variables: -#> (Intercept), age, genderfemale, WC +#> (Intercept), age, genderfemale, WC, I(WC^2) #> #> #> Linear model for "WC" @@ -485,7 +485,7 @@ list_models(mod3g, priors = FALSE, regcoef = FALSE, otherpars = FALSE) #> family: gaussian #> link: identity #> * Predictor variables: -#> (Intercept), age, genderfemale, WC +#> (Intercept), age, genderfemale, WC, I(WC^2) #> #> #> Linear model for "WC" @@ -1069,12 +1069,12 @@ list_models(mod9b, priors = FALSE, regcoef = FALSE, otherpars = FALSE, refcat = #> #> Multinomial logit model for "occup" #> * Predictor variables: -#> (Intercept), genderfemale, age, educhigh, smokeformer, smokecurrent +#> (Intercept), genderfemale, age, educhigh, smokeformer, smokecurrent, log(WC) #> #> #> Cumulative logit model for "smoke" #> * Predictor variables: -#> genderfemale, age, educhigh +#> genderfemale, age, educhigh, log(WC) #> #> #> Linear model for "WC" @@ -1085,7 +1085,7 @@ list_models(mod9b, priors = FALSE, regcoef = FALSE, otherpars = FALSE, refcat = ``` **Note:**
-Omitting auxiliary variables from the analysis model implies that the outcome +Omitting auxiliary variables from the analysis model implies that the outcome is independent of these variables, conditional on the other variables in the model. If this is not true, the model is mis-specified which may lead to biased results (similar to leaving a confounding variable out of a model). From 9be0bc3f5ff6dccbccf09cbd8c1bd7c3c897e964 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 15 Feb 2022 15:05:45 +0100 Subject: [PATCH 092/176] automatic changes in formating vignettes after re-running pre-compiled vignettes --- vignettes/ModelSpecification.Rmd | 351 +++++++++++------------------- vignettes/SelectingParameters.Rmd | 181 ++++----------- 2 files changed, 159 insertions(+), 373 deletions(-) diff --git a/vignettes/ModelSpecification.Rmd b/vignettes/ModelSpecification.Rmd index 560df899..3e247d1d 100644 --- a/vignettes/ModelSpecification.Rmd +++ b/vignettes/ModelSpecification.Rmd @@ -1,7 +1,7 @@ --- title: "Model Specification" date: "2020-06-20" -output: +output: rmarkdown::html_vignette: toc: true depth: 4 @@ -16,21 +16,21 @@ vignette: > In this vignette, we use the [NHANES](https://nerler.github.io/JointAI/reference/NHANES.html) -data for examples in cross-sectional data and the +data for examples in cross-sectional data and the dataset [simLong](https://nerler.github.io/JointAI/reference/simLong.html) for examples in longitudinal data. -For more info on these datasets, check out the vignette +For more info on these datasets, check out the vignette [*Visualizing Incomplete Data*](https://nerler.github.io/JointAI/articles/VisualizingIncompleteData.html), in which the distributions of variables and missing values in both sets is explored. To learn more about the theoretical background of the statistical approach -implemented in **JointAI**, check out the vignette +implemented in **JointAI**, check out the vignette [Theoretical Background](https://nerler.github.io/JointAI/articles/TheoreticalBackground.html). **Note:**
In some of the examples we use `n.adapt = 0` (and `n.iter = 0`, which is the -default). +default). This is to prevent the MCMC sampling and thereby reduce computational time when compiling this vignette. @@ -52,7 +52,7 @@ compiling this vignette. * `survreg_imp()`: parametric (Weibull) survival models * `coxph_imp()`: Proportional hazards survival models * `JM_imp()`: Joint model for longitudinal and survival data - + Specification of these functions is similar to the specification of the complete data versions `lm()`, `glm()`, `lme()` @@ -67,45 +67,26 @@ and [`coxph()`](https://stat.ethz.ch/R-manual/R-devel/library/survival/html/coxp All functions require the arguments `formula` (or `fixed` and `random` in for mixed models) and `data`. -Specification of the (fixed effects) model formula is demonstrated in section +Specification of the (fixed effects) model formula is demonstrated in section [Model formula](#ModelFormula), specification of the random random effects in section [Multi-level structure & longitudinal covariates](#MultiLevelStructure). -Additionally, `glm_imp()`, `glme_imp()` and `glmer_imp()` require the -specification of the model +Additionally, `glm_imp()`, `glme_imp()` and `glmer_imp()` require the +specification of the model [`family`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html) -(and `link` function). +(and `link` function). ### Model family and link functions -Implemented families and links for `glm_imp()`, `glme_imp()` and `glmer_imp()` +Implemented families and links for `glm_imp()`, `glme_imp()` and `glmer_imp()` are - - - - - - - - - - - - - - - - - - - - - - - - - -
family
`gaussian` with links: `identity`, `log`
`binomial` with links: `logit`, `probit`, `log`, `cloglog`
`Gamma` with links: `inverse`, `identity`, `log`
`poisson` with links: `log`, `identity`
+ +|family | | +|:----------|:-----------------------------------------------| +|`gaussian` |with links: `identity`, `log` | +|`binomial` |with links: `logit`, `probit`, `log`, `cloglog` | +|`Gamma` |with links: `inverse`, `identity`, `log` | +|`poisson` |with links: `log`, `identity` | The argument `family` can be provided as character string or as a function. If the link function is omitted, the @@ -157,7 +138,7 @@ linear predictor, in which covariates (independent variables) are separated by An intercept is added automatically (except in proportional hazard models or models for ordinal outcomes). -`survreg_imp()` and `coxph_imp()` expect a +`survreg_imp()` and `coxph_imp()` expect a [survival object](https://stat.ethz.ch/R-manual/R-devel/library/survival/html/Surv.html) (created with `Surv()`) on the left hand side of the model formula. Currently, only right censored data can be handled and there can only be @@ -168,7 +149,7 @@ provide the argument `formula` or the arguments `fixed` and `random`. ### Interactions Interactions between variables can be introduced using `:` or `*`, which adds -the interaction term AND the main effects, i.e., +the interaction term AND the main effects, i.e., ```r SBP ~ age + gender + smoke * creat @@ -187,16 +168,16 @@ mod2a <- glm_imp(educ ~ gender * (age + smoke + creat), data = NHANES, family = binomial(), n.adapt = 0) ``` -The function +The function [`parameters()`](https://nerler.github.io/JointAI/reference/parameters.html) -returns a matrix off all parameters that are specified to be followed +returns a matrix off all parameters that are specified to be followed (column `coef`) and, for regression coefficients, the name of the variable -the coefficient relates to (`varname`), the outcome variable of the +the coefficient relates to (`varname`), the outcome variable of the respective model `outcome`. For multinomial models, which have multiple linear -predictors, the column `outcat` identifies the category of the outcome the -parameters refer to. +predictors, the column `outcat` identifies the category of the outcome the +parameters refer to. -We use the function `parameters()` here and in other vignettes to demonstrate +We use the function `parameters()` here and in other vignettes to demonstrate the effect that different model specifications have. ```r @@ -281,8 +262,8 @@ parameters(mod2c) #> 20 smoke age alpha[7] ``` -In **JointAI**, interactions between any variables, observed or -incomplete, variables on different levels of a hierarchical structure, +In **JointAI**, interactions between any variables, observed or +incomplete, variables on different levels of a hierarchical structure, can be handled. When an incomplete variable is involved, the interaction term is re-calculated within each iteration of the MCMC sampling, using the imputed values from the @@ -297,7 +278,7 @@ match, and results may be incorrect. ### Non-linear functional forms In practice, associations between outcome and covariates do not always meet -the standard assumption that all covariate effects are linear. +the standard assumption that all covariate effects are linear. Often, assuming a logarithmic, quadratic, or other non-linear effect is more appropriate. @@ -306,28 +287,28 @@ functions such as [`log()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/Log.html) (the natural logarithm), [`sqrt()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/MathFun.html) -(the square root) or +(the square root) or [`exp()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/Log.html) (the exponential function). It is also possible to use algebraic operations to calculate a new variable from one or more covariates. To indicate to R that the operators used in the formula -should be interpreted as algebraic operators and not as formula operators, -such calculations need to be wrapped in the function +should be interpreted as algebraic operators and not as formula operators, +such calculations need to be wrapped in the function [`I()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/AsIs.html). -For example, to include a quadratic effect of the variable `x` we would have +For example, to include a quadratic effect of the variable `x` we would have to use `I(x^2)`. Just writing `x^2` would be interpreted as the interaction of `x` with itself, which simplifies to just `x`. -For *completely observed covariates*, **JointAI** can handle any standard type -of function implemented in R. This also includes splines, e.g., using +For *completely observed covariates*, **JointAI** can handle any standard type +of function implemented in R. This also includes splines, e.g., using [`ns()`](https://stat.ethz.ch/R-manual/R-devel/library/splines/html/ns.html) or [`bs()`](https://stat.ethz.ch/R-manual/R-devel/library/splines/html/bs.html) from the package **splines** (which is automatically installed with R). -Functions involving *variables that have missing values* need to be -re-calculated in each iteration of the MCMC sampling. +Functions involving *variables that have missing values* need to be +re-calculated in each iteration of the MCMC sampling. Therefore, currently, only functions that can be interpreted by [JAGS](https://mcmc-jags.sourceforge.io/) can be used for incomplete variables. Those functions include: @@ -340,7 +321,7 @@ Those functions include: multiple (in)complete variables, as long as the formula can be interpreted by [JAGS](https://mcmc-jags.sourceforge.io/). -The list of functions implemented in JAGS can be found in the +The list of functions implemented in JAGS can be found in the [JAGS user manual](https://sourceforge.net/projects/mcmc-jags/files/Manuals/). **Some examples:**^[Note: these examples are chosen to demonstrate functionality @@ -368,9 +349,9 @@ mod3d <- lm_imp(SBP ~ bili + sin(creat) + cos(albu), data = NHANES) #### What happens inside **JointAI**? When a model formula includes a function of a complete or incomplete variable, the main effect of that variable is automatically added as an auxiliary variable. -(For more info on auxiliary variables, see the section +(For more info on auxiliary variables, see the section ["Auxiliary variables"](#auxvars).) -In the linear predictors of models for covariates, usually, only the main +In the linear predictors of models for covariates, usually, only the main effects are used. In `mod3b` from above, for example, the spline of age is used as predictor for @@ -406,7 +387,7 @@ and other parameters by setting `priors`, `regcoef` and `otherpars` to `FALSE`. When a function of a variable is specified as an auxiliary variable, this function is used (as well) in the models for covariates. For example, in `mod3e`, waist circumference (`WC`) -is not part of the model for `SBP`, and the auxiliary variable `I(WC^2)` is +is not part of the model for `SBP`, and the auxiliary variable `I(WC^2)` is used in the linear predictor of the imputation model for `bili`: @@ -499,22 +480,22 @@ list_models(mod3g, priors = FALSE, regcoef = FALSE, otherpars = FALSE) Incomplete variables are always imputed on their original scale, i.e., * in `mod3b` the variable `bili` is imputed and the quadratic and cubic versions -calculated from the imputed values. -* Likewise, `creat` and `albu` in `mod3c` +calculated from the imputed values. +* Likewise, `creat` and `albu` in `mod3c` are imputed separately, and `I(creat/albu^2)` calculated from the imputed (and observed) values. **Important:**
-When different transformations of the same incomplete variable are used in one +When different transformations of the same incomplete variable are used in one model, it is strongly discouraged to calculate these transformations beforehand and to supply them as separate variables. The same is the case for interactions.
If, for example, a model formula contains both `x` and `x2` (where `x2` = `x^2`), they are treated as separate variables and imputed with different models. -Imputed values of `x2` are thus not equal to the square of imputed values of -`x`. +Imputed values of `x2` are thus not equal to the square of imputed values of +`x`. Instead, `x + I(x^2)` should be used in the model formula. Then, only `x` is imputed and used in the linear predictor of models for other incomplete variables, and `x^2` is calculated from the imputed values @@ -523,7 +504,7 @@ of `x`. #### Functions with restricted support -When a function has restricted support, e.g., `log(x)` is only defined for +When a function has restricted support, e.g., `log(x)` is only defined for `x > 0`, the model used to impute `x` needs to comply with these restrictions. This can either be achieved by truncating the distribution assumed for `x`, using the argument `trunc`, or by specifying a model for `x` that meets @@ -537,8 +518,8 @@ use the default model for continuous variables, `"lm"`, a linear model, i.e., assuming a normal distribution and truncate this distribution by specifying `trunc = list(bili = c(, ))` (where the lower and upper limits are the smallest and largest allowed values) or choose a model -(using the argument `models`; more details see the section on -[covariate model types](#meth)) that only imputes positive values such as a +(using the argument `models`; more details see the section on +[covariate model types](#meth)) that only imputes positive values such as a log-normal distribution (`"glm_lognorm"`) or a Gamma distribution (e.g., `"glm_gamma_log"`): @@ -565,7 +546,7 @@ that do exist in JAGS, but not in R, by defining a new function in R that has the name of the function in JAGS. **Example**:
-In JAGS the inverse logit transformation is defined in the function +In JAGS the inverse logit transformation is defined in the function `ilogit`. In R, there is no function `ilogit`, but the inverse logit is available as the distribution function of the logistic distribution `plogis()`. @@ -580,10 +561,10 @@ mod5a <- lm_imp(SBP ~ age + gender + ilogit(creat), data = NHANES) #### Nested functions -It is also possible to nest a function in another function. +It is also possible to nest a function in another function. **Example:**^[Again, this is just a demonstration of the possibilities in JointAI, but nesting -transformations will most often result in coefficients that that do not have +transformations will most often result in coefficients that that do not have meaningful interpretation in practice.] The complementary log log transformation is restricted to values larger than 0 @@ -609,16 +590,16 @@ mod6a <- lm_imp(SBP ~ age + gender + cloglog(ilogit(creat)), data = NHANES) ## Multi-level structure & longitudinal covariates{#MultiLevelStructure} In multi-level models, additional to the fixed effects structure specified by the argument `fixed` a random effects structure needs to be provided via the -argument `random`. +argument `random`. Alternatively, it is possible to provide a `formula` that contains both the -fixed and random effects structure (corresponding to the specification used +fixed and random effects structure (corresponding to the specification used in [**lme4**](https://CRAN.R-project.org/package=lme4)). ### Random effects `random` takes a one-sided formula starting with a `~`. Variables for which a random effect should be included are usually separated by a `+`, and the grouping -variable is separated by `|`. A random intercept is added automatically and +variable is separated by `|`. A random intercept is added automatically and only needs to be specified in a random intercept only model. A few examples: @@ -629,7 +610,7 @@ A few examples: random effect for `time` * `random = ~ time + x | id` random intercept, random slope for `time` and random effect for variable `x` - + The corresponding specifications using the argument `formula` would be * ` + (1 | id)` @@ -655,11 +636,11 @@ needs to be used: It is possible to model both crossed and nested random effects, however the distinction between crossed and nested random effects must come from the coding -of the id variables. +of the id variables. For example, if patients are nested in hospitals, all observations that have the same patient id also need to have the same hospital id. -When this is not the case, i.e., some patients were measured at multiple +When this is not the case, i.e., some patients were measured at multiple hospitals, the random effects are crossed. There is (theoretically) no restriction as to how many grouping levels @@ -667,7 +648,7 @@ are possible. ### Longitudinal covariates From **JointAI** version 0.5.0 onward imputation of longitudinal covariates is -possible. For details the types of models that are available for covariates +possible. For details the types of models that are available for covariates in a multi-level setting, see the section [covariate model types](#meth) below. @@ -677,19 +658,19 @@ When incomplete baseline covariates (level > 1) are involved in the model it is usually necessary to specify models for all variables on lower levels, even if they are completely observed. This is done automatically by **JointAI**, but it may be necessary to change -the default model types to models that better fit the distributions of the +the default model types to models that better fit the distributions of the respective variables. -It is typically not necessary to specify models for variables on higher levels +It is typically not necessary to specify models for variables on higher levels if there are no incomplete covariates on lower levels. For example, in a 2-level setting, if there are no missing values in level-2 -variables, it is not necessary to specify models for completely observed +variables, it is not necessary to specify models for completely observed level-1 variables. But if there are missing values in level-2 variables, models need to be specified for all level-1 variables. #### Why do we need models for completely observed covariates? -The joint distribution of an outcome $y$, covariates $x$, random effects $b$ +The joint distribution of an outcome $y$, covariates $x$, random effects $b$ and parameters $\theta$, $p(y, x, b, \theta)$, is modelled as the product of univariate conditional distributions. To facilitate the specification of these distributions they are ordered so that longitudinal (level-1) variables may have @@ -708,7 +689,7 @@ p(y, x, b, \theta) = & p(y \mid x_1, ..., x_4, b_y, \theta_y) && \text{analysis Since the parameter vectors $\theta_{x1}$, $\theta_{x2}$, ... are assumed to be a priori independent, and furthermore $x_1$ is completely observed and modelled independently of incomplete variables, -estimation of the other model parts is not affected by $p(x_1\mid \theta_{x1})$ +estimation of the other model parts is not affected by $p(x_1\mid \theta_{x1})$ and, hence, this model can be omitted. $p(x_3 \mid x_1, x_2, b_{x3}, \theta_{x3})$, on the other hand is modelled @@ -723,75 +704,27 @@ estimation of parameters in the other parts of the model and could be omitted. ## Covariate model types {#meth} **JointAI** automatically selects models for all incomplete covariates (and if necessary also for some complete covariates). -The type of model is selected automatically based on the `class` of the +The type of model is selected automatically based on the `class` of the variable and the number of levels. The automatically selected types for baseline (highest level) covariates are: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
name model variable type
`lm` linear regression continuous variables
`logit` logistic regression factors with two levels
`mlogit` multinomial logit model unordered factors with >2 levels
`clm` cumulative logit model ordered factors with >2 levels
+ +|name |model |variable type | +|:--------|:-----------------------|:--------------------------------| +|`lm` |linear regression |continuous variables | +|`logit` |logistic regression |factors with two levels | +|`mlogit` |multinomial logit model |unordered factors with >2 levels | +|`clm` |cumulative logit model |ordered factors with >2 levels | The default methods for lower level covariates are: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
name model variable type
`lmm` linear mixed model continuous longitudinal variables
`glmm_logit` logistic mixed model longitudinal factors with two levels
`mlogitmm` multinomial logit mixed model longitudinal unordered factors with >2 levels
`clmm` cumulative logit mixed model longitudinal ordered factors with >2 levels
+ +|name |model |variable type | +|:------------|:-----------------------------|:---------------------------------------------| +|`lmm` |linear mixed model |continuous longitudinal variables | +|`glmm_logit` |logistic mixed model |longitudinal factors with two levels | +|`mlogitmm` |multinomial logit mixed model |longitudinal unordered factors with >2 levels | +|`clmm` |cumulative logit mixed model |longitudinal ordered factors with >2 levels | The imputation models that are chosen by default may not necessarily be @@ -800,66 +733,26 @@ often do not comply with the assumptions of (conditional) normality. Therefore, alternative imputation methods are available for baseline covariates: - - - - - - - - - - - - - - - - - - - - - - - - - -
name model variable type
`lognorm` normal regression of the log-transformed variable right-skewed variables >0
`beta` beta regression (with logit-link) continuous variables with values in [0, 1]
`glm_<family>_<link>` e.g. `glm_gamma_inverse` for Gamma regression with an inverse-link
- -`lognorm` assumes a normal distribution for the natural logarithm of the + +|name |model |variable type | +|:---------------------|:------------------------------------------------------------------|:------------------------------------------| +|`lognorm` |normal regression of the log-transformed variable |right-skewed variables >0 | +|`beta` |beta regression (with logit-link) |continuous variables with values in [0, 1] | +|`glm__` |e.g. `glm_gamma_inverse` for Gamma regression with an inverse-link | | + +`lognorm` assumes a normal distribution for the natural logarithm of the variable, but the variable enters the linear predictor of the analysis model (and possibly other imputation models) on its original scale. For longitudinal (lower-level) covariates corresponding model types are . available: - - - - - - - - - - - - - - - - - - - - - - - - - -
name model variable type
`glmm_lognorm` normal mixed model for the log-transformed variable longitudinal right-skewed variables >0
`glmm_beta` beta regression (with logit-link) continuous variables with values in [0, 1]
`glmm_<family>_<link>` e.g. `glmm_poisson_log` for a poisson mixed model with log-link longitudinal count variables
+ +|name |model |variable type | +|:----------------------|:---------------------------------------------------------------|:------------------------------------------| +|`glmm_lognorm` |normal mixed model for the log-transformed variable |longitudinal right-skewed variables >0 | +|`glmm_beta` |beta regression (with logit-link) |continuous variables with values in [0, 1] | +|`glmm__` |e.g. `glmm_poisson_log` for a poisson mixed model with log-link |longitudinal count variables | Logistic (mixed) models can be abbreviated `glm_logit` (`glmm_logit`). @@ -888,9 +781,9 @@ mod8a$models When there is a "time" variable in the model, such as `age` (age of the child -at the time of the measurement) in the `simLong` +at the time of the measurement) in the `simLong` it may not be meaningful to specify a model for that variable. -Especially when the "time" variable is pre-specified by the design of the study it can +Especially when the "time" variable is pre-specified by the design of the study it can usually be assumed to be independent of the covariates and a model for it has no useful interpretation. @@ -898,7 +791,7 @@ The argument `no_model` allows us to exclude models for such variables (as long as they are completely observed): ```r -mod8b <- lme_imp(bmi ~ GESTBIR + ETHN + HEIGHT_M + SMOKE + hc + MARITAL + +mod8b <- lme_imp(bmi ~ GESTBIR + ETHN + HEIGHT_M + SMOKE + hc + MARITAL + ns(age, df = 2), random = ~ns(age, df = 2) | ID, data = simLong, no_model = "age", n.adapt = 0) @@ -926,10 +819,10 @@ Variables of type `logical` are automatically converted to binary factors. In **JointAI**, the models automatically specified for covariates are ordered by the hierarchical level of the respective response variable (descending). The linear predictor of each model contains the incomplete variables that are -specified later in the sequence and all complete variables of the same or +specified later in the sequence and all complete variables of the same or lower level. -Within each level, models are ordered by the proportion of missing values in +Within each level, models are ordered by the proportion of missing values in the respective response variables, so that the variable with the most missing values has the most covariates in its linear predictor. @@ -1000,13 +893,13 @@ list_models(mod8a, priors = FALSE, regcoef = FALSE, otherpars = FALSE, refcat = ## Auxiliary variables {#auxvars} Auxiliary variables are variables that are not part of the analysis model, but -should be considered as predictor variables in the imputation models because +should be considered as predictor variables in the imputation models because they can inform the imputation of unobserved values. Good auxiliary variables are ^[Van Buuren, S. (2012). Flexible imputation of missing data. Chapman and Hall/CRC. See also the [second edition online](https://stefvanbuuren.name/fimd/).] * associated with an incomplete variable of interest, or are -* associated with the missingness of that variable, and +* associated with the missingness of that variable, and * do not have too many missing values themselves. Importantly, they should be observed for a large proportion of the cases that have a missing value in the variable to be imputed. @@ -1048,7 +941,7 @@ list_models(mod9a, priors = FALSE, regcoef = FALSE, otherpars = FALSE, refcat = ### Functions of variables as auxiliary variables As shown above in [`mod3e`](#mod3e) and [`mod3f`](#mod3e), it is possible to -specify functions of auxiliary variables. In that case, the auxiliary variable +specify functions of auxiliary variables. In that case, the auxiliary variable is not considered as linear effect but as specified by the function: ```r @@ -1158,8 +1051,8 @@ mod10a <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, #### Setting reference categories for individual variables Alternatively, `refcats` takes a named vector, in which the reference category -for each variable can be specified either by its number or its name, or one of -the three global types: "first", "last" or "largest". +for each variable can be specified either by its number or its name, or one of +the three global types: "first", "last" or "largest". For variables for which no reference category is specified in the list the default is used. @@ -1177,7 +1070,7 @@ mod10b <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, To help to specify the reference category, the function [`set_refcat()`](https://nerler.github.io/JointAI/reference/set_refcat.html) -can be used. +can be used. It prints the names of the categorical variables that are selected by * a specified model formula and/or @@ -1185,7 +1078,7 @@ It prints the names of the categorical variables that are selected by * a vector of naming covariates or all categorical variables in the data if only `data` is provided, -and asks a number of questions which the user needs to reply to by input of +and asks a number of questions which the user needs to reply to by input of a number. @@ -1213,8 +1106,8 @@ When option 4 is chosen, a question for each categorical variable is asked, for example: ```r -#> The reference category for “race” should be -#> +#> The reference category for “race” should be +#> #> 1: Mexican American #> 2: Other Hispanic #> 3: Non-Hispanic White @@ -1229,7 +1122,7 @@ the determined specification for the argument `refcats` is printed: #> In the JointAI model specify: #> refcats = c(gender = 'female', race = 'Non-Hispanic White', educ = 'low', #> occup = 'not working', smoke = 'never') -#> +#> #> or use the output of this function. ``` @@ -1242,22 +1135,22 @@ the determined specification for the argument `refcats` is printed: mod10c <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, refcats = refs_mod10, data = NHANES, n.adapt = 0) #> Warning: -#> It is currently not possible to use "contr.poly" for incomplete categorical covariates. I will use -#> "contr.treatment" instead. You can specify (globally) which types of contrasts are used by changing -#> "options('contrasts')". +#> It is currently not possible to use "contr.poly" for incomplete categorical covariates. +#> I will use "contr.treatment" instead. You can specify (globally) which types of +#> contrasts are used by changing "options('contrasts')". ``` **Note:**
Changing a reference category via the argument `refcats` does not change the order of levels in the dataset or any of the data matrices inside **JointAI**. -Only when, in the JAGS model, the categorical variables is converted into -dummy variables, the reference category is used to determine for which levels +Only when, in the JAGS model, the categorical variables is converted into +dummy variables, the reference category is used to determine for which levels the dummies are created. ## Hyper-parameters -In the Bayesian framework, parameters are random variables for which a +In the Bayesian framework, parameters are random variables for which a distribution needs to be specified. These distributions depend on parameters themselves, i.e., on hyper-parameters. @@ -1304,8 +1197,8 @@ default_hyperpars() #> 0.000 0.001 ``` -To change the hyper-parameters in a **JointAI** model, the list obtained from -`default_hyperpars()` can be edited and passed to the argument `hyperpars` +To change the hyper-parameters in a **JointAI** model, the list obtained from +`default_hyperpars()` can be edited and passed to the argument `hyperpars` in the main functions `*_imp()`. * `mu_reg_*` and `tau_reg_*` refer to the mean and precision in the @@ -1318,7 +1211,7 @@ in the main functions `*_imp()`. of degrees of freedom depending on the number of random effects `nranef` (dimension of `D`). By default, `KinvD` will be set to the number of random effects plus one. -* `shape_diag_RinvD` and `rate_diag_RinvD` are the scale and rate parameters +* `shape_diag_RinvD` and `rate_diag_RinvD` are the scale and rate parameters of the Gamma prior of the diagonal elements of `RinvD`. In random effects models with only one random effect, instead of the Wishart @@ -1327,7 +1220,7 @@ distribution a Gamma prior is used for the inverse of `D`. ## Scaling When variables are measured on very different scales this can result in slow -convergence and bad mixing. +convergence and bad mixing. Therefore, **JointAI** includes scaling of continuous covariates in the JAGS model (i.e., instead of writing `... + covar + ...` in the linear predictor, `... + (covar - mean)/sd) + ...` is written). @@ -1340,7 +1233,7 @@ If `scale_vars` is a vector of variable names, scaling will only be done for those variables. By default, only the MCMC samples that is scaled back to the scale of the data -is stored in a `JointAI` object. When the argument `keep_scaled_mcmc = TRUE` +is stored in a `JointAI` object. When the argument `keep_scaled_mcmc = TRUE` also the scaled sample is kept. This is mainly for de-bugging purposes. @@ -1349,13 +1242,13 @@ It is possible to use shrinkage priors to penalize large regression coefficients. This can be specified via the argument `shrinkage`. At the moment, only ridge regression is implemented. -Setting `shrinkage = 'ridge'` will impose ridge priors on all regression +Setting `shrinkage = 'ridge'` will impose ridge priors on all regression coefficients. To only use shrinkage for some of the sub-models (main analysis model and covariate models), a vector can be provided that contains the names of the response variables of the models in which shrinkage should be applied, and the type of shrinkage for each of them. -For example, in `mod11a` ridge regression is used for all models, and in +For example, in `mod11a` ridge regression is used for all models, and in `modd11b` only in the models for `SBP` and `educ`: ```r @@ -1373,5 +1266,5 @@ mod11b <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, ``` Ridge regression is implemented as a $\text{Ga}(0.01, 0.01)$ prior for the -precision of the regression coefficients $\beta$ instead of setting this +precision of the regression coefficients $\beta$ instead of setting this precision to a fixed (small) value. diff --git a/vignettes/SelectingParameters.Rmd b/vignettes/SelectingParameters.Rmd index 15395403..00872a7b 100644 --- a/vignettes/SelectingParameters.Rmd +++ b/vignettes/SelectingParameters.Rmd @@ -48,104 +48,31 @@ have an argument `monitor_params`. `monitor_params` takes a named list (often a named vector also works) with the following possible entries: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
name/key word what is monitored
`analysis_main` `betas` and `sigma_main`, `tau_main` (in beta regression) or `shape_main` (in parametric survival models), `D_main` (in multi-level models) and `basehaz` (in proportional hazards models)
`analysis_random` `ranef_main`, `D_main`, `invD_main`, `RinvD_main`
`other_models` `alphas`, `tau_other`, `gamma_other`, `delta_other`
`imps` imputed values
`betas` regression coefficients of the main analysis model(s)
`tau_main` precision of the residuals from the analysis model(s)
`sigma_main` standard deviation of the residuals from the analysis model(s)
`gamma_main` intercepts in ordinal main model
`delta_main` increments of ordinal intercepts in main model(s)
`ranef_main` random effects of the analysis model(s)
`D_main` covariance matrix of the random effects of the main model(s)
`invD_main` inverse of `D_main`
`RinvD_main` scale matrix in Wishart prior for `invD_main`
`alphas` regression coefficients in the covariate models
`tau_other` precision parameters of the residuals from covariate models
`gamma_other` intercepts in ordinal covariate models
`delta_other` increments of ordinal intercepts in covariate models
`ranef_other` random effects of the covariate model(s)
`D_other` covariance matrix of the random effects of the covariate model(s)
`invD_other` inverse of `D_other`
`RinvD_other` scale matrix in Wishart prior for `invD_other`
`other` additional parameters
+ +|name/key word |what is monitored | +|:-----------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +|`analysis_main` |`betas` and `sigma_main`, `tau_main` (in beta regression) or `shape_main` (in parametric survival models), `D_main` (in multi-level models) and `basehaz` (in proportional hazards models) | +|`analysis_random` |`ranef_main`, `D_main`, `invD_main`, `RinvD_main` | +|`other_models` |`alphas`, `tau_other`, `gamma_other`, `delta_other` | +|`imps` |imputed values | +|`betas` |regression coefficients of the main analysis model(s) | +|`tau_main` |precision of the residuals from the analysis model(s) | +|`sigma_main` |standard deviation of the residuals from the analysis model(s) | +|`gamma_main` |intercepts in ordinal main model | +|`delta_main` |increments of ordinal intercepts in main model(s) | +|`ranef_main` |random effects of the analysis model(s) | +|`D_main` |covariance matrix of the random effects of the main model(s) | +|`invD_main` |inverse of `D_main` | +|`RinvD_main` |scale matrix in Wishart prior for `invD_main` | +|`alphas` |regression coefficients in the covariate models | +|`tau_other` |precision parameters of the residuals from covariate models | +|`gamma_other` |intercepts in ordinal covariate models | +|`delta_other` |increments of ordinal intercepts in covariate models | +|`ranef_other` |random effects of the covariate model(s) | +|`D_other` |covariance matrix of the random effects of the covariate model(s) | +|`invD_other` |inverse of `D_other` | +|`RinvD_other` |scale matrix in Wishart prior for `invD_other` | +|`other` |additional parameters | Each of the key words works as a switch, except for `other`, which should be a vector of character strings. @@ -421,52 +348,18 @@ id variable for each observation in cross-sectional data (multi-level data shoul already contain an id variable). `get_MIdat()` takes the arguments: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
argument explanation
`object` 'an object of class 'JointAI'
`m` number of datasets to be created
`include` logical; should the original data be included?
`start` the first iteration that may be randomly chosen (i.e., all previous iterations are discarded as burn-in)
`minspace` minimum number of iterations between iterations chosen as imputed values
`seed` optional seed value in order to make the random selection of iterations reproducible
`export_to_SPSS` logical; should the datasets be exported to SPSS, i.e., written as .txt and .sps file? If `export_to_SPSS = FALSE` (default) the imputed data is only returned `data.frame`
`resdir` directory the files are exported to
`filename` the name of the .txt and .sps files
+ +|argument |explanation | +|:----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +|`object` |'an object of class 'JointAI' | +|`m` |number of datasets to be created | +|`include` |logical; should the original data be included? | +|`start` |the first iteration that may be randomly chosen (i.e., all previous iterations are discarded as burn-in) | +|`minspace` |minimum number of iterations between iterations chosen as imputed values | +|`seed` |optional seed value in order to make the random selection of iterations reproducible | +|`export_to_SPSS` |logical; should the datasets be exported to SPSS, i.e., written as .txt and .sps file? If `export_to_SPSS = FALSE` (default) the imputed data is only returned `data.frame` | +|`resdir` |directory the files are exported to | +|`filename` |the name of the .txt and .sps files | ### Random effects For mixed models, `analysis_main` also includes the random effects covariance From 9e2bfcf9822c20568f0816962e047eef966f78c7 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 15 Feb 2022 15:12:25 +0100 Subject: [PATCH 093/176] update news.md --- NEWS.md | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/NEWS.md b/NEWS.md index 0a6ac590..b3b9c809 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,13 +2,24 @@ ## New features -* `md_pattern()` has an additional argument `sort_columns` to provide the option to switch off the sorting of columns by number of missing values. +* `md_pattern()` has an additional argument `sort_columns` to provide the option + to switch off the sorting of columns by number of missing values. -## Bug fixes +## Bug fixes and improvements * `formula()` did not return a formula when `add_samples()` was used. * Use of `add_samples()` will now result in the `call` element of a `JointAI` object being a `list` and no longer a nested list. +* `JM_imp()` with ordinal longitudinal outcome is not using the correct + version of the linear predictor in the quadrature procedure approximating the + integral in the survival. +* Re-structuring of the JAGS model code for proportional hazards models to + reduce computational time. +* change in how parallel computation is done: does not rely on **doFuture** and + **foreach** any more, only package **future** is required. +* `comp_info` element of fitted `JointAI` object has changed due to the changes + in parallel computing, and computational time is not reported separately for + the adaptive phase and the sampling phase. ----------------------------------------------------------------------------- From 3c61961987eec0d691b4aedc8b7b3ac930521c4b Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 17 Feb 2022 12:41:43 +0100 Subject: [PATCH 094/176] bugfix: when there are no mcmc samples, as.mcmc.list will return an error. This has to be recorded, but not cause a stop. --- R/helpfunctions_JAGS.R | 2 +- R/model_imp.R | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/helpfunctions_JAGS.R b/R/helpfunctions_JAGS.R index 84a2293f..aa559aca 100644 --- a/R/helpfunctions_JAGS.R +++ b/R/helpfunctions_JAGS.R @@ -140,7 +140,7 @@ run_parallel <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, res <- lapply(out, future::value) - mcmc <- coda::as.mcmc.list(lapply(res, function(x) x$mcmc[[1L]])) + mcmc <- try(coda::as.mcmc.list(lapply(res, function(x) x$mcmc[[1L]]))) time_adapt <- max(do.call(c, lapply(res, "[[", "time_adapt"))) time_sample <- max(do.call(c, lapply(res, "[[", "time_sample"))) diff --git a/R/model_imp.R b/R/model_imp.R index b3b3264c..0f40bd9f 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -831,7 +831,7 @@ model_imp <- function(formula = NULL, fixed = NULL, data, random = NULL, warnmsg("There is no mcmc sample. Something went wrong.") # post processing ------------------------------------------------------------ - if (n.iter > 0 & !is.null(mcmc)) { + if (n.iter > 0 & !is.null(mcmc) & !inherits(mcmc, "try-error")) { MCMC <- mcmc if (any(!vapply(Mlist$scale_pars, is.null, FUN.VALUE = logical(1)), From 37b897156abc50f7a48204c10461423af4585f5e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 17 Feb 2022 14:44:39 +0100 Subject: [PATCH 095/176] update documentation JointAIobject wrt comp_info element; added R.version.string --- R/JointAIObject.R | 11 ++++++----- R/model_imp.R | 1 + man/JointAIObject.Rd | 11 ++++++----- 3 files changed, 13 insertions(+), 10 deletions(-) diff --git a/R/JointAIObject.R b/R/JointAIObject.R index ae76b38e..ff24922e 100644 --- a/R/JointAIObject.R +++ b/R/JointAIObject.R @@ -96,13 +96,14 @@ #' \code{keep_scaled_sample = TRUE})} #' \item{\code{MCMC}}{MCMC sample, scaled back to the scale of the data} #' \item{\code{comp_info}}{a list with information on the computational setting -#' (\code{start_ime}: date and time the calculation was +#' (\code{start_time}: date and time the calculation was #' started, \code{duration}: computational time of the -#' model (adaptive + sampling phase), +#' model adaptive and sampling phase, #' \code{JointAI_version}: package version, -#' \code{future}: the call to \code{future::plan()}, if -#' any was found (i.e., the specification for parallel -#' computation))} +#' \code{R_version}: the \code{R.version.string}, +#' \code{parallel}: whether parallel computation was used, +#' \code{workers}: if parallel computation was used, the +#' number of workers)} #' \item{\code{fitted.values}}{fitted/predicted values (if available)} #' \item{\code{residuals}}{residuals (if available)} #' \item{\code{call}}{the original call} diff --git a/R/model_imp.R b/R/model_imp.R index 0f40bd9f..b5b376f8 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -902,6 +902,7 @@ model_imp <- function(formula = NULL, fixed = NULL, data, random = NULL, list("adapt" = jags_res$time_adapt, "sample" = jags_res$time_sample), JointAI_version = packageVersion("JointAI"), + R_version = R.version.string, parallel = if (!is.null(jags_res)) jags_res$parallel, workers = if (isTRUE(jags_res$parallel)) jags_res$workers), diff --git a/man/JointAIObject.Rd b/man/JointAIObject.Rd index 252ffb2b..afd73994 100644 --- a/man/JointAIObject.Rd +++ b/man/JointAIObject.Rd @@ -94,13 +94,14 @@ corresponding covariates} \code{keep_scaled_sample = TRUE})} \item{\code{MCMC}}{MCMC sample, scaled back to the scale of the data} \item{\code{comp_info}}{a list with information on the computational setting -(\code{start_ime}: date and time the calculation was +(\code{start_time}: date and time the calculation was started, \code{duration}: computational time of the -model (adaptive + sampling phase), +model adaptive and sampling phase, \code{JointAI_version}: package version, -\code{future}: the call to \code{future::plan()}, if -any was found (i.e., the specification for parallel -computation))} +\code{R_version}: the \code{R.version.string}, +\code{parallel}: whether parallel computation was used, +\code{workers}: if parallel computation was used, the +number of workers)} \item{\code{fitted.values}}{fitted/predicted values (if available)} \item{\code{residuals}}{residuals (if available)} \item{\code{call}}{the original call} From 19cff5a41d51eb40a1d1f53d0d67bd9634393dd4 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 17 Feb 2022 15:11:52 +0100 Subject: [PATCH 096/176] bugfix: selection of elements of data matrices to monitor when monitor_params = c(imps = TRUE) --- NEWS.md | 12 ++++++++---- R/get_params.R | 6 +++--- 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/NEWS.md b/NEWS.md index b3b9c809..8a0209f7 100644 --- a/NEWS.md +++ b/NEWS.md @@ -6,13 +6,17 @@ to switch off the sorting of columns by number of missing values. -## Bug fixes and improvements -* `formula()` did not return a formula when `add_samples()` was used. -* Use of `add_samples()` will now result in the `call` element of a `JointAI` - object being a `list` and no longer a nested list. +## Bug fixes +* `formula()` now also return the model formula when `add_samples()` is used. * `JM_imp()` with ordinal longitudinal outcome is not using the correct version of the linear predictor in the quadrature procedure approximating the integral in the survival. +* Bug causing the wrong elements of the data matrix to be monitored when + `monitor_params(imps = TRUE)` in survival models fixed. + +## Small improvements +* Use of `add_samples()` will now result in the `call` element of a `JointAI` + object being a `list` and no longer a nested list. * Re-structuring of the JAGS model code for proportional hazards models to reduce computational time. * change in how parallel computation is done: does not rely on **doFuture** and diff --git a/R/get_params.R b/R/get_params.R index 65d74bce..37332558 100644 --- a/R/get_params.R +++ b/R/get_params.R @@ -78,9 +78,9 @@ get_params <- function(analysis_main = TRUE, impvals <- if (isTRUE(args$imps)) { unlist(unname(lapply(names(Mlist$M), function(k) { if (any(is.na(Mlist$M[[k]]))) { - misvals <- which(is.na(Mlist$M[[k]][, colnames(Mlist$M[[k]]) %in% - names(Mlist$data), drop = FALSE]), - arr.ind = TRUE) + misvals <- which(is.na(Mlist$M[[k]]), arr.ind = TRUE) + relevant_cols <- which(colnames(Mlist$M[[k]]) %in% names(Mlist$data)) + misvals <- misvals[misvals[, "col"] %in% relevant_cols, ] apply(misvals, 1L, function(x) { paste0(k, "[", x[1L], ",", x[2L], "]") From 203bef966c570a8c9a841a9a7a28c7fb33e9e89e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 21 Feb 2022 15:45:49 +0100 Subject: [PATCH 097/176] improve efficiency of calculating predicted values for coxph models --- NEWS.md | 1 + R/predict.R | 48 +++++++++++++++++++++++++----------------------- 2 files changed, 26 insertions(+), 23 deletions(-) diff --git a/NEWS.md b/NEWS.md index 8a0209f7..70c741ce 100644 --- a/NEWS.md +++ b/NEWS.md @@ -24,6 +24,7 @@ * `comp_info` element of fitted `JointAI` object has changed due to the changes in parallel computing, and computational time is not reported separately for the adaptive phase and the sampling phase. +* `predict()` is now faster for proportional hazards models. ----------------------------------------------------------------------------- diff --git a/R/predict.R b/R/predict.R index d53121fc..ab5e4250 100644 --- a/R/predict.R +++ b/R/predict.R @@ -535,28 +535,25 @@ predict_coxph <- function(Mlist, coef_list, MCMC, newdata, data, info_list, } lp_list <- lapply(desgn_mat, function(x) { - vapply(seq_len(nrow(x)), function(i) { - if (!is.null(scale_pars)) { - MCMC[, coefs$coef[match(colnames(x), coefs$varname)], drop = FALSE] %*% - (x[i, ] - scale_pars$center[match(colnames(x), rownames(scale_pars))]) - } else { - MCMC[, coefs$coef[match(colnames(x), - coefs$varname)], drop = FALSE] %*% x[i, ] - } - }, FUN.VALUE = numeric(nrow(MCMC))) + if (!is.null(scale_pars)) { + MCMC[, coefs$coef[match(colnames(x), coefs$varname)], drop = FALSE] %*% + (t(x) - scale_pars$center[match(colnames(x), rownames(scale_pars))]) + } else { + t(x) %*% MCMC[, coefs$coef[match(colnames(x), coefs$varname)], + drop = FALSE] + } }) - lps <- array(unlist(lp_list), dim = c(nrow(lp_list[[1L]]), - ncol(lp_list[[1L]]), - length(lp_list)), - dimnames = list(NULL, NULL, gsub("M_", "", names(lp_list)))) - eta_surv <- if (any(Mlist$group_lvls >= Mlist$group_lvls[gsub("M_", "", resp_mat)])) { - apply(lps[, , names(which(Mlist$group_lvls >= - Mlist$group_lvls[gsub("M_", "", resp_mat)]))], - c(1L, 2L), sum) + + lvls <- names(which(Mlist$group_lvls >= + Mlist$group_lvls[gsub("M_", "", resp_mat)])) + + Reduce(function(x1, x2) x1 + x2, + lp_list[paste0("M_", lvls)]) + } else { 0L } @@ -564,9 +561,13 @@ predict_coxph <- function(Mlist, coef_list, MCMC, newdata, data, info_list, eta_surv_long <- if (any(Mlist$group_lvls < Mlist$group_lvls[gsub("M_", "", resp_mat)]) & survinfo[[1L]]$haslong) { - apply(lps[, , names(which(Mlist$group_lvls < - Mlist$group_lvls[gsub("M_", "", resp_mat)]))], - c(1L, 2L), sum) + + lvls <- names(which(Mlist$group_lvls < + Mlist$group_lvls[gsub("M_", "", resp_mat)])) + + Reduce(function(x1, x2) x1 + x2, + lp_list[paste0("M_", lvls)]) + } else { 0L } @@ -597,10 +598,11 @@ predict_coxph <- function(Mlist, coef_list, MCMC, newdata, data, info_list, gkx + 1L))), ord = 4L, outer.ok = TRUE) + mcmc_cols <- grep(paste0("\\bbeta_Bh0_", clean_survname(varname), "\\b"), + colnames(MCMC)) + logh0s <- lapply(seq_len(nrow(MCMC)), function(m) { - matrix(Bsh0 %*% MCMC[m, grep(paste0("\\bbeta_Bh0_", - clean_survname(varname), "\\b"), - colnames(MCMC))], + matrix(Bsh0 %*% MCMC[m, mcmc_cols], ncol = 15L, nrow = nrow(newdata), byrow = TRUE) }) From 1a8ab45b0f227115ac08725348652dc85fc2ab2c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 2 Mar 2022 15:52:59 +0100 Subject: [PATCH 098/176] fix oversight: comp_info element should be called "parallel" not "future" --- R/add_samples.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/add_samples.R b/R/add_samples.R index 8ecc3c1e..1892f148 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -130,7 +130,7 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, newobject$MCMC <- newMCMC newobject$call <- c(object$call, match.call()) newobject$mcmc_settings$variable.names <- var_names - newobject$comp_info$future <- c(object$comp_info$parallel, + newobject$comp_info$parallel <- c(object$comp_info$parallel, jags_res$parallel) newobject$model <- if (isTRUE(jags_res$parallel)) { adapt From b93c7291642c539d34ac700eb49b7e445168ba58 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 2 Mar 2022 15:53:47 +0100 Subject: [PATCH 099/176] bugfix for monitoring RinvD in models with random effects on multiple levels (nranef is then a vector not a scalar) --- R/get_params.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/get_params.R b/R/get_params.R index 37332558..88b7db72 100644 --- a/R/get_params.R +++ b/R/get_params.R @@ -304,15 +304,15 @@ get_ranefpars <- function(info_list, Mlist, args, set = "main") { }) RinvD_block_indep <- lapply(ranef_info, function(x) { - if (x$nranef > 1) { - lapply(x$lvls, function(lvl) { + lapply(x$lvls, function(lvl) { + if (x$nranef[lvl] > 1) { if (isTRUE(x$rd_vcov[[lvl]] == "blockdiag")) { paste0("RinvD_", x$varname, "_", lvl, "[", seq.int(max(1L, x$nranef[lvl])), ",", seq.int(max(1L, x$nranef[lvl])), "]") } - }) - } + } + }) }) params <- c(params, From 45b3530c9abdc95868ffec12ca88e23569f1dfe0 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 2 Mar 2022 15:55:07 +0100 Subject: [PATCH 100/176] take start time at beginning of main function --- R/model_imp.R | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/R/model_imp.R b/R/model_imp.R index b5b376f8..96772ee8 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -717,7 +717,7 @@ model_imp <- function(formula = NULL, fixed = NULL, data, random = NULL, append_data_list = NULL, ...) { modimpcall <- as.list(match.call())[-1L] - + start_time <- Sys.time() # checks & warnings ------------------------------------------------------- if (!is.null(formula) & is.null(fixed) & is.null(random)) { @@ -897,10 +897,12 @@ model_imp <- function(formula = NULL, fixed = NULL, data, random = NULL, model = if (n.adapt > 0) adapt, sample = if (n.iter > 0 & !is.null(mcmc) & keep_scaled_mcmc) mcmc, MCMC = if (n.iter > 0 & !is.null(mcmc)) coda::as.mcmc.list(MCMC), - comp_info = list(start_time = Sys.time(), - duration = if (!is.null(jags_res)) - list("adapt" = jags_res$time_adapt, - "sample" = jags_res$time_sample), + comp_info = list(start_time = start_time, + duration = if (!is.null(jags_res)) { + duration_obj( + list("adapt" = jags_res$time_adapt, + "sample" = jags_res$time_sample)) + }, JointAI_version = packageVersion("JointAI"), R_version = R.version.string, parallel = if (!is.null(jags_res)) jags_res$parallel, From 2e00dcc03a1d2d899ca4d07fda71686ba73f794e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 2 Mar 2022 15:55:46 +0100 Subject: [PATCH 101/176] don't use maximum of computation time when parallel chains are used, but keep time per chain --- R/helpfunctions_JAGS.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/helpfunctions_JAGS.R b/R/helpfunctions_JAGS.R index aa559aca..e2425b5c 100644 --- a/R/helpfunctions_JAGS.R +++ b/R/helpfunctions_JAGS.R @@ -141,8 +141,8 @@ run_parallel <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, res <- lapply(out, future::value) mcmc <- try(coda::as.mcmc.list(lapply(res, function(x) x$mcmc[[1L]]))) - time_adapt <- max(do.call(c, lapply(res, "[[", "time_adapt"))) - time_sample <- max(do.call(c, lapply(res, "[[", "time_sample"))) + time_adapt <- do.call(c, lapply(res, "[[", "time_adapt")) + time_sample <- do.call(c, lapply(res, "[[", "time_sample")) list(adapt = lapply(res, "[[", "adapt"), mcmc = mcmc, From b320eaac734fcf026efd63819c621e3c6d41fb77 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 2 Mar 2022 15:56:35 +0100 Subject: [PATCH 102/176] change format of comp_info$duration --- NEWS.md | 6 +++-- R/add_samples.R | 7 ++--- R/helpfunctions_JAGS.R | 58 ++++++++++++++++++++++++++++++++++++---- man/difftime_df.Rd | 15 +++++++++++ man/duration_obj.Rd | 15 +++++++++++ man/reformat_difftime.Rd | 15 +++++++++++ 6 files changed, 106 insertions(+), 10 deletions(-) create mode 100644 man/difftime_df.Rd create mode 100644 man/duration_obj.Rd create mode 100644 man/reformat_difftime.Rd diff --git a/NEWS.md b/NEWS.md index 70c741ce..74eb4533 100644 --- a/NEWS.md +++ b/NEWS.md @@ -23,8 +23,10 @@ **foreach** any more, only package **future** is required. * `comp_info` element of fitted `JointAI` object has changed due to the changes in parallel computing, and computational time is not reported separately for - the adaptive phase and the sampling phase. -* `predict()` is now faster for proportional hazards models. + the adaptive phase and the sampling phase, and separately per chain when + parallel computation was used. +* `predict()` is now a lot faster for proportional hazards models. + ----------------------------------------------------------------------------- diff --git a/R/add_samples.R b/R/add_samples.R index 1892f148..2c3c368e 100644 --- a/R/add_samples.R +++ b/R/add_samples.R @@ -145,9 +145,10 @@ add_samples <- function(object, n.iter, add = TRUE, thin = NULL, coda::thin(newMCMC)) # add computational time to JointAI object - newobject$comp_info$duration <- c(object$comp_info$duration, - list("adapt" = jags_res$time_adapt, - "sample" = jags_res$time_sample)) + newobject$comp_info$duration <- rbind_duration( + object$comp_info$duration, + list("adapt" = jags_res$time_adapt, + "sample" = jags_res$time_sample)) return(newobject) } diff --git a/R/helpfunctions_JAGS.R b/R/helpfunctions_JAGS.R index e2425b5c..88cb8e6e 100644 --- a/R/helpfunctions_JAGS.R +++ b/R/helpfunctions_JAGS.R @@ -78,7 +78,9 @@ run_jags <- function(inits, data_list, modelfile, n_chains, n_adapt, n_iter, } t2 <- Sys.time() - list(adapt = adapt, mcmc = mcmc, time_adapt = t1 - t0, time_sample = t2 - t1) + list(adapt = adapt, mcmc = mcmc, + time_adapt = t1 - t0, + time_sample = t2 - t1) } @@ -144,10 +146,11 @@ run_parallel <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, time_adapt <- do.call(c, lapply(res, "[[", "time_adapt")) time_sample <- do.call(c, lapply(res, "[[", "time_sample")) - list(adapt = lapply(res, "[[", "adapt"), - mcmc = mcmc, - time_adapt = reformat_difftime(time_adapt), - time_sample = reformat_difftime(time_sample)) + list(adapt = lapply(res, "[[", "adapt"), + mcmc = mcmc, + time_adapt = difftime_df(reformat_difftime(time_adapt)), + time_sample = difftime_df(reformat_difftime(time_sample)) + ) } else { @@ -160,13 +163,22 @@ run_parallel <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, mess = mess, progress_bar = progress_bar, add_samples = add_samples, adapt = models) } + fit$parallel <- parallel fit$workers <- length(f$workers) + + if (!isTRUE(parallel)) { + fit$time_adapt <- difftime_df(fit$time_adapt) + fit$time_sample <- difftime_df(fit$time_sample) + } fit } } +#' Set all elements of a `difftime` object to the same, largest meaningful unit +#' @param dt a `difftime` object (potentially a vector of `difftime`s) +#' @keywords internal reformat_difftime <- function(dt) { units(dt) <- "secs" w <- which(min(dt)/c(secs = 1, mins = 60, hours = 3600, days = 86400) > 1L) @@ -174,3 +186,39 @@ reformat_difftime <- function(dt) { units(dt) <- names(w)[length(w)] dt } + + +#' Converts a `difftime` object to a `data.frame` +#' @param dt `difftime` object (vector of `difftime` objects) +#' @keywords internal +difftime_df <- function(dt) { + if (length(dt) > 1L) { + dt <- setNames(dt, paste0("chain", seq_along(dt))) + } else { + dt <- setNames(dt, "total") + } + as.data.frame(as.list(dt)) +} + + +rbind_duration <- function(dur, dur_new) { + Map(function(dur_old, dur_new) { + rownames(dur_new) <- paste0("run ", nrow(dur_old) + 1) + rbind(dur_old, dur_new) + }, dur_old = dur, dur_new = dur_new) +} + + +#' Create a duration object +#' +#' Add row names to the object +#' +#' @param dur list of `difftime` objects +#' @keywords internal +duration_obj <- function(dur) { + lapply(dur, function(x) { + rownames(x) <- paste0("run ", 1:nrow(x)) + x + }) +} + diff --git a/man/difftime_df.Rd b/man/difftime_df.Rd new file mode 100644 index 00000000..8e0cc62b --- /dev/null +++ b/man/difftime_df.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGS.R +\name{difftime_df} +\alias{difftime_df} +\title{Converts a \code{difftime} object to a \code{data.frame}} +\usage{ +difftime_df(dt) +} +\arguments{ +\item{dt}{\code{difftime} object (vector of \code{difftime} objects)} +} +\description{ +Converts a \code{difftime} object to a \code{data.frame} +} +\keyword{internal} diff --git a/man/duration_obj.Rd b/man/duration_obj.Rd new file mode 100644 index 00000000..d914b7bc --- /dev/null +++ b/man/duration_obj.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGS.R +\name{duration_obj} +\alias{duration_obj} +\title{Create a duration object} +\usage{ +duration_obj(dur) +} +\arguments{ +\item{dur}{list of \code{difftime} objects} +} +\description{ +Add row names to the object +} +\keyword{internal} diff --git a/man/reformat_difftime.Rd b/man/reformat_difftime.Rd new file mode 100644 index 00000000..787d6851 --- /dev/null +++ b/man/reformat_difftime.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGS.R +\name{reformat_difftime} +\alias{reformat_difftime} +\title{Set all elements of a \code{difftime} object to the same, largest meaningful unit} +\usage{ +reformat_difftime(dt) +} +\arguments{ +\item{dt}{a \code{difftime} object (potentially a vector of \code{difftime}s)} +} +\description{ +Set all elements of a \code{difftime} object to the same, largest meaningful unit +} +\keyword{internal} From 41c245acd2bafa497afb10f127becc311077072e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 2 Mar 2022 15:58:52 +0100 Subject: [PATCH 103/176] new function to add computational time across chains, phases, ... --- NAMESPACE | 1 + NEWS.md | 3 +- R/helpfunctions_JAGS.R | 62 ++++++++++++++++++++++++++++++++++++++++++ man/sum_duration.Rd | 22 +++++++++++++++ 4 files changed, 87 insertions(+), 1 deletion(-) create mode 100644 man/sum_duration.Rd diff --git a/NAMESPACE b/NAMESPACE index e3651e51..1d2deae5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -61,6 +61,7 @@ export(plot_imp_distr) export(predDF) export(rd_vcov) export(set_refcat) +export(sum_duration) export(survreg_imp) export(traceplot) import(future) diff --git a/NEWS.md b/NEWS.md index 74eb4533..e022f395 100644 --- a/NEWS.md +++ b/NEWS.md @@ -4,7 +4,8 @@ ## New features * `md_pattern()` has an additional argument `sort_columns` to provide the option to switch off the sorting of columns by number of missing values. - +* `sum_duration()`: new function to get sum of computational time across chains, + phases, runs, ... ## Bug fixes * `formula()` now also return the model formula when `add_samples()` is used. diff --git a/R/helpfunctions_JAGS.R b/R/helpfunctions_JAGS.R index 88cb8e6e..1e7a0f50 100644 --- a/R/helpfunctions_JAGS.R +++ b/R/helpfunctions_JAGS.R @@ -222,3 +222,65 @@ duration_obj <- function(dur) { }) } + +#' Calculate the sum of the computational duration of a JointAI object +#' +#' @param object object of class `JointAI` +#' @param by optional grouping information; options are `NULL` (default) to +#' calculate the sum over all chains and runs and both the adaptive +#' and sampling phase, `"run"` to get the duration per run, +#' `"phase"` to get the sum over all chains and runs per phase, +#' `"chain"` to get the sum per chain over both phases and all runs, +#' `"phase and run"` to get the sum over all chains, separately per +#' phase and run. +#' +#' @export +#' +sum_duration <- function(object, by = NULL) { + obj <- object$comp_info$duration + + if (is.null(by) | by %in% c("phase", "phase and run", "run and phase")) { + + s <- lapply(obj, function(p) { + + r <- Map(function(vec, parallel) { + if (parallel) { + max(do.call(c, vec)) + } else { + sum(do.call(c, vec)) + } + }, vec = split(p, rownames(p)), parallel = object$comp_info$parallel) + + do.call(c, r) + }) + + + if (by %in% c("phase and run", "run and phase")) { + s + } else if (is.null(by)) { + sum(do.call(c, s)) + } else if (by == "phase") { + lapply(s, sum) + } + + } else if (by == "run") { + + r <- do.call(cbind, obj) + + s <- Map(function(vec, parallel) { + if (parallel) { + max(do.call(c, vec)) + } else { + sum(do.call(c, vec)) + } + }, vec = split(r, rownames(r)), + parallel = object$comp_info$parallel) + + do.call(c, s) + + } else if (by == "chain") { + do.call(c, lapply(c(do.call(rbind, obj)), sum)) + } +} + + diff --git a/man/sum_duration.Rd b/man/sum_duration.Rd new file mode 100644 index 00000000..2371aa93 --- /dev/null +++ b/man/sum_duration.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpfunctions_JAGS.R +\name{sum_duration} +\alias{sum_duration} +\title{Calculate the sum of the computational duration of a JointAI object} +\usage{ +sum_duration(object, by = NULL) +} +\arguments{ +\item{object}{object of class \code{JointAI}} + +\item{by}{optional grouping information; options are \code{NULL} (default) to +calculate the sum over all chains and runs and both the adaptive +and sampling phase, \code{"run"} to get the duration per run, +\code{"phase"} to get the sum over all chains and runs per phase, +\code{"chain"} to get the sum per chain over both phases and all runs, +\code{"phase and run"} to get the sum over all chains, separately per +phase and run.} +} +\description{ +Calculate the sum of the computational duration of a JointAI object +} From 2b0c08f412fefb10e137be573ee878c64defb371 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Wed, 9 Mar 2022 12:41:27 +0100 Subject: [PATCH 104/176] bugfix in sum_duration() when by = NULL --- R/helpfunctions_JAGS.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/helpfunctions_JAGS.R b/R/helpfunctions_JAGS.R index 1e7a0f50..8b66ea11 100644 --- a/R/helpfunctions_JAGS.R +++ b/R/helpfunctions_JAGS.R @@ -239,7 +239,7 @@ duration_obj <- function(dur) { sum_duration <- function(object, by = NULL) { obj <- object$comp_info$duration - if (is.null(by) | by %in% c("phase", "phase and run", "run and phase")) { + if (is.null(by) || by %in% c("phase", "phase and run", "run and phase")) { s <- lapply(obj, function(p) { @@ -255,10 +255,10 @@ sum_duration <- function(object, by = NULL) { }) - if (by %in% c("phase and run", "run and phase")) { - s - } else if (is.null(by)) { + if (is.null(by)) { sum(do.call(c, s)) + } else if (by %in% c("phase and run", "run and phase")) { + s } else if (by == "phase") { lapply(s, sum) } From adce9adecd9c664a72c358e1db93973dac58feb3 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 14 Mar 2022 13:05:03 +0100 Subject: [PATCH 105/176] bugfix in predDF (didn't include auxiliary variables) --- NEWS.md | 2 ++ R/predict.R | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index e022f395..5f7b6865 100644 --- a/NEWS.md +++ b/NEWS.md @@ -14,6 +14,8 @@ integral in the survival. * Bug causing the wrong elements of the data matrix to be monitored when `monitor_params(imps = TRUE)` in survival models fixed. +* `predDF()`: bugfix for models including auxiliary variables (which were + previously not included into the data) ## Small improvements * Use of `add_samples()` will now result in the `call` element of a `JointAI` diff --git a/R/predict.R b/R/predict.R index ab5e4250..d07a7d7d 100644 --- a/R/predict.R +++ b/R/predict.R @@ -46,7 +46,7 @@ predDF.JointAI <- function(object, vars, length = 100L, ...) { predDF.list(object = c(object$fixed, object$random, - object$auxvars, + object$Mlist$auxvars, if (!is.null(object$Mlist$timevar)) as.formula(paste0("~", object$Mlist$timevar)) ), From ffc7820cc27e98156adc0450ef8039bb7e9dc73b Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 17 Mar 2022 14:27:12 +0100 Subject: [PATCH 106/176] bugfix: object$Mlist$timevar is not a formula but a character string --- R/summary.JointAI.R | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/R/summary.JointAI.R b/R/summary.JointAI.R index 7e5192ff..d0284ee3 100644 --- a/R/summary.JointAI.R +++ b/R/summary.JointAI.R @@ -710,8 +710,9 @@ get_missinfo <- function(object) { errormsg("Use only with 'JointAI' objects.") - allvars <- all_vars(c(object$fixed, object$random, object$Mlist$auxvars, - object$Mlist$timevar)) + allvars <- unique( + c(all_vars(c(object$fixed, object$random, object$Mlist$auxvars)), + object$Mlist$timevar)) groups <- object$Mlist$groups From 505ba717cd3d50fe98cafe6bc30d3c107aef0d16 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 26 May 2022 11:21:23 +0200 Subject: [PATCH 107/176] re-built readme --- README.md | 45 +++++++++++++++++++++++---------------------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index a00d1b0b..d2d2b7cf 100644 --- a/README.md +++ b/README.md @@ -5,12 +5,11 @@ -[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version-last-release/JointAI)](https://CRAN.R-project.org/package=JointAI) +[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/JointAI)](https://CRAN.R-project.org/package=JointAI) [![](https://cranlogs.r-pkg.org/badges/grand-total/JointAI)](https://CRAN.R-project.org/package=JointAI) [![Download counter](https://cranlogs.r-pkg.org/badges/JointAI)](https://cran.r-project.org/package=JointAI) -[![Rdoc](https://www.rdocumentation.org/badges/version/JointAI)](https://www.rdocumentation.org/packages/JointAI) -[![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://app.codecov.io/gh/NErler/JointAI) +[![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://codecov.io/gh/NErler/JointAI) [![Travis-CI Build Status](https://travis-ci.org/NErler/JointAI.svg?branch=master)](https://travis-ci.org/NErler/JointAI) [![R build @@ -105,6 +104,8 @@ plot_all(NHANES[c(1, 5:6, 8:12)], fill = '#D10E3B', border = '#460E1B', ncol = 4 ``` r md_pattern(NHANES, color = c('#460E1B', '#D10E3B')) +#> Warning in register(): Can't find generic `scale_type` in package ggplot2 to +#> register S3 method. ``` @@ -143,18 +144,18 @@ summary(lm1) #> #> #> Posterior summary: -#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 87.984 9.0412 70.110 107.092 0.00000 1.00 0.0258 -#> genderfemale -3.501 2.2488 -8.039 1.059 0.10400 1.00 0.0258 -#> age 0.333 0.0713 0.199 0.471 0.00000 1.01 0.0275 -#> WC 0.226 0.0757 0.072 0.373 0.00267 1.00 0.0258 -#> alc>=1 6.509 2.3290 1.899 10.859 0.01067 1.00 0.0270 -#> educhigh -2.780 2.1237 -6.886 1.248 0.19733 1.00 0.0258 -#> bili -5.173 4.8315 -14.599 4.109 0.28800 1.01 0.0303 +#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD +#> (Intercept) 87.662 8.6088 70.3830 104.899 0.00000 1.00 0.0271 +#> genderfemale -3.487 2.2407 -7.9563 0.818 0.10533 1.01 0.0258 +#> age 0.334 0.0683 0.1986 0.468 0.00000 1.01 0.0258 +#> WC 0.230 0.0721 0.0876 0.376 0.00133 1.00 0.0258 +#> alc>=1 6.419 2.3862 1.6656 11.112 0.00667 1.03 0.0358 +#> educhigh -2.805 2.0681 -6.9371 1.339 0.17067 1.00 0.0258 +#> bili -5.277 4.7332 -14.7727 3.596 0.25333 1.01 0.0275 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_SBP 13.6 0.739 12.3 15.1 1.01 0.0289 +#> sigma_SBP 13.5 0.725 12.2 15 1.01 0.0258 #> #> #> MCMC settings: @@ -170,19 +171,19 @@ summary(lm1) coef(lm1) #> $SBP #> (Intercept) genderfemale age WC alc>=1 educhigh -#> 87.9839157 -3.5010429 0.3329532 0.2262894 6.5093606 -2.7800225 +#> 87.6622381 -3.4873104 0.3335133 0.2302755 6.4194926 -2.8054874 #> bili sigma_SBP -#> -5.1730414 13.5670206 +#> -5.2768560 13.5278177 confint(lm1) #> $SBP #> 2.5% 97.5% -#> (Intercept) 70.11037933 107.0920122 -#> genderfemale -8.03905105 1.0594821 -#> age 0.19919441 0.4705334 -#> WC 0.07201019 0.3734877 -#> alc>=1 1.89897665 10.8594963 -#> educhigh -6.88561508 1.2481772 -#> bili -14.59898407 4.1089909 -#> sigma_SBP 12.25273343 15.1162472 +#> (Intercept) 70.38301720 104.8986161 +#> genderfemale -7.95631510 0.8182921 +#> age 0.19857014 0.4678630 +#> WC 0.08761699 0.3756334 +#> alc>=1 1.66562640 11.1121370 +#> educhigh -6.93714769 1.3389344 +#> bili -14.77269911 3.5955383 +#> sigma_SBP 12.16165429 15.0367180 ``` From 4e8612b8dfcf143130202c7f5cf1999bc65ccd51 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 29 Jul 2022 10:26:16 +0200 Subject: [PATCH 108/176] bugfix in nonprop --- NEWS.md | 1 + R/divide_matrices.R | 2 +- R/helpfunctions_divide_matrices.R | 5 +++-- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/NEWS.md b/NEWS.md index 5f7b6865..da532a3b 100644 --- a/NEWS.md +++ b/NEWS.md @@ -16,6 +16,7 @@ `monitor_params(imps = TRUE)` in survival models fixed. * `predDF()`: bugfix for models including auxiliary variables (which were previously not included into the data) +* `nonprop`: bugfix for non-proportional effects in covariate models ## Small improvements * Use of `add_samples()` will now result in the `call` element of a `JointAI` diff --git a/R/divide_matrices.R b/R/divide_matrices.R index a5df64ce..055fa914 100644 --- a/R/divide_matrices.R +++ b/R/divide_matrices.R @@ -233,7 +233,7 @@ divide_matrices <- function(data, fixed, random = NULL, analysis_type, # get the linear predictor variables that have non-proportional effects in # cumulative logit models lp_nonprop <- get_nonprop_lp(nonprop, dsgn_mat_lvls = Mlvls, - data, refs, fixed) + data, refs, fixed, lp_cols) # reduce the design matrices to the correct rows, according to their levels diff --git a/R/helpfunctions_divide_matrices.R b/R/helpfunctions_divide_matrices.R index bc0819e1..8dd1ce72 100644 --- a/R/helpfunctions_divide_matrices.R +++ b/R/helpfunctions_divide_matrices.R @@ -657,7 +657,7 @@ get_linpreds <- function(fixed, random, data, models, auxvars = NULL, -get_nonprop_lp <- function(nonprop, dsgn_mat_lvls, data, refs, fixed) { +get_nonprop_lp <- function(nonprop, dsgn_mat_lvls, data, refs, fixed, lp_cols) { # get the linear predictors of covariates with non-proportional effects in # cumulative logit (mixed) models @@ -683,7 +683,8 @@ get_nonprop_lp <- function(nonprop, dsgn_mat_lvls, data, refs, fixed) { lapply(names(nonprop), function(k) { - if (any(!all_vars(nonprop[[k]]) %in% all_vars(fixed[[k]]))) { + propvars <- cvapply(names(unlist(unname(lp_cols[[k]]))), replace_dummy, refs) + if (any(!all_vars(nonprop[[k]]) %in% propvars)) { errormsg( "All variables that have non-proportional effect (specified via the argument %s also need to be part of the main model formula.", From 8d0294eb2b554a9a616141fdaca1023e17650cde Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 08:02:10 +0200 Subject: [PATCH 109/176] new package or R versions caused change in empty lines in JAGS models --- .../testout/clm_lapply.models.jagsmodel..txt | 3761 ++++++------ .../testout/clmm_lapply.models.jagsmodel..txt | 5385 ++++++++--------- 2 files changed, 4497 insertions(+), 4649 deletions(-) diff --git a/tests/testthat/testout/clm_lapply.models.jagsmodel..txt b/tests/testthat/testout/clm_lapply.models.jagsmodel..txt index c43f2760..325eb524 100644 --- a/tests/testthat/testout/clm_lapply.models.jagsmodel..txt +++ b/tests/testthat/testout/clm_lapply.models.jagsmodel..txt @@ -1,1934 +1,1867 @@ $m0a -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- 0 - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- 0 + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + } $m0b -model { - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- 0 - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - -} +model { + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- 0 + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } $m1a -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + } $m1b -model { - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - } - - # Priors for the model for O2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - -} +model { + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + } + + # Priors for the model for O2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } $m2a -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2b -model { - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - } - - # Priors for the model for O2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + } + + # Priors for the model for O2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3a -model { - - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] + - M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4] - } - - # Priors for the model for C1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - -} +model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] + + M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4] + } + + # Priors for the model for C1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + } $m3b -model { - - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] - } - - # Priors for the model for C1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- 0 - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - -} +model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + } + + # Priors for the model for C1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- 0 + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } $m4a -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 6] * beta[1] + M_lvlone[i, 7] * beta[2] + - M_lvlone[i, 8] * beta[3] + M_lvlone[i, 9] * beta[4] + - M_lvlone[i, 10] * beta[5] + M_lvlone[i, 11] * beta[6] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[7] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[8] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[9] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[10] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[11] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] - - M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] - M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] - M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 6] * beta[1] + M_lvlone[i, 7] * beta[2] + + M_lvlone[i, 8] * beta[3] + M_lvlone[i, 9] * beta[4] + + M_lvlone[i, 10] * beta[5] + M_lvlone[i, 11] * beta[6] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[7] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[8] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[9] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[10] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[11] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } $m4b -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] + - M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] + - M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] + - M_lvlone[i, 14] * alpha[7] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8] - - M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- M_lvlone[i, 12] * alpha[9] + M_lvlone[i, 13] * alpha[10] + - M_lvlone[i, 14] * alpha[11] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[12] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - - M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0) - - } - - # Priors for the model for O2 - for (k in 9:12) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] + + M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] + + M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] + + M_lvlone[i, 14] * alpha[7] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8] + + M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- M_lvlone[i, 12] * alpha[9] + M_lvlone[i, 13] * alpha[10] + + M_lvlone[i, 14] * alpha[11] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[12] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + + M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0) + + } + + # Priors for the model for O2 + for (k in 9:12) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] + } + + } $m5a -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } $m5b -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:13) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:13) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] + } + + } $m5c -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] + } + + } $m5d -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] - M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] - M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] + M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] + M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] + } + + } $m5e -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- 0 - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] + - M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] + - M_lvlone[i, 9] * beta[4] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + - M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] + - M_lvlone[i, 12] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] + - M_lvlone[i, 9] * beta[15] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] + - M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] + - M_lvlone[i, 12] * beta[19] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] + - M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] + - M_lvlone[i, 9] * beta[26] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] + - M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] + - M_lvlone[i, 12] * beta[30] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:33) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] - M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] - M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- 0 + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] + + M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] + + M_lvlone[i, 9] * beta[4] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + + M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] + + M_lvlone[i, 12] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] + + M_lvlone[i, 9] * beta[15] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] + + M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] + + M_lvlone[i, 12] * beta[19] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] + + M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] + + M_lvlone[i, 9] * beta[26] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] + + M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] + + M_lvlone[i, 12] * beta[30] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:33) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] + M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] + M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] + } + + } $m6a -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } $m6b -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:13) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:13) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] + } + + } $m6c -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] + } + + } $m6d -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] - M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] - M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] + M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] + M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] + } + + } $m6e -model { - - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- 0 - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] + - M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] + - M_lvlone[i, 9] * beta[4] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + - M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] + - M_lvlone[i, 12] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] + - M_lvlone[i, 9] * beta[15] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] + - M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] + - M_lvlone[i, 12] * beta[19] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] + - M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] + - M_lvlone[i, 9] * beta[26] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] + - M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] + - M_lvlone[i, 12] * beta[30] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:33) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] - M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] - M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] - } - - -} +model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- 0 + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] + + M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] + + M_lvlone[i, 9] * beta[4] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + + M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] + + M_lvlone[i, 12] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] + + M_lvlone[i, 9] * beta[15] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] + + M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] + + M_lvlone[i, 12] * beta[19] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] + + M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] + + M_lvlone[i, 9] * beta[26] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] + + M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] + + M_lvlone[i, 12] * beta[30] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:33) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] + M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] + M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] + } + + } diff --git a/tests/testthat/testout/clmm_lapply.models.jagsmodel..txt b/tests/testthat/testout/clmm_lapply.models.jagsmodel..txt index a3b021f8..0a4bf47f 100644 --- a/tests/testthat/testout/clmm_lapply.models.jagsmodel..txt +++ b/tests/testthat/testout/clmm_lapply.models.jagsmodel..txt @@ -1,2764 +1,2679 @@ $m0a -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + } $m0b -model { - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } $m1a -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] - } - - - - # Priors for the model for o1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] + } + + + + # Priors for the model for o1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + } $m1b -model { - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] - } - - - - # Priors for the model for o2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] + } + + + + # Priors for the model for o2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } $m1c -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + } $m1d -model { - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] + - beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } $m2a -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - - - # Priors for the model for o1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + + + # Priors for the model for o1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2b -model { - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - - - # Priors for the model for o2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + + + # Priors for the model for o2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2c -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m2d -model { - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] + - beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m3a -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 2] + - beta[3] * M_lvlone[i, 3] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - -} +model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 2] + + beta[3] * M_lvlone[i, 3] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + } $m3b -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 3] + - beta[3] * M_lvlone[i, 4] + beta[4] * M_lvlone[i, 5] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - -} +model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 3] + + beta[3] * M_lvlone[i, 4] + beta[4] * M_lvlone[i, 5] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } $m4a -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + beta[6] * M_lvlone[i, 3] + - beta[7] * M_lvlone[i, 4] + beta[8] * M_lvlone[i, 5] + - beta[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[10] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + - beta[11] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 4] * beta[1] + M_id[ii, 5] * beta[2] + - M_id[ii, 6] * beta[3] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * beta[4] + - (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] - } - - - - # Priors for the model for o2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 3] * alpha[6] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[7] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[8] - log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[9] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[10] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[11] - log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[12] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[13] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 6:14) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 3] * alpha[15] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[16] - - M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2]) - - - } - - # Priors for the model for C2 - for (k in 15:16) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 6] <- M_lvlone[i, 3] * M_id[group_id[i], 7] - M_lvlone[i, 7] <- M_lvlone[i, 4] * M_id[group_id[i], 7] - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_id[group_id[i], 7] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + beta[6] * M_lvlone[i, 3] + + beta[7] * M_lvlone[i, 4] + beta[8] * M_lvlone[i, 5] + + beta[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[10] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + + beta[11] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 4] * beta[1] + M_id[ii, 5] * beta[2] + + M_id[ii, 6] * beta[3] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * beta[4] + + (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + } + + + + # Priors for the model for o2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 3] * alpha[6] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[7] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[8] + log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[9] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[10] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[11] + log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[12] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[13] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 6:14) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 3] * alpha[15] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[16] + + M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2]) + + + } + + # Priors for the model for C2 + for (k in 15:16) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 6] <- M_lvlone[i, 3] * M_id[group_id[i], 7] + M_lvlone[i, 7] <- M_lvlone[i, 4] * M_id[group_id[i], 7] + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_id[group_id[i], 7] + } + + } $m4b -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[2] - } - - - - # Priors for the model for o1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - - M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0) - - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- M_id[ii, 5] * alpha[1] + M_id[ii, 6] * alpha[2] + - M_id[ii, 7] * alpha[3] + - (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[4] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[5] - } - - - - # Priors for the model for o2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[6] + M_id[ii, 5] * alpha[7] + M_id[ii, 6] * alpha[8] + - M_id[ii, 7] * alpha[9] + - (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[10] - - M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1]) - - - } - - # Priors for the model for C2 - for (k in 6:10) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[2] + } + + + + # Priors for the model for o1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + + M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0) + + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- M_id[ii, 5] * alpha[1] + M_id[ii, 6] * alpha[2] + + M_id[ii, 7] * alpha[3] + + (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[4] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[5] + } + + + + # Priors for the model for o2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[6] + M_id[ii, 5] * alpha[7] + M_id[ii, 6] * alpha[8] + + M_id[ii, 7] * alpha[9] + + (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[10] + + M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1]) + + + } + + # Priors for the model for C2 + for (k in 6:10) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3] + } + + } $m4c -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_o1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_o1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:4] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] + - M_id[ii, 4] * beta[2] - mu_b_o1_id[ii, 2] <- beta[4] - mu_b_o1_id[ii, 3] <- beta[3] - mu_b_o1_id[ii, 4] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - for (k in 1:4) { - RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_o1_id[1:4, 1:4] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id) - D_o1_id[1:4, 1:4] <- inverse(invD_o1_id[ , ]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for time - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - M_id[ii, 4] * alpha[7] - } - - # Priors for the model for c1 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_o1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_o1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:4] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] + + M_id[ii, 4] * beta[2] + mu_b_o1_id[ii, 2] <- beta[4] + mu_b_o1_id[ii, 3] <- beta[3] + mu_b_o1_id[ii, 4] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + for (k in 1:4) { + RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_o1_id[1:4, 1:4] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id) + D_o1_id[1:4, 1:4] <- inverse(invD_o1_id[ , ]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for time + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + M_id[ii, 4] * alpha[7] + } + + # Priors for the model for c1 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m4d -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[4] * M_lvlone[i, 5] + - beta[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[7] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:2] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] - mu_b_o1_id[ii, 2] <- beta[2] + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[6] - } - - - - # Priors for the model for o1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - for (k in 1:2) { - RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_o1_id[1:2, 1:2] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id) - D_o1_id[1:2, 1:2] <- inverse(invD_o1_id[ , ]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] - } - - # Priors for the model for b2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[4] * M_lvlone[i, 5] + + beta[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[7] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:2] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] + mu_b_o1_id[ii, 2] <- beta[2] + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[6] + } + + + + # Priors for the model for o1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + for (k in 1:2) { + RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_o1_id[1:2, 1:2] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id) + D_o1_id[1:2, 1:2] <- inverse(invD_o1_id[ , ]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + } + + # Priors for the model for b2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] + } + + } $m4e -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] - } - - - - # Priors for the model for o1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal_ridge_beta[k]) - tau_reg_ordinal_ridge_beta[k] ~ dgamma(0.01, 0.01) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] + } + + + + # Priors for the model for o1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal_ridge_beta[k]) + tau_reg_ordinal_ridge_beta[k] ~ dgamma(0.01, 0.01) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + } $m5a -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[7] * M_lvlone[i, 3] - eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[8] * M_lvlone[i, 3] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] - } - - - - # Priors for the model for o1 - for (k in 1:8) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] - - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + - M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5] - } - - # Priors for the model for b2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 3] * alpha[6] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] + - M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9] - } - - # Priors for the model for C2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10] - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0) - } - - # Priors for the model for O2 - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[7] * M_lvlone[i, 3] + eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[8] * M_lvlone[i, 3] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + } + + + + # Priors for the model for o1 + for (k in 1:8) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + + M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5] + } + + # Priors for the model for b2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 3] * alpha[6] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] + + M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9] + } + + # Priors for the model for C2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10] + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0) + } + + # Priors for the model for O2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + } $m5b -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + - M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + - M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + - M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + + M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + + M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] + } + + } $m5c -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + - M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + - M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + - M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + + M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + + M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] + } + + } $m5d -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] + - (M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] + - (M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] + - (M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8] - } - - - - # Priors for the model for o1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + - M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (ii in 1:100) { - M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] - M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] - M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + + (M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] + + (M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] + + (M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8] + } + + + + # Priors for the model for o1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + + M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (ii in 1:100) { + M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] + M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] + M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] + } + + } $m5e -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] + - beta[3] * M_id[group_id[i], 7] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] + - beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + - beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + - beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + - beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] + - beta[12] * M_id[group_id[i], 7] + - beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] + - beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + - beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + - beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + - beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:20) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + - M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (ii in 1:100) { - M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] - M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] - M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] + + beta[3] * M_id[group_id[i], 7] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] + + beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + + beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + + beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + + beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] + + beta[12] * M_id[group_id[i], 7] + + beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] + + beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + + beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + + beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + + beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:20) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + + M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (ii in 1:100) { + M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] + M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] + M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] + } + + } $m6a -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[7] * M_lvlone[i, 3] - eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[8] * M_lvlone[i, 3] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] - } - - - - # Priors for the model for o1 - for (k in 1:8) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] - - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + - M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5] - } - - # Priors for the model for b2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 3] * alpha[6] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] + - M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9] - } - - # Priors for the model for C2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10] - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0) - } - - # Priors for the model for O2 - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[7] * M_lvlone[i, 3] + eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[8] * M_lvlone[i, 3] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + } + + + + # Priors for the model for o1 + for (k in 1:8) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + + M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5] + } + + # Priors for the model for b2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 3] * alpha[6] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] + + M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9] + } + + # Priors for the model for C2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10] + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0) + } + + # Priors for the model for O2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + } $m6b -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + - M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + - M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + - M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + + M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + + M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] + } + + } $m6c -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + - M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + - M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + - M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + + M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + + M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] + } + + } $m6d -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] + - (M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] + - (M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] + - (M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8] - } - - - - # Priors for the model for o1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + - M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (ii in 1:100) { - M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] - M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] - M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + + (M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] + + (M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] + + (M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8] + } + + + + # Priors for the model for o1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + + M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (ii in 1:100) { + M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] + M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] + M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] + } + + } $m6e -model { - - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] + - beta[3] * M_id[group_id[i], 7] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] + - beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + - beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + - beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + - beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] + - beta[12] * M_id[group_id[i], 7] + - beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] + - beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + - beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + - beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + - beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:20) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + - M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (ii in 1:100) { - M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] - M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] - M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] - } - - -} +model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] + + beta[3] * M_id[group_id[i], 7] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] + + beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + + beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + + beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + + beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] + + beta[12] * M_id[group_id[i], 7] + + beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] + + beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + + beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + + beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + + beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:20) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + + M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (ii in 1:100) { + M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] + M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] + M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] + } + + } $m7a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + beta[3] * M_lvlone[i, 4] + - beta[4] * M_lvlone[i, 5] + beta[5] * M_lvlone[i, 6] + - beta[6] * M_lvlone[i, 7] + beta[7] * M_lvlone[i, 8] + - beta[8] * M_lvlone[i, 9] + beta[9] * M_lvlone[i, 10] + - beta[10] * M_lvlone[i, 11] + - beta[11] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for y - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 9] + - alpha[3] * M_lvlone[i, 10] + alpha[4] * M_lvlone[i, 11] + - alpha[5] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[1] - } - - - - # Priors for the model for o2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - - # Cumulative logit mixed effects model for x ------------------------------------ - for (i in 1:329) { - M_lvlone[i, 3] ~ dcat(p_x[i, 1:4]) - eta_x[i] <- b_x_id[group_id[i], 1] + - alpha[7] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] - - p_x[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_x[i, 2:4]))) - p_x[i, 2] <- max(1e-10, min(1-1e-10, psum_x[i, 1] - psum_x[i, 2])) - p_x[i, 3] <- max(1e-10, min(1-1e-10, psum_x[i, 2] - psum_x[i, 3])) - p_x[i, 4] <- max(1e-10, min(1-1e-10, psum_x[i, 3])) - - logit(psum_x[i, 1]) <- gamma_x[1] + eta_x[i] - logit(psum_x[i, 2]) <- gamma_x[2] + eta_x[i] - logit(psum_x[i, 3]) <- gamma_x[3] + eta_x[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_x_id[ii, 1:1] ~ dnorm(mu_b_x_id[ii, ], invD_x_id[ , ]) - mu_b_x_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[6] - } - - - - # Priors for the model for x - for (k in 6:7) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_x[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_x[2] <- gamma_x[1] - exp(delta_x[1]) - gamma_x[3] <- gamma_x[2] - exp(delta_x[2]) - - invD_x_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_x_id[1, 1] <- 1 / (invD_x_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + beta[3] * M_lvlone[i, 4] + + beta[4] * M_lvlone[i, 5] + beta[5] * M_lvlone[i, 6] + + beta[6] * M_lvlone[i, 7] + beta[7] * M_lvlone[i, 8] + + beta[8] * M_lvlone[i, 9] + beta[9] * M_lvlone[i, 10] + + beta[10] * M_lvlone[i, 11] + + beta[11] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for y + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 9] + + alpha[3] * M_lvlone[i, 10] + alpha[4] * M_lvlone[i, 11] + + alpha[5] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[1] + } + + + + # Priors for the model for o2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Cumulative logit mixed effects model for x ------------------------------------ + for (i in 1:329) { + M_lvlone[i, 3] ~ dcat(p_x[i, 1:4]) + eta_x[i] <- b_x_id[group_id[i], 1] + + alpha[7] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] + + p_x[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_x[i, 2:4]))) + p_x[i, 2] <- max(1e-10, min(1-1e-10, psum_x[i, 1] - psum_x[i, 2])) + p_x[i, 3] <- max(1e-10, min(1-1e-10, psum_x[i, 2] - psum_x[i, 3])) + p_x[i, 4] <- max(1e-10, min(1-1e-10, psum_x[i, 3])) + + logit(psum_x[i, 1]) <- gamma_x[1] + eta_x[i] + logit(psum_x[i, 2]) <- gamma_x[2] + eta_x[i] + logit(psum_x[i, 3]) <- gamma_x[3] + eta_x[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_x_id[ii, 1:1] ~ dnorm(mu_b_x_id[ii, ], invD_x_id[ , ]) + mu_b_x_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[6] + } + + + + # Priors for the model for x + for (k in 6:7) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_x[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_x[2] <- gamma_x[1] - exp(delta_x[1]) + gamma_x[3] <- gamma_x[2] - exp(delta_x[2]) + + invD_x_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_x_id[1, 1] <- 1 / (invD_x_id[1, 1]) + } $m7b -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + beta[2] * M_lvlone[i, 5] + - beta[3] * M_lvlone[i, 6] + beta[4] * M_lvlone[i, 7] + - beta[5] * M_lvlone[i, 8] + beta[6] * M_lvlone[i, 9] + - beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[8] * M_lvlone[i, 10] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:8) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 5] + - alpha[3] * M_lvlone[i, 6] + alpha[4] * M_lvlone[i, 7] + - alpha[5] * M_lvlone[i, 8] + alpha[6] * M_lvlone[i, 9] + - alpha[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for b2 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] + - alpha[10] * M_lvlone[i, 6] + alpha[11] * M_lvlone[i, 7] + - alpha[12] * M_lvlone[i, 8] + alpha[13] * M_lvlone[i, 9] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] - } - - # Priors for the model for c2 - for (k in 8:13) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + beta[2] * M_lvlone[i, 5] + + beta[3] * M_lvlone[i, 6] + beta[4] * M_lvlone[i, 7] + + beta[5] * M_lvlone[i, 8] + beta[6] * M_lvlone[i, 9] + + beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[8] * M_lvlone[i, 10] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:8) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 5] + + alpha[3] * M_lvlone[i, 6] + alpha[4] * M_lvlone[i, 7] + + alpha[5] * M_lvlone[i, 8] + alpha[6] * M_lvlone[i, 9] + + alpha[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for b2 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] + + alpha[10] * M_lvlone[i, 6] + alpha[11] * M_lvlone[i, 7] + + alpha[12] * M_lvlone[i, 8] + alpha[13] * M_lvlone[i, 9] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] + } + + # Priors for the model for c2 + for (k in 8:13) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } From 338cae0384da0012e483d0c566b7a230f67f12f4 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 08:03:52 +0200 Subject: [PATCH 110/176] use of <= instead of = symbol (should always have been <= symbol); change due to updated R or package versions --- .../testout/clm_lapply.models0.coef..txt | 10 +++---- .../testout/clm_lapply.models0.confint..txt | 30 +++++++++---------- .../testout/clm_lapply.models0.print..txt | 20 ++++++------- .../testout/clm_lapply.models0.summary..txt | 30 +++++++++---------- .../testout/clmm_lapply.models0.coef..txt | 12 ++++---- .../testout/clmm_lapply.models0.confint..txt | 20 ++++++------- .../testout/clmm_lapply.models0.print..txt | 20 ++++++------- .../testout/clmm_lapply.models0.summary..txt | 20 ++++++------- 8 files changed, 81 insertions(+), 81 deletions(-) diff --git a/tests/testthat/testout/clm_lapply.models0.coef..txt b/tests/testthat/testout/clm_lapply.models0.coef..txt index e50e0465..38ff33ea 100644 --- a/tests/testthat/testout/clm_lapply.models0.coef..txt +++ b/tests/testthat/testout/clm_lapply.models0.coef..txt @@ -124,7 +124,7 @@ $m6a $m6a$O1 M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1 0 0 0 0 0 0 0 0 0 0 0 - C2 O1 = 1 O1 = 2 O1 = 3 + C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 0 0 0 0 @@ -132,7 +132,7 @@ $m6b $m6b$O1 M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 O1 = 1 O1 = 2 O1 = 3 + C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 0 0 0 0 0 @@ -140,7 +140,7 @@ $m6c $m6c$O1 M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2 0 0 0 0 0 0 0 0 0 0 0 - C1:C2 C1 C2 C1:C2 O1 = 1 O1 = 2 O1 = 3 + C1:C2 C1 C2 C1:C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 0 0 0 0 0 0 0 @@ -148,7 +148,7 @@ $m6d $m6d$O1 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 C1 C2 O1 = 1 O1 = 2 O1 = 3 + C1 C2 C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 0 0 0 0 0 0 0 @@ -160,7 +160,7 @@ $m6e$O1 0 0 0 0 0 0 0 0 0 0 0 C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 0 0 0 0 0 0 0 0 0 0 0 -O1 = 1 O1 = 2 O1 = 3 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 0 0 0 diff --git a/tests/testthat/testout/clm_lapply.models0.confint..txt b/tests/testthat/testout/clm_lapply.models0.confint..txt index fed158ca..70700c47 100644 --- a/tests/testthat/testout/clm_lapply.models0.confint..txt +++ b/tests/testthat/testout/clm_lapply.models0.confint..txt @@ -244,9 +244,9 @@ C1 0 0 C2 0 0 C1 0 0 C2 0 0 -O1 = 1 0 0 -O1 = 2 0 0 -O1 = 3 0 0 +O1 ≤ 1 0 0 +O1 ≤ 2 0 0 +O1 ≤ 3 0 0 $m6b @@ -265,9 +265,9 @@ C1 0 0 C2 0 0 C1 0 0 C2 0 0 -O1 = 1 0 0 -O1 = 2 0 0 -O1 = 3 0 0 +O1 ≤ 1 0 0 +O1 ≤ 2 0 0 +O1 ≤ 3 0 0 $m6c @@ -288,9 +288,9 @@ C1:C2 0 0 C1 0 0 C2 0 0 C1:C2 0 0 -O1 = 1 0 0 -O1 = 2 0 0 -O1 = 3 0 0 +O1 ≤ 1 0 0 +O1 ≤ 2 0 0 +O1 ≤ 3 0 0 $m6d @@ -311,9 +311,9 @@ C1 0 0 C2 0 0 C1 0 0 C2 0 0 -O1 = 1 0 0 -O1 = 2 0 0 -O1 = 3 0 0 +O1 ≤ 1 0 0 +O1 ≤ 2 0 0 +O1 ≤ 3 0 0 $m6e @@ -352,8 +352,8 @@ O24 0 0 M22:C2 0 0 M23:C2 0 0 M24:C2 0 0 -O1 = 1 0 0 -O1 = 2 0 0 -O1 = 3 0 0 +O1 ≤ 1 0 0 +O1 ≤ 2 0 0 +O1 ≤ 3 0 0 diff --git a/tests/testthat/testout/clm_lapply.models0.print..txt b/tests/testthat/testout/clm_lapply.models0.print..txt index 6bb3481e..026a5aea 100644 --- a/tests/testthat/testout/clm_lapply.models0.print..txt +++ b/tests/testthat/testout/clm_lapply.models0.print..txt @@ -221,7 +221,7 @@ clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 M22 M23 M24 O22 O23 O24 C1 C2 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 0 0 0 0 0 0 0 0 0 0 0 C1 C2 C1 C2 0 0 0 0 @@ -235,7 +235,7 @@ clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 0 0 0 0 0 0 0 0 0 0 0 C2 C1 C2 C1 C2 0 0 0 0 0 @@ -249,7 +249,7 @@ clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 M22 M23 M24 O22 O23 O24 C1 C2 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 0 0 0 0 0 0 0 0 0 0 0 C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 0 0 0 0 0 0 0 @@ -263,7 +263,7 @@ clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 0 0 0 0 0 0 0 0 0 0 0 M24:C2 C1 C2 C1 C2 C1 C2 0 0 0 0 0 0 0 @@ -278,7 +278,7 @@ clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 C1 M22 M23 M24 C2 O22 O23 O24 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24 0 0 0 0 0 0 0 0 0 0 0 M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 0 0 0 0 0 0 0 0 0 0 0 @@ -540,7 +540,7 @@ clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 M22 M23 M24 O22 O23 O24 C1 C2 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 0 0 0 0 0 0 0 0 0 0 0 C1 C2 C1 C2 0 0 0 0 @@ -556,7 +556,7 @@ clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 0 0 0 0 0 0 0 0 0 0 0 C2 C1 C2 C1 C2 0 0 0 0 0 @@ -572,7 +572,7 @@ clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 M22 M23 M24 O22 O23 O24 C1 C2 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 0 0 0 0 0 0 0 0 0 0 0 C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 0 0 0 0 0 0 0 @@ -588,7 +588,7 @@ clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 0 0 0 0 0 0 0 0 0 0 0 M24:C2 C1 C2 C1 C2 C1 C2 0 0 0 0 0 0 0 @@ -605,7 +605,7 @@ clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, Coefficients: -O1 = 1 O1 = 2 O1 = 3 C1 M22 M23 M24 C2 O22 O23 O24 +O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24 0 0 0 0 0 0 0 0 0 0 0 M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 0 0 0 0 0 0 0 0 0 0 0 diff --git a/tests/testthat/testout/clm_lapply.models0.summary..txt b/tests/testthat/testout/clm_lapply.models0.summary..txt index 72984dc3..ffc96593 100644 --- a/tests/testthat/testout/clm_lapply.models0.summary..txt +++ b/tests/testthat/testout/clm_lapply.models0.summary..txt @@ -816,9 +816,9 @@ O14: C2 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 = 1 0 0 0 0 0 NaN NaN -O1 = 2 0 0 0 0 0 NaN NaN -O1 = 3 0 0 0 0 0 NaN NaN +O1 ≤ 1 0 0 0 0 0 NaN NaN +O1 ≤ 2 0 0 0 0 0 NaN NaN +O1 ≤ 3 0 0 0 0 0 NaN NaN MCMC settings: @@ -857,9 +857,9 @@ O14: C2 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 = 1 0 0 0 0 0 NaN NaN -O1 = 2 0 0 0 0 0 NaN NaN -O1 = 3 0 0 0 0 0 NaN NaN +O1 ≤ 1 0 0 0 0 0 NaN NaN +O1 ≤ 2 0 0 0 0 0 NaN NaN +O1 ≤ 3 0 0 0 0 0 NaN NaN MCMC settings: @@ -900,9 +900,9 @@ O14: C1:C2 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 = 1 0 0 0 0 0 NaN NaN -O1 = 2 0 0 0 0 0 NaN NaN -O1 = 3 0 0 0 0 0 NaN NaN +O1 ≤ 1 0 0 0 0 0 NaN NaN +O1 ≤ 2 0 0 0 0 0 NaN NaN +O1 ≤ 3 0 0 0 0 0 NaN NaN MCMC settings: @@ -943,9 +943,9 @@ O14: C2 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 = 1 0 0 0 0 0 NaN NaN -O1 = 2 0 0 0 0 0 NaN NaN -O1 = 3 0 0 0 0 0 NaN NaN +O1 ≤ 1 0 0 0 0 0 NaN NaN +O1 ≤ 2 0 0 0 0 0 NaN NaN +O1 ≤ 3 0 0 0 0 0 NaN NaN MCMC settings: @@ -1005,9 +1005,9 @@ O14: M24:C2 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 = 1 0 0 0 0 0 NaN NaN -O1 = 2 0 0 0 0 0 NaN NaN -O1 = 3 0 0 0 0 0 NaN NaN +O1 ≤ 1 0 0 0 0 0 NaN NaN +O1 ≤ 2 0 0 0 0 0 NaN NaN +O1 ≤ 3 0 0 0 0 0 NaN NaN MCMC settings: diff --git a/tests/testthat/testout/clmm_lapply.models0.coef..txt b/tests/testthat/testout/clmm_lapply.models0.coef..txt index 6973aa6c..a66ca9c6 100644 --- a/tests/testthat/testout/clmm_lapply.models0.coef..txt +++ b/tests/testthat/testout/clmm_lapply.models0.coef..txt @@ -180,7 +180,7 @@ $m6a $m6a$o1 O22 O23 C1 C2 C1 C2 0 0 0 0 0 0 - b21 b21 D_o1_id[1,1] o1 = 1 o1 = 2 + b21 b21 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 0 0 0 0 0 @@ -188,9 +188,9 @@ $m6b $m6b$o1 M22 M23 M24 O22 O23 c1:C2 0 0 0 0 0 0 - C2 C2 c1 c1 D_o1_id[1,1] o1 = 1 + C2 C2 c1 c1 D_o1_id[1,1] o1 ≤ 1 0 0 0 0 0 0 - o1 = 2 + o1 ≤ 2 0 @@ -200,7 +200,7 @@ $m6c$o1 0 0 0 0 0 0 C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1] 0 0 0 0 0 0 - o1 = 1 o1 = 2 + o1 ≤ 1 o1 ≤ 2 0 0 @@ -210,7 +210,7 @@ $m6d$o1 0 0 0 0 0 0 M23:C2 M24:C2 C2 C2 c1 c1 0 0 0 0 0 0 -D_o1_id[1,1] o1 = 1 o1 = 2 +D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 0 0 0 @@ -222,7 +222,7 @@ $m6e$o1 0 0 0 0 0 0 C2 O22 O23 M22:C2 M23:C2 M24:C2 0 0 0 0 0 0 - c1 c1 D_o1_id[1,1] o1 = 1 o1 = 2 + c1 c1 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 0 0 0 0 0 diff --git a/tests/testthat/testout/clmm_lapply.models0.confint..txt b/tests/testthat/testout/clmm_lapply.models0.confint..txt index 8c9b80cb..071a70c4 100644 --- a/tests/testthat/testout/clmm_lapply.models0.confint..txt +++ b/tests/testthat/testout/clmm_lapply.models0.confint..txt @@ -306,8 +306,8 @@ C2 0 0 b21 0 0 b21 0 0 D_o1_id[1,1] 0 0 -o1 = 1 0 0 -o1 = 2 0 0 +o1 ≤ 1 0 0 +o1 ≤ 2 0 0 $m6b @@ -324,8 +324,8 @@ C2 0 0 c1 0 0 c1 0 0 D_o1_id[1,1] 0 0 -o1 = 1 0 0 -o1 = 2 0 0 +o1 ≤ 1 0 0 +o1 ≤ 2 0 0 $m6c @@ -343,8 +343,8 @@ c1:C2 0 0 c1 0 0 c1:C2 0 0 D_o1_id[1,1] 0 0 -o1 = 1 0 0 -o1 = 2 0 0 +o1 ≤ 1 0 0 +o1 ≤ 2 0 0 $m6d @@ -363,8 +363,8 @@ C2 0 0 c1 0 0 c1 0 0 D_o1_id[1,1] 0 0 -o1 = 1 0 0 -o1 = 2 0 0 +o1 ≤ 1 0 0 +o1 ≤ 2 0 0 $m6e @@ -391,8 +391,8 @@ M24:C2 0 0 c1 0 0 c1 0 0 D_o1_id[1,1] 0 0 -o1 = 1 0 0 -o1 = 2 0 0 +o1 ≤ 1 0 0 +o1 ≤ 2 0 0 $m7a diff --git a/tests/testthat/testout/clmm_lapply.models0.print..txt b/tests/testthat/testout/clmm_lapply.models0.print..txt index 0d41bf3e..de5c5fdb 100644 --- a/tests/testthat/testout/clmm_lapply.models0.print..txt +++ b/tests/testthat/testout/clmm_lapply.models0.print..txt @@ -459,7 +459,7 @@ clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 O22 O23 C1 C2 C1 C2 b21 b21 +o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21 0 0 0 0 0 0 0 0 0 0 @@ -479,7 +479,7 @@ clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 +o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 0 0 0 0 0 0 0 0 0 0 0 c1 0 @@ -501,7 +501,7 @@ clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 +o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 0 0 0 0 0 0 0 0 0 0 0 c1 c1:C2 0 0 @@ -523,7 +523,7 @@ clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 +o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 0 0 0 0 0 0 0 0 0 0 0 C2 c1 c1 0 0 0 @@ -545,7 +545,7 @@ clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 +o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 0 0 0 0 0 0 0 0 0 0 0 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 0 0 0 0 0 0 0 0 0 0 0 @@ -1113,7 +1113,7 @@ clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 O22 O23 C1 C2 C1 C2 b21 b21 +o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21 0 0 0 0 0 0 0 0 0 0 @@ -1135,7 +1135,7 @@ clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 +o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 0 0 0 0 0 0 0 0 0 0 0 c1 0 @@ -1159,7 +1159,7 @@ clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 +o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 0 0 0 0 0 0 0 0 0 0 0 c1 c1:C2 0 0 @@ -1183,7 +1183,7 @@ clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 +o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 0 0 0 0 0 0 0 0 0 0 0 C2 c1 c1 0 0 0 @@ -1207,7 +1207,7 @@ clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, Bayesian cumulative logit mixed model for "o1" Fixed effects: -o1 = 1 o1 = 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 +o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 0 0 0 0 0 0 0 0 0 0 0 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 0 0 0 0 0 0 0 0 0 0 0 diff --git a/tests/testthat/testout/clmm_lapply.models0.summary..txt b/tests/testthat/testout/clmm_lapply.models0.summary..txt index ebf4d62e..b8e0ccd3 100644 --- a/tests/testthat/testout/clmm_lapply.models0.summary..txt +++ b/tests/testthat/testout/clmm_lapply.models0.summary..txt @@ -1200,8 +1200,8 @@ o13: b21 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 = 1 0 0 0 0 0 NaN NaN -o1 = 2 0 0 0 0 0 NaN NaN +o1 ≤ 1 0 0 0 0 0 NaN NaN +o1 ≤ 2 0 0 0 0 0 NaN NaN Posterior summary of random effects covariance matrix: @@ -1246,8 +1246,8 @@ o13: c1 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 = 1 0 0 0 0 0 NaN NaN -o1 = 2 0 0 0 0 0 NaN NaN +o1 ≤ 1 0 0 0 0 0 NaN NaN +o1 ≤ 2 0 0 0 0 0 NaN NaN Posterior summary of random effects covariance matrix: @@ -1293,8 +1293,8 @@ o13: c1:C2 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 = 1 0 0 0 0 0 NaN NaN -o1 = 2 0 0 0 0 0 NaN NaN +o1 ≤ 1 0 0 0 0 0 NaN NaN +o1 ≤ 2 0 0 0 0 0 NaN NaN Posterior summary of random effects covariance matrix: @@ -1341,8 +1341,8 @@ o13: c1 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 = 1 0 0 0 0 0 NaN NaN -o1 = 2 0 0 0 0 0 NaN NaN +o1 ≤ 1 0 0 0 0 0 NaN NaN +o1 ≤ 2 0 0 0 0 0 NaN NaN Posterior summary of random effects covariance matrix: @@ -1397,8 +1397,8 @@ o13: c1 0 0 0 0 0 NaN NaN Posterior summary of the intercepts: Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 = 1 0 0 0 0 0 NaN NaN -o1 = 2 0 0 0 0 0 NaN NaN +o1 ≤ 1 0 0 0 0 0 NaN NaN +o1 ≤ 2 0 0 0 0 0 NaN NaN Posterior summary of random effects covariance matrix: From 1b7b5eba74c423d85a48f4535dc409ff23059d43 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 08:49:06 +0200 Subject: [PATCH 111/176] update roxygen version --- DESCRIPTION | 2 +- man/MC_error.Rd | 6 ++--- man/densplot.Rd | 2 +- man/hc_rdslope_info.Rd | 4 ++- man/hc_rdslope_interact.Rd | 4 ++- man/model_imp.Rd | 52 ++++++++++++++++++++++++++++---------- man/ns.Rd | 3 ++- man/paste_linpred.Rd | 3 ++- man/plot_all.Rd | 4 +-- man/residuals.JointAI.Rd | 3 ++- man/summary.JointAI.Rd | 3 ++- man/traceplot.Rd | 12 +-------- 12 files changed, 60 insertions(+), 38 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 11c4a5a4..cccbdf98 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -17,7 +17,7 @@ URL: https://nerler.github.io/JointAI/ License: GPL (>= 2) BugReports: https://github.com/nerler/JointAI/issues/ LazyData: TRUE -RoxygenNote: 7.1.2 +RoxygenNote: 7.2.1 Roxygen: list(old_usage = TRUE, markdown = TRUE) Imports: rjags, mcmcse, coda, rlang, future, mathjaxr, survival, MASS SystemRequirements: JAGS (https://mcmc-jags.sourceforge.io/) diff --git a/man/MC_error.Rd b/man/MC_error.Rd index 5ba4b21a..5b2a2c2d 100644 --- a/man/MC_error.Rd +++ b/man/MC_error.Rd @@ -85,11 +85,11 @@ columns \code{est} (posterior mean), \code{MCSE} (Monte Carlo error), Calculate, print and plot the Monte Carlo error of the samples from a 'JointAI' model, combining the samples from all MCMC chains. } -\section{Methods (by generic)}{ +\section{Functions}{ \itemize{ -\item \code{plot}: plot Monte Carlo error -}} +\item \code{plot(MCElist)}: plot Monte Carlo error +}} \note{ Lesaffre & Lawson (2012; p. 195) suggest the Monte Carlo error of a parameter should not be more than 5\% of the posterior standard diff --git a/man/densplot.Rd b/man/densplot.Rd index bb0018ed..1b9f47a7 100644 --- a/man/densplot.Rd +++ b/man/densplot.Rd @@ -46,7 +46,7 @@ should be excluded} can be passed to \code{graphics::abline()} to create vertical lines. Each of the list elements needs to contain at least -\verb{v = }, where is a vector of the +\code{v = } where is a vector of the same length as the number of plots (see examples).} \item{nrow}{optional; number of rows in the plot layout; diff --git a/man/hc_rdslope_info.Rd b/man/hc_rdslope_info.Rd index dd1d62af..d67226ef 100644 --- a/man/hc_rdslope_info.Rd +++ b/man/hc_rdslope_info.Rd @@ -33,7 +33,9 @@ Get info on the main effects in a random slope structure for a given level and sub-model } \details{ -Argument \code{hc_cols} should have the structure:\if{html}{\out{

}}\preformatted{list( +Argument \code{hc_cols} should have the structure: + +\if{html}{\out{
}}\preformatted{list( "(Intercept)" = list(main = c(M_id = 1), interact = NULL), time = list(main = c(M_lvlone = 4), diff --git a/man/hc_rdslope_interact.Rd b/man/hc_rdslope_interact.Rd index 72a09348..024e44c9 100644 --- a/man/hc_rdslope_interact.Rd +++ b/man/hc_rdslope_interact.Rd @@ -31,7 +31,9 @@ and one row per (main) random effect Get info on the interactions with random slopes for a given level and sub-model } \details{ -Argument \code{hc_cols} should have the structure:\if{html}{\out{
}}\preformatted{list( +Argument \code{hc_cols} should have the structure: + +\if{html}{\out{
}}\preformatted{list( "(Intercept)" = list(main = c(M_id = 1), interact = NULL), time = list(main = c(M_lvlone = 4), diff --git a/man/model_imp.Rd b/man/model_imp.Rd index 80d6207e..3acee4a5 100644 --- a/man/model_imp.Rd +++ b/man/model_imp.Rd @@ -316,10 +316,14 @@ It is possible to specify multi-level models as it is done in the package \href{https://CRAN.R-project.org/package=nlme}{\pkg{nlme}}, using \code{fixed} and \code{random}, or as it is done in the package \href{https://CRAN.R-project.org/package=lme4}{\pkg{lme4}}, -using \code{formula} and specifying the random effects in brackets:\if{html}{\out{
}}\preformatted{formula = y ~ x1 + x2 + x3 + (1 | id) +using \code{formula} and specifying the random effects in brackets: + +\if{html}{\out{
}}\preformatted{formula = y ~ x1 + x2 + x3 + (1 | id) }\if{html}{\out{
}} -is equivalent to\if{html}{\out{
}}\preformatted{fixed = y ~ x1 + x2 + x3, random = ~ 1|id +is equivalent to + +\if{html}{\out{
}}\preformatted{fixed = y ~ x1 + x2 + x3, random = ~ 1|id }\if{html}{\out{
}} } @@ -345,7 +349,9 @@ To fit multiple main models at the same time, a \code{list} of \code{formula} objects can be passed to the argument \code{formula}. Outcomes of one model may be contained as covariates in another model and it is possible to combine models for variables on different levels, -for example:\if{html}{\out{
}}\preformatted{formula = list(y ~ x1 + x2 + x3 + x4 + time + (time | id), +for example: + +\if{html}{\out{
}}\preformatted{formula = list(y ~ x1 + x2 + x3 + x4 + time + (time | id), x2 ~ x3 + x4 + x5) }\if{html}{\out{
}} @@ -373,7 +379,9 @@ the names are the structures and the vectors contain the names of the response variables which are included in this structure. For example, for a multivariate mixed model with five outcomes -\code{y1}, ..., \code{y5}, the specification could be:\if{html}{\out{
}}\preformatted{rd_vcov = list(blockdiag = c("y1", "y2"), +\code{y1}, ..., \code{y5}, the specification could be: + +\if{html}{\out{
}}\preformatted{rd_vcov = list(blockdiag = c("y1", "y2"), full = c("y3", "y4"), indep = "y5") }\if{html}{\out{
}} @@ -384,12 +392,16 @@ random effects for \code{y1} and \code{y2} are assumed to be correlated within e outcome, and the random effects for \code{y5} are assumed to be independent. It is possible to have multiple sets of response variables for which separate -full variance-covariance matrices are used, for example:\if{html}{\out{
}}\preformatted{rd_vcov = list(full = c("y1", "y2", "y5"), +full variance-covariance matrices are used, for example: + +\if{html}{\out{
}}\preformatted{rd_vcov = list(full = c("y1", "y2", "y5"), full = c("y3", "y4")) }\if{html}{\out{
}} In models with multiple levels of nesting, separate structures can be -specified per level:\if{html}{\out{
}}\preformatted{rd_vcov = list(id = list(blockdiag = c("y1", "y2"), +specified per level: + +\if{html}{\out{
}}\preformatted{rd_vcov = list(id = list(blockdiag = c("y1", "y2"), full = c("y3", "y4"), indep = "y5"), center = "indep") @@ -420,7 +432,9 @@ carried backward. \subsection{Differences to basic regression models}{ It is not possible to specify transformations of outcome variables, i.e., -it is not possible to use a model formula like\if{html}{\out{
}}\preformatted{log(y) ~ x1 + x2 + ... +it is not possible to use a model formula like + +\if{html}{\out{
}}\preformatted{log(y) ~ x1 + x2 + ... }\if{html}{\out{
}} In the specific case of a transformation with the natural logarithm, @@ -637,12 +651,16 @@ ordinal response variables. For example, the following three specifications are equivalent and assume a non-proportional effect of \code{C1} on \code{O1}, but \code{C1} is assumed to have a -proportional effect on the incomplete ordinal covariate \code{O2}:\if{html}{\out{
}}\preformatted{clm_imp(O1 ~ C1 + C2 + B2 + O2, data = wideDF, nonprop = ~ C1) +proportional effect on the incomplete ordinal covariate \code{O2}: + +\if{html}{\out{
}}\preformatted{clm_imp(O1 ~ C1 + C2 + B2 + O2, data = wideDF, nonprop = ~ C1) clm_imp(O1 ~ C1 + C2 + B2 + O2, data = wideDF, nonprop = list(~ C1)) clm_imp(O1 ~ C1 + C2 + B2 + O2, data = wideDF, nonprop = list(O1 = ~ C1)) }\if{html}{\out{
}} -To specify non-proportional effects on \code{O2}, a named list has to be provided:\if{html}{\out{
}}\preformatted{clm_imp(O1 ~ C1 + C2 + B2 + O2 + B1, data = wideDF, +To specify non-proportional effects on \code{O2}, a named list has to be provided: + +\if{html}{\out{
}}\preformatted{clm_imp(O1 ~ C1 + C2 + B2 + O2 + B1, data = wideDF, nonprop = list(O1 = ~ C1, O2 = ~ C1 + B1)) }\if{html}{\out{
}} @@ -666,14 +684,18 @@ another model. An example would be the use of "baseline" cholesterol First, the variable \code{chol0} is added to the \code{PBC} data. For most patients the value of cholesterol at baseline is observed, but not for all. It is important that the data has a row with \code{day = 0} for each -patient.\if{html}{\out{
}}\preformatted{PBC <- merge(PBC, +patient. + +\if{html}{\out{
}}\preformatted{PBC <- merge(PBC, subset(PBC, day == 0, select = c("id", "chol")), by = "id", suffixes = c("", "0")) }\if{html}{\out{
}} Next, the custom piece of JAGS model syntax needs to be specified. We loop here only over the patients for which the baseline cholesterol -is missing.\if{html}{\out{
}}\preformatted{calc_chol0 <- " +is missing. + +\if{html}{\out{
}}\preformatted{calc_chol0 <- " for (ii in 1:28) \{ M_id[row_chol0_id[ii], 3] <- M_lvlone[row_chol0_lvlone[ii], 1] \}" @@ -682,13 +704,17 @@ for (ii in 1:28) \{ To be able to run the model with the custom imputation "model" for baseline cholesterol we need to provide the numbers of the rows in the data matrices that contain the missing values of baseline cholesterol and the rows that -contain the imputed cholesterol at \code{day = 0}:\if{html}{\out{
}}\preformatted{row_chol0_lvlone <- which(PBC$day == 0 & is.na(PBC$chol0)) +contain the imputed cholesterol at \code{day = 0}: + +\if{html}{\out{
}}\preformatted{row_chol0_lvlone <- which(PBC$day == 0 & is.na(PBC$chol0)) row_chol0_id <- match(PBC$id, unique(PBC$id))[row_chol0_lvlone] }\if{html}{\out{
}} Then we pass both the custom sub-model and the additional data to the analysis function \code{coxph_imp()}. Note that we explicitly need to specify -the model for \code{chol}.\if{html}{\out{
}}\preformatted{coxph_imp(list(Surv(futime, status != "censored") ~ age + sex + chol0, +the model for \code{chol}. + +\if{html}{\out{
}}\preformatted{coxph_imp(list(Surv(futime, status != "censored") ~ age + sex + chol0, chol ~ age + sex + day + (day | id)), no_model = "day", data = PBC, append_data_list = list(row_chol0_lvlone = row_chol0_lvlone, diff --git a/man/ns.Rd b/man/ns.Rd index 77c44c3b..f17ad89d 100644 --- a/man/ns.Rd +++ b/man/ns.Rd @@ -4,7 +4,8 @@ \alias{ns} \title{Generate a Basis Matrix for Natural Cubic Splines} \usage{ -ns(x, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots = range(x)) +ns(x, df = NULL, knots = NULL, intercept = FALSE, + Boundary.knots = range(x)) } \arguments{ \item{x}{the predictor variable. Missing values are allowed.} diff --git a/man/paste_linpred.Rd b/man/paste_linpred.Rd index 1f9008f0..dc57e180 100644 --- a/man/paste_linpred.Rd +++ b/man/paste_linpred.Rd @@ -4,7 +4,8 @@ \alias{paste_linpred} \title{Write a linear predictor} \usage{ -paste_linpred(parname, parelmts, matnam, index, cols, scale_pars, isgk = FALSE) +paste_linpred(parname, parelmts, matnam, index, cols, scale_pars, + isgk = FALSE) } \arguments{ \item{parname}{character string; name fo the parameter (e.g., "beta")} diff --git a/man/plot_all.Rd b/man/plot_all.Rd index 8405e723..fbb6e06e 100644 --- a/man/plot_all.Rd +++ b/man/plot_all.Rd @@ -28,9 +28,7 @@ if \code{TRUE} it is given for all variables} \item{idvars}{name of the column that specifies the multi-level grouping structure} -\item{xlab}{labels for the x- and y-axis} - -\item{ylab}{labels for the x- and y-axis} +\item{xlab, ylab}{labels for the x- and y-axis} \item{...}{additional parameters passed to \code{\link[graphics]{barplot}} and \code{\link[graphics]{hist}}} diff --git a/man/residuals.JointAI.Rd b/man/residuals.JointAI.Rd index b1712e04..154df009 100644 --- a/man/residuals.JointAI.Rd +++ b/man/residuals.JointAI.Rd @@ -4,7 +4,8 @@ \alias{residuals.JointAI} \title{Extract residuals from an object of class JointAI} \usage{ -\method{residuals}{JointAI}(object, type = c("working", "pearson", "response"), warn = TRUE, ...) +\method{residuals}{JointAI}(object, type = c("working", "pearson", + "response"), warn = TRUE, ...) } \arguments{ \item{object}{object inheriting from class 'JointAI'} diff --git a/man/summary.JointAI.Rd b/man/summary.JointAI.Rd index a89cee25..a46986fd 100644 --- a/man/summary.JointAI.Rd +++ b/man/summary.JointAI.Rd @@ -9,7 +9,8 @@ \alias{print.JointAI} \title{Summarize the results from an object of class JointAI} \usage{ -\method{print}{Dmat}(x, digits = getOption("digits"), scientific = getOption("scipen"), ...) +\method{print}{Dmat}(x, digits = getOption("digits"), + scientific = getOption("scipen"), ...) \method{summary}{JointAI}(object, start = NULL, end = NULL, thin = NULL, quantiles = c(0.025, 0.975), subset = NULL, exclude_chains = NULL, diff --git a/man/traceplot.Rd b/man/traceplot.Rd index 0f68c09f..2bfa1145 100644 --- a/man/traceplot.Rd +++ b/man/traceplot.Rd @@ -18,17 +18,7 @@ traceplot(object, ...) \item{...}{ Arguments passed on to \code{\link[graphics:matplot]{graphics::matplot}} \describe{ - \item{\code{lty}}{vector of line types, widths, and end styles. - The first element is for the first column, the second element for - the second column, etc., even if lines are not plotted for all - columns. Line types will be used cyclically until all plots are - drawn.} - \item{\code{lwd}}{vector of line types, widths, and end styles. - The first element is for the first column, the second element for - the second column, etc., even if lines are not plotted for all - columns. Line types will be used cyclically until all plots are - drawn.} - \item{\code{lend}}{vector of line types, widths, and end styles. + \item{\code{lty,lwd,lend}}{vector of line types, widths, and end styles. The first element is for the first column, the second element for the second column, etc., even if lines are not plotted for all columns. Line types will be used cyclically until all plots are From 2743fce7df77b5e0f70ac4e891554ea85f30d5ba Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 08:49:43 +0200 Subject: [PATCH 112/176] use inherits() instead of class() --- R/get_modeltypes.R | 2 +- R/model_imp.R | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/get_modeltypes.R b/R/get_modeltypes.R index 42ad549c..03fb2dc7 100644 --- a/R/get_modeltypes.R +++ b/R/get_modeltypes.R @@ -9,7 +9,7 @@ get_models <- function(fixed, random = NULL, data, auxvars = NULL, if (missing(data)) errormsg("No dataset given.") - if (!is.null(auxvars) & class(auxvars) != 'formula') + if (!is.null(auxvars) & !inherits(auxvars, "formula")) errormsg("The argument %s should be a formula.", dQuote("auxvars")) models_user <- models diff --git a/R/model_imp.R b/R/model_imp.R index 96772ee8..4641a8bd 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -827,7 +827,7 @@ model_imp <- function(formula = NULL, fixed = NULL, data, random = NULL, mcmc <- jags_res$mcmc - if (n.iter > 0 & class(mcmc) != "mcmc.list") + if (n.iter > 0 & !inherits(mcmc, "mcmc.list")) warnmsg("There is no mcmc sample. Something went wrong.") # post processing ------------------------------------------------------------ From ad07293e2bee24a4598adb132cb9e072296825b6 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 16:27:08 +0200 Subject: [PATCH 113/176] Use snapshot tests instead of self-constructed helper version --- tests/testthat/_snaps/clm.md | 9241 ++++++ tests/testthat/_snaps/clmm.md | 23054 +++++++++++++ tests/testthat/_snaps/coxph.md | 2161 ++ tests/testthat/_snaps/glm.md | 17588 ++++++++++ tests/testthat/_snaps/glmm.md | 49011 ++++++++++++++++++++++++++++ tests/testthat/_snaps/mlogit.md | 3942 +++ tests/testthat/_snaps/mlogitmm.md | 12527 +++++++ tests/testthat/_snaps/survreg.md | 4093 +++ tests/testthat/test-clm.R | 51 +- tests/testthat/test-clmm.R | 64 +- tests/testthat/test-coxph.R | 27 +- tests/testthat/test-glm.R | 26 +- tests/testthat/test-glmm.R | 30 +- tests/testthat/test-mlogit.R | 43 +- tests/testthat/test-mlogitmm.R | 53 +- tests/testthat/test-survreg.R | 30 +- 16 files changed, 121759 insertions(+), 182 deletions(-) create mode 100644 tests/testthat/_snaps/clm.md create mode 100644 tests/testthat/_snaps/clmm.md create mode 100644 tests/testthat/_snaps/coxph.md create mode 100644 tests/testthat/_snaps/glm.md create mode 100644 tests/testthat/_snaps/glmm.md create mode 100644 tests/testthat/_snaps/mlogit.md create mode 100644 tests/testthat/_snaps/mlogitmm.md create mode 100644 tests/testthat/_snaps/survreg.md diff --git a/tests/testthat/_snaps/clm.md b/tests/testthat/_snaps/clm.md new file mode 100644 index 00000000..03f066f8 --- /dev/null +++ b/tests/testthat/_snaps/clm.md @@ -0,0 +1,9241 @@ +# data_list remains the same + + Code + lapply(models, "[[", "data_list") + Output + $m0a + $m0a$M_lvlone + O1 (Intercept) + 1 2 1 + 2 4 1 + 3 3 1 + 4 2 1 + 5 3 1 + 6 1 1 + 7 3 1 + 8 4 1 + 9 4 1 + 10 2 1 + 11 1 1 + 12 3 1 + 13 3 1 + 14 1 1 + 15 1 1 + 16 4 1 + 17 2 1 + 18 3 1 + 19 4 1 + 20 1 1 + 21 3 1 + 22 4 1 + 23 4 1 + 24 2 1 + 25 1 1 + 26 3 1 + 27 4 1 + 28 1 1 + 29 4 1 + 30 4 1 + 31 2 1 + 32 3 1 + 33 3 1 + 34 1 1 + 35 1 1 + 36 4 1 + 37 4 1 + 38 4 1 + 39 1 1 + 40 2 1 + 41 1 1 + 42 1 1 + 43 2 1 + 44 2 1 + 45 1 1 + 46 1 1 + 47 4 1 + 48 4 1 + 49 2 1 + 50 2 1 + 51 1 1 + 52 3 1 + 53 1 1 + 54 3 1 + 55 2 1 + 56 4 1 + 57 2 1 + 58 1 1 + 59 1 1 + 60 4 1 + 61 2 1 + 62 4 1 + 63 3 1 + 64 2 1 + 65 3 1 + 66 3 1 + 67 2 1 + 68 1 1 + 69 1 1 + 70 1 1 + 71 1 1 + 72 3 1 + 73 2 1 + 74 2 1 + 75 3 1 + 76 3 1 + 77 4 1 + 78 3 1 + 79 2 1 + 80 2 1 + 81 3 1 + 82 1 1 + 83 3 1 + 84 2 1 + 85 2 1 + 86 4 1 + 87 3 1 + 88 2 1 + 89 3 1 + 90 3 1 + 91 4 1 + 92 1 1 + 93 4 1 + 94 1 1 + 95 1 1 + 96 3 1 + 97 1 1 + 98 3 1 + 99 3 1 + 100 3 1 + + $m0a$mu_delta_ordinal + [1] 0 + + $m0a$tau_delta_ordinal + [1] 1e-04 + + + $m0b + $m0b$M_lvlone + O2 (Intercept) + 1 4 1 + 2 4 1 + 3 4 1 + 4 1 1 + 5 2 1 + 6 3 1 + 7 4 1 + 8 2 1 + 9 4 1 + 10 3 1 + 11 2 1 + 12 1 1 + 13 1 1 + 14 1 1 + 15 4 1 + 16 3 1 + 17 3 1 + 18 1 1 + 19 3 1 + 20 1 1 + 21 3 1 + 22 3 1 + 23 2 1 + 24 3 1 + 25 2 1 + 26 2 1 + 27 1 1 + 28 4 1 + 29 3 1 + 30 3 1 + 31 2 1 + 32 2 1 + 33 1 1 + 34 1 1 + 35 4 1 + 36 3 1 + 37 3 1 + 38 1 1 + 39 2 1 + 40 3 1 + 41 3 1 + 42 3 1 + 43 3 1 + 44 4 1 + 45 4 1 + 46 1 1 + 47 4 1 + 48 4 1 + 49 1 1 + 50 2 1 + 51 1 1 + 52 3 1 + 53 2 1 + 54 1 1 + 55 2 1 + 56 3 1 + 57 NA 1 + 58 4 1 + 59 4 1 + 60 3 1 + 61 4 1 + 62 1 1 + 63 4 1 + 64 4 1 + 65 4 1 + 66 1 1 + 67 3 1 + 68 3 1 + 69 4 1 + 70 1 1 + 71 4 1 + 72 4 1 + 73 2 1 + 74 4 1 + 75 3 1 + 76 2 1 + 77 2 1 + 78 3 1 + 79 2 1 + 80 1 1 + 81 4 1 + 82 2 1 + 83 4 1 + 84 1 1 + 85 1 1 + 86 2 1 + 87 3 1 + 88 3 1 + 89 2 1 + 90 4 1 + 91 2 1 + 92 1 1 + 93 NA 1 + 94 3 1 + 95 1 1 + 96 3 1 + 97 2 1 + 98 2 1 + 99 4 1 + 100 3 1 + + $m0b$mu_delta_ordinal + [1] 0 + + $m0b$tau_delta_ordinal + [1] 1e-04 + + + $m1a + $m1a$M_lvlone + O1 (Intercept) C1 + 1 2 1 1.410531 + 2 4 1 1.434183 + 3 3 1 1.430994 + 4 2 1 1.453096 + 5 3 1 1.438344 + 6 1 1 1.453207 + 7 3 1 1.425176 + 8 4 1 1.437908 + 9 4 1 1.416911 + 10 2 1 1.448638 + 11 1 1 1.428375 + 12 3 1 1.450130 + 13 3 1 1.420545 + 14 1 1 1.423005 + 15 1 1 1.435902 + 16 4 1 1.423901 + 17 2 1 1.457208 + 18 3 1 1.414280 + 19 4 1 1.443383 + 20 1 1 1.434954 + 21 3 1 1.429499 + 22 4 1 1.441897 + 23 4 1 1.423713 + 24 2 1 1.435395 + 25 1 1 1.425944 + 26 3 1 1.437115 + 27 4 1 1.441326 + 28 1 1 1.422953 + 29 4 1 1.437797 + 30 4 1 1.472121 + 31 2 1 1.421782 + 32 3 1 1.457672 + 33 3 1 1.430842 + 34 1 1 1.431523 + 35 1 1 1.421395 + 36 4 1 1.434496 + 37 4 1 1.425383 + 38 4 1 1.421802 + 39 1 1 1.430094 + 40 2 1 1.447621 + 41 1 1 1.434797 + 42 1 1 1.446091 + 43 2 1 1.445306 + 44 2 1 1.448783 + 45 1 1 1.450617 + 46 1 1 1.415055 + 47 4 1 1.436590 + 48 4 1 1.433938 + 49 2 1 1.414941 + 50 2 1 1.421807 + 51 1 1 1.453203 + 52 3 1 1.452129 + 53 1 1 1.431510 + 54 3 1 1.430082 + 55 2 1 1.443492 + 56 4 1 1.436460 + 57 2 1 1.418119 + 58 1 1 1.434971 + 59 1 1 1.445599 + 60 4 1 1.437097 + 61 2 1 1.428360 + 62 4 1 1.440550 + 63 3 1 1.443014 + 64 2 1 1.424298 + 65 3 1 1.448823 + 66 3 1 1.425834 + 67 2 1 1.427102 + 68 1 1 1.414240 + 69 1 1 1.456218 + 70 1 1 1.470594 + 71 1 1 1.425058 + 72 3 1 1.432371 + 73 2 1 1.441656 + 74 2 1 1.434952 + 75 3 1 1.402860 + 76 3 1 1.453363 + 77 4 1 1.432909 + 78 3 1 1.435103 + 79 2 1 1.434462 + 80 2 1 1.434661 + 81 3 1 1.445881 + 82 1 1 1.442548 + 83 3 1 1.430097 + 84 2 1 1.430119 + 85 2 1 1.430315 + 86 4 1 1.437584 + 87 3 1 1.409738 + 88 2 1 1.422388 + 89 3 1 1.422509 + 90 3 1 1.439432 + 91 4 1 1.430175 + 92 1 1 1.418002 + 93 4 1 1.423812 + 94 1 1 1.423473 + 95 1 1 1.434412 + 96 3 1 1.450844 + 97 1 1 1.433371 + 98 3 1 1.444378 + 99 3 1 1.422523 + 100 3 1 1.410394 + + $m1a$spM_lvlone + center scale + O1 NA NA + (Intercept) NA NA + C1 1.434101 0.01299651 + + $m1a$mu_reg_ordinal + [1] 0 + + $m1a$tau_reg_ordinal + [1] 1e-04 + + $m1a$mu_delta_ordinal + [1] 0 + + $m1a$tau_delta_ordinal + [1] 1e-04 + + + $m1b + $m1b$M_lvlone + O2 (Intercept) C1 + 1 4 1 1.410531 + 2 4 1 1.434183 + 3 4 1 1.430994 + 4 1 1 1.453096 + 5 2 1 1.438344 + 6 3 1 1.453207 + 7 4 1 1.425176 + 8 2 1 1.437908 + 9 4 1 1.416911 + 10 3 1 1.448638 + 11 2 1 1.428375 + 12 1 1 1.450130 + 13 1 1 1.420545 + 14 1 1 1.423005 + 15 4 1 1.435902 + 16 3 1 1.423901 + 17 3 1 1.457208 + 18 1 1 1.414280 + 19 3 1 1.443383 + 20 1 1 1.434954 + 21 3 1 1.429499 + 22 3 1 1.441897 + 23 2 1 1.423713 + 24 3 1 1.435395 + 25 2 1 1.425944 + 26 2 1 1.437115 + 27 1 1 1.441326 + 28 4 1 1.422953 + 29 3 1 1.437797 + 30 3 1 1.472121 + 31 2 1 1.421782 + 32 2 1 1.457672 + 33 1 1 1.430842 + 34 1 1 1.431523 + 35 4 1 1.421395 + 36 3 1 1.434496 + 37 3 1 1.425383 + 38 1 1 1.421802 + 39 2 1 1.430094 + 40 3 1 1.447621 + 41 3 1 1.434797 + 42 3 1 1.446091 + 43 3 1 1.445306 + 44 4 1 1.448783 + 45 4 1 1.450617 + 46 1 1 1.415055 + 47 4 1 1.436590 + 48 4 1 1.433938 + 49 1 1 1.414941 + 50 2 1 1.421807 + 51 1 1 1.453203 + 52 3 1 1.452129 + 53 2 1 1.431510 + 54 1 1 1.430082 + 55 2 1 1.443492 + 56 3 1 1.436460 + 57 NA 1 1.418119 + 58 4 1 1.434971 + 59 4 1 1.445599 + 60 3 1 1.437097 + 61 4 1 1.428360 + 62 1 1 1.440550 + 63 4 1 1.443014 + 64 4 1 1.424298 + 65 4 1 1.448823 + 66 1 1 1.425834 + 67 3 1 1.427102 + 68 3 1 1.414240 + 69 4 1 1.456218 + 70 1 1 1.470594 + 71 4 1 1.425058 + 72 4 1 1.432371 + 73 2 1 1.441656 + 74 4 1 1.434952 + 75 3 1 1.402860 + 76 2 1 1.453363 + 77 2 1 1.432909 + 78 3 1 1.435103 + 79 2 1 1.434462 + 80 1 1 1.434661 + 81 4 1 1.445881 + 82 2 1 1.442548 + 83 4 1 1.430097 + 84 1 1 1.430119 + 85 1 1 1.430315 + 86 2 1 1.437584 + 87 3 1 1.409738 + 88 3 1 1.422388 + 89 2 1 1.422509 + 90 4 1 1.439432 + 91 2 1 1.430175 + 92 1 1 1.418002 + 93 NA 1 1.423812 + 94 3 1 1.423473 + 95 1 1 1.434412 + 96 3 1 1.450844 + 97 2 1 1.433371 + 98 2 1 1.444378 + 99 4 1 1.422523 + 100 3 1 1.410394 + + $m1b$spM_lvlone + center scale + O2 NA NA + (Intercept) NA NA + C1 1.434101 0.01299651 + + $m1b$mu_reg_ordinal + [1] 0 + + $m1b$tau_reg_ordinal + [1] 1e-04 + + $m1b$mu_delta_ordinal + [1] 0 + + $m1b$tau_delta_ordinal + [1] 1e-04 + + + $m2a + $m2a$M_lvlone + O1 C2 (Intercept) + 1 2 0.144065882 1 + 2 4 0.032778478 1 + 3 3 0.343008492 1 + 4 2 -0.361887858 1 + 5 3 -0.389600647 1 + 6 1 -0.205306841 1 + 7 3 0.079434830 1 + 8 4 -0.331246757 1 + 9 4 -0.329638800 1 + 10 2 0.167597533 1 + 11 1 0.860207989 1 + 12 3 0.022730640 1 + 13 3 0.217171172 1 + 14 1 -0.403002412 1 + 15 1 0.087369742 1 + 16 4 -0.183870429 1 + 17 2 -0.194577002 1 + 18 3 -0.349718516 1 + 19 4 -0.508781244 1 + 20 1 0.494883111 1 + 21 3 0.258041067 1 + 22 4 -0.922621989 1 + 23 4 0.431254949 1 + 24 2 -0.294218881 1 + 25 1 -0.425548895 1 + 26 3 0.057176054 1 + 27 4 0.289090158 1 + 28 1 -0.473079489 1 + 29 4 -0.385664863 1 + 30 4 -0.154780107 1 + 31 2 0.100536296 1 + 32 3 0.634791958 1 + 33 3 -0.387252617 1 + 34 1 -0.181741088 1 + 35 1 -0.311562695 1 + 36 4 -0.044115907 1 + 37 4 -0.657409991 1 + 38 4 0.159577214 1 + 39 1 -0.460416933 1 + 40 2 NA 1 + 41 1 -0.248909867 1 + 42 1 -0.609021545 1 + 43 2 0.025471883 1 + 44 2 0.066648592 1 + 45 1 -0.276108719 1 + 46 1 -0.179737577 1 + 47 4 0.181190937 1 + 48 4 -0.453871693 1 + 49 2 0.448629602 1 + 50 2 -0.529811821 1 + 51 1 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0.057176054 1 + 27 1 0.289090158 1 + 28 4 -0.473079489 1 + 29 3 -0.385664863 1 + 30 3 -0.154780107 1 + 31 2 0.100536296 1 + 32 2 0.634791958 1 + 33 1 -0.387252617 1 + 34 1 -0.181741088 1 + 35 4 -0.311562695 1 + 36 3 -0.044115907 1 + 37 3 -0.657409991 1 + 38 1 0.159577214 1 + 39 2 -0.460416933 1 + 40 3 NA 1 + 41 3 -0.248909867 1 + 42 3 -0.609021545 1 + 43 3 0.025471883 1 + 44 4 0.066648592 1 + 45 4 -0.276108719 1 + 46 1 -0.179737577 1 + 47 4 0.181190937 1 + 48 4 -0.453871693 1 + 49 1 0.448629602 1 + 50 2 -0.529811821 1 + 51 1 -0.028304571 1 + 52 3 -0.520318482 1 + 53 2 0.171317619 1 + 54 1 0.432732046 1 + 55 2 -0.346286005 1 + 56 3 -0.469375653 1 + 57 NA 0.031021711 1 + 58 4 -0.118837515 1 + 59 4 0.507769984 1 + 60 3 0.271797031 1 + 61 4 -0.124442204 1 + 62 1 0.277677389 1 + 63 4 -0.102893730 1 + 64 4 NA 1 + 65 4 -0.678303052 1 + 66 1 0.478880037 1 + 67 3 -0.428028760 1 + 68 3 0.048119185 1 + 69 4 0.216932805 1 + 70 1 -0.234575269 1 + 71 4 0.006827078 1 + 72 4 -0.456055171 1 + 73 2 0.346486708 1 + 74 4 0.205092215 1 + 75 3 -0.136596858 1 + 76 2 -0.500179043 1 + 77 2 0.527352086 1 + 78 3 0.022742250 1 + 79 2 NA 1 + 80 1 -0.002032440 1 + 81 4 -0.154246160 1 + 82 2 0.140201825 1 + 83 4 -0.141417121 1 + 84 1 NA 1 + 85 1 -0.021285339 1 + 86 2 -0.010196306 1 + 87 3 -0.089747520 1 + 88 3 -0.083699898 1 + 89 2 -0.044061996 1 + 90 4 -0.209291697 1 + 91 2 0.639036426 1 + 92 1 0.094698299 1 + 93 NA -0.055510622 1 + 94 3 -0.421318463 1 + 95 1 0.125295503 1 + 96 3 0.213084904 1 + 97 2 -0.161914659 1 + 98 2 -0.034767685 1 + 99 4 -0.320681689 1 + 100 3 0.058192962 1 + + $m2b$spM_lvlone + center scale + O2 NA NA + C2 -0.06490582 0.3331735 + (Intercept) NA NA + + $m2b$mu_reg_norm + [1] 0 + + $m2b$tau_reg_norm + [1] 1e-04 + + $m2b$shape_tau_norm + [1] 0.01 + + $m2b$rate_tau_norm + [1] 0.01 + + $m2b$mu_reg_ordinal + [1] 0 + + $m2b$tau_reg_ordinal + [1] 1e-04 + + $m2b$mu_delta_ordinal + [1] 0 + + $m2b$tau_delta_ordinal + [1] 1e-04 + + + $m3a + $m3a$M_lvlone + C1 (Intercept) O1.L O1.Q O1.C + 1 1.410531 1 -0.2236068 -0.5 0.6708204 + 2 1.434183 1 0.6708204 0.5 0.2236068 + 3 1.430994 1 0.2236068 -0.5 -0.6708204 + 4 1.453096 1 -0.2236068 -0.5 0.6708204 + 5 1.438344 1 0.2236068 -0.5 -0.6708204 + 6 1.453207 1 -0.6708204 0.5 -0.2236068 + 7 1.425176 1 0.2236068 -0.5 -0.6708204 + 8 1.437908 1 0.6708204 0.5 0.2236068 + 9 1.416911 1 0.6708204 0.5 0.2236068 + 10 1.448638 1 -0.2236068 -0.5 0.6708204 + 11 1.428375 1 -0.6708204 0.5 -0.2236068 + 12 1.450130 1 0.2236068 -0.5 -0.6708204 + 13 1.420545 1 0.2236068 -0.5 -0.6708204 + 14 1.423005 1 -0.6708204 0.5 -0.2236068 + 15 1.435902 1 -0.6708204 0.5 -0.2236068 + 16 1.423901 1 0.6708204 0.5 0.2236068 + 17 1.457208 1 -0.2236068 -0.5 0.6708204 + 18 1.414280 1 0.2236068 -0.5 -0.6708204 + 19 1.443383 1 0.6708204 0.5 0.2236068 + 20 1.434954 1 -0.6708204 0.5 -0.2236068 + 21 1.429499 1 0.2236068 -0.5 -0.6708204 + 22 1.441897 1 0.6708204 0.5 0.2236068 + 23 1.423713 1 0.6708204 0.5 0.2236068 + 24 1.435395 1 -0.2236068 -0.5 0.6708204 + 25 1.425944 1 -0.6708204 0.5 -0.2236068 + 26 1.437115 1 0.2236068 -0.5 -0.6708204 + 27 1.441326 1 0.6708204 0.5 0.2236068 + 28 1.422953 1 -0.6708204 0.5 -0.2236068 + 29 1.437797 1 0.6708204 0.5 0.2236068 + 30 1.472121 1 0.6708204 0.5 0.2236068 + 31 1.421782 1 -0.2236068 -0.5 0.6708204 + 32 1.457672 1 0.2236068 -0.5 -0.6708204 + 33 1.430842 1 0.2236068 -0.5 -0.6708204 + 34 1.431523 1 -0.6708204 0.5 -0.2236068 + 35 1.421395 1 -0.6708204 0.5 -0.2236068 + 36 1.434496 1 0.6708204 0.5 0.2236068 + 37 1.425383 1 0.6708204 0.5 0.2236068 + 38 1.421802 1 0.6708204 0.5 0.2236068 + 39 1.430094 1 -0.6708204 0.5 -0.2236068 + 40 1.447621 1 -0.2236068 -0.5 0.6708204 + 41 1.434797 1 -0.6708204 0.5 -0.2236068 + 42 1.446091 1 -0.6708204 0.5 -0.2236068 + 43 1.445306 1 -0.2236068 -0.5 0.6708204 + 44 1.448783 1 -0.2236068 -0.5 0.6708204 + 45 1.450617 1 -0.6708204 0.5 -0.2236068 + 46 1.415055 1 -0.6708204 0.5 -0.2236068 + 47 1.436590 1 0.6708204 0.5 0.2236068 + 48 1.433938 1 0.6708204 0.5 0.2236068 + 49 1.414941 1 -0.2236068 -0.5 0.6708204 + 50 1.421807 1 -0.2236068 -0.5 0.6708204 + 51 1.453203 1 -0.6708204 0.5 -0.2236068 + 52 1.452129 1 0.2236068 -0.5 -0.6708204 + 53 1.431510 1 -0.6708204 0.5 -0.2236068 + 54 1.430082 1 0.2236068 -0.5 -0.6708204 + 55 1.443492 1 -0.2236068 -0.5 0.6708204 + 56 1.436460 1 0.6708204 0.5 0.2236068 + 57 1.418119 1 -0.2236068 -0.5 0.6708204 + 58 1.434971 1 -0.6708204 0.5 -0.2236068 + 59 1.445599 1 -0.6708204 0.5 -0.2236068 + 60 1.437097 1 0.6708204 0.5 0.2236068 + 61 1.428360 1 -0.2236068 -0.5 0.6708204 + 62 1.440550 1 0.6708204 0.5 0.2236068 + 63 1.443014 1 0.2236068 -0.5 -0.6708204 + 64 1.424298 1 -0.2236068 -0.5 0.6708204 + 65 1.448823 1 0.2236068 -0.5 -0.6708204 + 66 1.425834 1 0.2236068 -0.5 -0.6708204 + 67 1.427102 1 -0.2236068 -0.5 0.6708204 + 68 1.414240 1 -0.6708204 0.5 -0.2236068 + 69 1.456218 1 -0.6708204 0.5 -0.2236068 + 70 1.470594 1 -0.6708204 0.5 -0.2236068 + 71 1.425058 1 -0.6708204 0.5 -0.2236068 + 72 1.432371 1 0.2236068 -0.5 -0.6708204 + 73 1.441656 1 -0.2236068 -0.5 0.6708204 + 74 1.434952 1 -0.2236068 -0.5 0.6708204 + 75 1.402860 1 0.2236068 -0.5 -0.6708204 + 76 1.453363 1 0.2236068 -0.5 -0.6708204 + 77 1.432909 1 0.6708204 0.5 0.2236068 + 78 1.435103 1 0.2236068 -0.5 -0.6708204 + 79 1.434462 1 -0.2236068 -0.5 0.6708204 + 80 1.434661 1 -0.2236068 -0.5 0.6708204 + 81 1.445881 1 0.2236068 -0.5 -0.6708204 + 82 1.442548 1 -0.6708204 0.5 -0.2236068 + 83 1.430097 1 0.2236068 -0.5 -0.6708204 + 84 1.430119 1 -0.2236068 -0.5 0.6708204 + 85 1.430315 1 -0.2236068 -0.5 0.6708204 + 86 1.437584 1 0.6708204 0.5 0.2236068 + 87 1.409738 1 0.2236068 -0.5 -0.6708204 + 88 1.422388 1 -0.2236068 -0.5 0.6708204 + 89 1.422509 1 0.2236068 -0.5 -0.6708204 + 90 1.439432 1 0.2236068 -0.5 -0.6708204 + 91 1.430175 1 0.6708204 0.5 0.2236068 + 92 1.418002 1 -0.6708204 0.5 -0.2236068 + 93 1.423812 1 0.6708204 0.5 0.2236068 + 94 1.423473 1 -0.6708204 0.5 -0.2236068 + 95 1.434412 1 -0.6708204 0.5 -0.2236068 + 96 1.450844 1 0.2236068 -0.5 -0.6708204 + 97 1.433371 1 -0.6708204 0.5 -0.2236068 + 98 1.444378 1 0.2236068 -0.5 -0.6708204 + 99 1.422523 1 0.2236068 -0.5 -0.6708204 + 100 1.410394 1 0.2236068 -0.5 -0.6708204 + + $m3a$mu_reg_norm + [1] 0 + + $m3a$tau_reg_norm + [1] 1e-04 + + $m3a$shape_tau_norm + [1] 0.01 + + $m3a$rate_tau_norm + [1] 0.01 + + + $m3b + $m3b$M_lvlone + C1 O2 (Intercept) O22 O23 O24 + 1 1.410531 4 1 NA NA NA + 2 1.434183 4 1 NA NA NA + 3 1.430994 4 1 NA NA NA + 4 1.453096 1 1 NA NA NA + 5 1.438344 2 1 NA NA NA + 6 1.453207 3 1 NA NA NA + 7 1.425176 4 1 NA NA NA + 8 1.437908 2 1 NA NA NA + 9 1.416911 4 1 NA NA NA + 10 1.448638 3 1 NA NA NA + 11 1.428375 2 1 NA NA NA + 12 1.450130 1 1 NA NA NA + 13 1.420545 1 1 NA NA NA + 14 1.423005 1 1 NA NA NA + 15 1.435902 4 1 NA NA NA + 16 1.423901 3 1 NA NA NA + 17 1.457208 3 1 NA NA NA + 18 1.414280 1 1 NA NA NA + 19 1.443383 3 1 NA NA NA + 20 1.434954 1 1 NA NA NA + 21 1.429499 3 1 NA NA NA + 22 1.441897 3 1 NA NA NA + 23 1.423713 2 1 NA NA NA + 24 1.435395 3 1 NA NA NA + 25 1.425944 2 1 NA NA NA + 26 1.437115 2 1 NA NA NA + 27 1.441326 1 1 NA NA NA + 28 1.422953 4 1 NA NA NA + 29 1.437797 3 1 NA NA NA + 30 1.472121 3 1 NA NA NA + 31 1.421782 2 1 NA NA NA + 32 1.457672 2 1 NA NA NA + 33 1.430842 1 1 NA NA NA + 34 1.431523 1 1 NA NA NA + 35 1.421395 4 1 NA NA NA + 36 1.434496 3 1 NA NA NA + 37 1.425383 3 1 NA NA NA + 38 1.421802 1 1 NA NA NA + 39 1.430094 2 1 NA NA NA + 40 1.447621 3 1 NA NA NA + 41 1.434797 3 1 NA NA NA + 42 1.446091 3 1 NA NA NA + 43 1.445306 3 1 NA NA NA + 44 1.448783 4 1 NA NA NA + 45 1.450617 4 1 NA NA NA + 46 1.415055 1 1 NA NA NA + 47 1.436590 4 1 NA NA NA + 48 1.433938 4 1 NA NA NA + 49 1.414941 1 1 NA NA NA + 50 1.421807 2 1 NA NA NA + 51 1.453203 1 1 NA NA NA + 52 1.452129 3 1 NA NA NA + 53 1.431510 2 1 NA NA NA + 54 1.430082 1 1 NA NA NA + 55 1.443492 2 1 NA NA NA + 56 1.436460 3 1 NA NA NA + 57 1.418119 NA 1 NA NA NA + 58 1.434971 4 1 NA NA NA + 59 1.445599 4 1 NA NA NA + 60 1.437097 3 1 NA NA NA + 61 1.428360 4 1 NA NA NA + 62 1.440550 1 1 NA NA NA + 63 1.443014 4 1 NA NA NA + 64 1.424298 4 1 NA NA NA + 65 1.448823 4 1 NA NA NA + 66 1.425834 1 1 NA NA NA + 67 1.427102 3 1 NA NA NA + 68 1.414240 3 1 NA NA NA + 69 1.456218 4 1 NA NA NA + 70 1.470594 1 1 NA NA NA + 71 1.425058 4 1 NA NA NA + 72 1.432371 4 1 NA NA NA + 73 1.441656 2 1 NA NA NA + 74 1.434952 4 1 NA NA NA + 75 1.402860 3 1 NA NA NA + 76 1.453363 2 1 NA NA NA + 77 1.432909 2 1 NA NA NA + 78 1.435103 3 1 NA NA NA + 79 1.434462 2 1 NA NA NA + 80 1.434661 1 1 NA NA NA + 81 1.445881 4 1 NA NA NA + 82 1.442548 2 1 NA NA NA + 83 1.430097 4 1 NA NA NA + 84 1.430119 1 1 NA NA NA + 85 1.430315 1 1 NA NA NA + 86 1.437584 2 1 NA NA NA + 87 1.409738 3 1 NA NA NA + 88 1.422388 3 1 NA NA NA + 89 1.422509 2 1 NA NA NA + 90 1.439432 4 1 NA NA NA + 91 1.430175 2 1 NA NA NA + 92 1.418002 1 1 NA NA NA + 93 1.423812 NA 1 NA NA NA + 94 1.423473 3 1 NA NA NA + 95 1.434412 1 1 NA NA NA + 96 1.450844 3 1 NA NA NA + 97 1.433371 2 1 NA NA NA + 98 1.444378 2 1 NA NA NA + 99 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7 NA NA + 8 NA NA + 9 NA NA + 10 NA NA + 11 NA NA + 12 NA NA + 13 NA NA + 14 NA NA + 15 NA NA + 16 NA NA + 17 NA NA + 18 NA NA + 19 NA NA + 20 NA NA + 21 NA NA + 22 NA NA + 23 NA NA + 24 NA NA + 25 NA NA + 26 NA NA + 27 NA NA + 28 NA NA + 29 NA NA + 30 NA NA + 31 NA NA + 32 NA NA + 33 NA NA + 34 NA NA + 35 NA NA + 36 NA NA + 37 NA NA + 38 NA NA + 39 NA NA + 40 NA NA + 41 NA NA + 42 NA NA + 43 NA NA + 44 NA NA + 45 NA NA + 46 NA NA + 47 NA NA + 48 NA NA + 49 NA NA + 50 NA NA + 51 NA NA + 52 NA NA + 53 NA NA + 54 NA NA + 55 NA NA + 56 NA NA + 57 NA NA + 58 NA NA + 59 NA NA + 60 NA NA + 61 NA NA + 62 NA NA + 63 NA NA + 64 NA NA + 65 NA NA + 66 NA NA + 67 NA NA + 68 NA NA + 69 NA NA + 70 NA NA + 71 NA NA + 72 NA NA + 73 NA NA + 74 NA NA + 75 NA NA + 76 NA NA + 77 NA NA + 78 NA NA + 79 NA NA + 80 NA NA + 81 NA NA + 82 NA NA + 83 NA NA + 84 NA NA + 85 NA NA + 86 NA NA + 87 NA NA + 88 NA NA + 89 NA NA + 90 NA NA + 91 NA NA + 92 NA NA + 93 NA NA + 94 NA NA + 95 NA NA + 96 NA NA + 97 NA NA + 98 NA NA + 99 NA NA + 100 NA NA + + $m6e$spM_lvlone + center scale + O1 NA NA + C2 -0.064905817 0.33317347 + M2 NA NA + O2 NA NA + (Intercept) NA NA + C1 1.434100545 0.01299651 + M22 NA NA + M23 NA NA + M24 NA NA + O22 NA NA + O23 NA NA + O24 NA NA + M22:C2 -0.035803577 0.16299962 + M23:C2 -0.008443652 0.22326710 + M24:C2 -0.014114090 0.17029222 + + $m6e$mu_reg_norm + [1] 0 + + $m6e$tau_reg_norm + [1] 1e-04 + + $m6e$shape_tau_norm + [1] 0.01 + + $m6e$rate_tau_norm + [1] 0.01 + + $m6e$mu_reg_multinomial + [1] 0 + + $m6e$tau_reg_multinomial + [1] 1e-04 + + $m6e$mu_reg_ordinal + [1] 0 + + $m6e$tau_reg_ordinal + [1] 1e-04 + + $m6e$mu_delta_ordinal + [1] 0 + + $m6e$tau_delta_ordinal + [1] 1e-04 + + + +# jagsmodel remains the same + + Code + lapply(models, "[[", "jagsmodel") + Output + $m0a + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- 0 + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + } + $m0b + model { + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- 0 + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } + $m1a + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + } + $m1b + model { + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + } + + # Priors for the model for O2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } + $m2a + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2b + model { + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + } + + # Priors for the model for O2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3a + model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] + + M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4] + } + + # Priors for the model for C1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + } + $m3b + model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + } + + # Priors for the model for C1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- 0 + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } + $m4a + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 6] * beta[1] + M_lvlone[i, 7] * beta[2] + + M_lvlone[i, 8] * beta[3] + M_lvlone[i, 9] * beta[4] + + M_lvlone[i, 10] * beta[5] + M_lvlone[i, 11] * beta[6] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[7] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[8] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[9] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[10] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[11] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } + $m4b + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + } + + # Priors for the model for O1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] + + M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] + + M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] + + M_lvlone[i, 14] * alpha[7] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8] + + M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- M_lvlone[i, 12] * alpha[9] + M_lvlone[i, 13] * alpha[10] + + M_lvlone[i, 14] * alpha[11] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[12] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + + M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0) + + } + + # Priors for the model for O2 + for (k in 9:12) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] + } + + } + $m5a + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } + $m5b + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:13) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] + } + + } + $m5c + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] + } + + } + $m5d + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] + M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] + M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] + } + + } + $m5e + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- 0 + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] + + M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] + + M_lvlone[i, 9] * beta[4] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + + M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] + + M_lvlone[i, 12] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] + + M_lvlone[i, 9] * beta[15] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] + + M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] + + M_lvlone[i, 12] * beta[19] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] + + M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] + + M_lvlone[i, 9] * beta[26] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] + + M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] + + M_lvlone[i, 12] * beta[30] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33] + + p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) + p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:33) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] + M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] + M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] + } + + } + $m6a + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + } + $m6b + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:13) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] + } + + } + $m6c + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] + } + + } + $m6d + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + + M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + + M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9] + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] + M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] + M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] + } + + } + $m6e + model { + + # Cumulative logit model for O1 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) + eta_O1[i] <- 0 + + eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] + + M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] + + M_lvlone[i, 9] * beta[4] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + + M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] + + M_lvlone[i, 12] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + + M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] + + M_lvlone[i, 9] * beta[15] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] + + M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] + + M_lvlone[i, 12] * beta[19] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22] + eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] + + M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] + + M_lvlone[i, 9] * beta[26] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] + + M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] + + M_lvlone[i, 12] * beta[30] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33] + + p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) + p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) + p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) + p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) + + logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] + logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] + logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] + } + + # Priors for the model for O1 + for (k in 1:33) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) + gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + M_lvlone[i, 12] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + M_lvlone[i, 12] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + M_lvlone[i, 12] * alpha[23] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] + M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] + M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] + } + + } + +# GRcrit and MCerror give same result + + Code + lapply(models0, GR_crit, multivariate = FALSE) + Output + $m0a + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m0b + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_O2[1] NaN NaN + gamma_O2[2] NaN NaN + gamma_O2[3] NaN NaN + + + $m1a + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + C1 NaN NaN + + + $m1b + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_O2[1] NaN NaN + gamma_O2[2] NaN NaN + gamma_O2[3] NaN NaN + C1 NaN NaN + + + $m2a + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + C2 NaN NaN + + + $m2b + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_O2[1] NaN NaN + gamma_O2[2] NaN NaN + gamma_O2[3] NaN NaN + C2 NaN NaN + + + $m3a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + sigma_C1 NaN NaN + + + $m3b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + sigma_C1 NaN NaN + + + $m4a + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + abs(C1 - C2) NaN NaN + log(C1) NaN NaN + O22:abs(C1 - C2) NaN NaN + O23:abs(C1 - C2) NaN NaN + O24:abs(C1 - C2) NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m4b + Potential scale reduction factors: + + Point est. + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN + abs(C1 - C2) NaN + log(C1) NaN + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + gamma_O1[1] NaN + gamma_O1[2] NaN + gamma_O1[3] NaN + Upper C.I. + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN + abs(C1 - C2) NaN + log(C1) NaN + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + gamma_O1[1] NaN + gamma_O1[2] NaN + gamma_O1[3] NaN + + + $m5a + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + O12: C1 NaN NaN + O12: C2 NaN NaN + O13: C1 NaN NaN + O13: C2 NaN NaN + O14: C1 NaN NaN + O14: C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m5b + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + C1:C2 NaN NaN + O12: C1 NaN NaN + O12: C2 NaN NaN + O13: C1 NaN NaN + O13: C2 NaN NaN + O14: C1 NaN NaN + O14: C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m5c + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + O12: C1 NaN NaN + O12: C2 NaN NaN + O12: C1:C2 NaN NaN + O13: C1 NaN NaN + O13: C2 NaN NaN + O13: C1:C2 NaN NaN + O14: C1 NaN NaN + O14: C2 NaN NaN + O14: C1:C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m5d + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + M22:C2 NaN NaN + M23:C2 NaN NaN + M24:C2 NaN NaN + O12: C1 NaN NaN + O12: C2 NaN NaN + O13: C1 NaN NaN + O13: C2 NaN NaN + O14: C1 NaN NaN + O14: C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m5e + Potential scale reduction factors: + + Point est. Upper C.I. + O12: C1 NaN NaN + O12: M22 NaN NaN + O12: M23 NaN NaN + O12: M24 NaN NaN + O12: C2 NaN NaN + O12: O22 NaN NaN + O12: O23 NaN NaN + O12: O24 NaN NaN + O12: M22:C2 NaN NaN + O12: M23:C2 NaN NaN + O12: M24:C2 NaN NaN + O13: C1 NaN NaN + O13: M22 NaN NaN + O13: M23 NaN NaN + O13: M24 NaN NaN + O13: C2 NaN NaN + O13: O22 NaN NaN + O13: O23 NaN NaN + O13: O24 NaN NaN + O13: M22:C2 NaN NaN + O13: M23:C2 NaN NaN + O13: M24:C2 NaN NaN + O14: C1 NaN NaN + O14: M22 NaN NaN + O14: M23 NaN NaN + O14: M24 NaN NaN + O14: C2 NaN NaN + O14: O22 NaN NaN + O14: O23 NaN NaN + O14: O24 NaN NaN + O14: M22:C2 NaN NaN + O14: M23:C2 NaN NaN + O14: M24:C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m6a + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + O12: C1 NaN NaN + O12: C2 NaN NaN + O13: C1 NaN NaN + O13: C2 NaN NaN + O14: C1 NaN NaN + O14: C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m6b + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + C1:C2 NaN NaN + O12: C1 NaN NaN + O12: C2 NaN NaN + O13: C1 NaN NaN + O13: C2 NaN NaN + O14: C1 NaN NaN + O14: C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m6c + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + O12: C1 NaN NaN + O12: C2 NaN NaN + O12: C1:C2 NaN NaN + O13: C1 NaN NaN + O13: C2 NaN NaN + O13: C1:C2 NaN NaN + O14: C1 NaN NaN + O14: C2 NaN NaN + O14: C1:C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m6d + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + M22:C2 NaN NaN + M23:C2 NaN NaN + M24:C2 NaN NaN + O12: C1 NaN NaN + O12: C2 NaN NaN + O13: C1 NaN NaN + O13: C2 NaN NaN + O14: C1 NaN NaN + O14: C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + $m6e + Potential scale reduction factors: + + Point est. Upper C.I. + O12: C1 NaN NaN + O12: M22 NaN NaN + O12: M23 NaN NaN + O12: M24 NaN NaN + O12: C2 NaN NaN + O12: O22 NaN NaN + O12: O23 NaN NaN + O12: O24 NaN NaN + O12: M22:C2 NaN NaN + O12: M23:C2 NaN NaN + O12: M24:C2 NaN NaN + O13: C1 NaN NaN + O13: M22 NaN NaN + O13: M23 NaN NaN + O13: M24 NaN NaN + O13: C2 NaN NaN + O13: O22 NaN NaN + O13: O23 NaN NaN + O13: O24 NaN NaN + O13: M22:C2 NaN NaN + O13: M23:C2 NaN NaN + O13: M24:C2 NaN NaN + O14: C1 NaN NaN + O14: M22 NaN NaN + O14: M23 NaN NaN + O14: M24 NaN NaN + O14: C2 NaN NaN + O14: O22 NaN NaN + O14: O23 NaN NaN + O14: O24 NaN NaN + O14: M22:C2 NaN NaN + O14: M23:C2 NaN NaN + O14: M24:C2 NaN NaN + gamma_O1[1] NaN NaN + gamma_O1[2] NaN NaN + gamma_O1[3] NaN NaN + + + +--- + + Code + lapply(models0, MC_error) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + est MCSE SD MCSE/SD + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m0b + est MCSE SD MCSE/SD + gamma_O2[1] 0 0 0 NaN + gamma_O2[2] 0 0 0 NaN + gamma_O2[3] 0 0 0 NaN + + $m1a + est MCSE SD MCSE/SD + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + C1 0 0 0 NaN + + $m1b + est MCSE SD MCSE/SD + gamma_O2[1] 0 0 0 NaN + gamma_O2[2] 0 0 0 NaN + gamma_O2[3] 0 0 0 NaN + C1 0 0 0 NaN + + $m2a + est MCSE SD MCSE/SD + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + C2 0 0 0 NaN + + $m2b + est MCSE SD MCSE/SD + gamma_O2[1] 0 0 0 NaN + gamma_O2[2] 0 0 0 NaN + gamma_O2[3] 0 0 0 NaN + C2 0 0 0 NaN + + $m3a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m3b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m4a + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + abs(C1 - C2) 0 0 0 NaN + log(C1) 0 0 0 NaN + O22:abs(C1 - C2) 0 0 0 NaN + O23:abs(C1 - C2) 0 0 0 NaN + O24:abs(C1 - C2) 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m4b + est MCSE SD MCSE/SD + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 NaN + abs(C1 - C2) 0 0 0 NaN + log(C1) 0 0 0 NaN + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m5a + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + O12: C1 0 0 0 NaN + O12: C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m5b + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + C1:C2 0 0 0 NaN + O12: C1 0 0 0 NaN + O12: C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m5c + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + O12: C1 0 0 0 NaN + O12: C2 0 0 0 NaN + O12: C1:C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: C2 0 0 0 NaN + O13: C1:C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: C2 0 0 0 NaN + O14: C1:C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m5d + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + M22:C2 0 0 0 NaN + M23:C2 0 0 0 NaN + M24:C2 0 0 0 NaN + O12: C1 0 0 0 NaN + O12: C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m5e + est MCSE SD MCSE/SD + O12: C1 0 0 0 NaN + O12: M22 0 0 0 NaN + O12: M23 0 0 0 NaN + O12: M24 0 0 0 NaN + O12: C2 0 0 0 NaN + O12: O22 0 0 0 NaN + O12: O23 0 0 0 NaN + O12: O24 0 0 0 NaN + O12: M22:C2 0 0 0 NaN + O12: M23:C2 0 0 0 NaN + O12: M24:C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: M22 0 0 0 NaN + O13: M23 0 0 0 NaN + O13: M24 0 0 0 NaN + O13: C2 0 0 0 NaN + O13: O22 0 0 0 NaN + O13: O23 0 0 0 NaN + O13: O24 0 0 0 NaN + O13: M22:C2 0 0 0 NaN + O13: M23:C2 0 0 0 NaN + O13: M24:C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: M22 0 0 0 NaN + O14: M23 0 0 0 NaN + O14: M24 0 0 0 NaN + O14: C2 0 0 0 NaN + O14: O22 0 0 0 NaN + O14: O23 0 0 0 NaN + O14: O24 0 0 0 NaN + O14: M22:C2 0 0 0 NaN + O14: M23:C2 0 0 0 NaN + O14: M24:C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m6a + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + O12: C1 0 0 0 NaN + O12: C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m6b + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + C1:C2 0 0 0 NaN + O12: C1 0 0 0 NaN + O12: C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m6c + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + O12: C1 0 0 0 NaN + O12: C2 0 0 0 NaN + O12: C1:C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: C2 0 0 0 NaN + O13: C1:C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: C2 0 0 0 NaN + O14: C1:C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m6d + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + M22:C2 0 0 0 NaN + M23:C2 0 0 0 NaN + M24:C2 0 0 0 NaN + O12: C1 0 0 0 NaN + O12: C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + $m6e + est MCSE SD MCSE/SD + O12: C1 0 0 0 NaN + O12: M22 0 0 0 NaN + O12: M23 0 0 0 NaN + O12: M24 0 0 0 NaN + O12: C2 0 0 0 NaN + O12: O22 0 0 0 NaN + O12: O23 0 0 0 NaN + O12: O24 0 0 0 NaN + O12: M22:C2 0 0 0 NaN + O12: M23:C2 0 0 0 NaN + O12: M24:C2 0 0 0 NaN + O13: C1 0 0 0 NaN + O13: M22 0 0 0 NaN + O13: M23 0 0 0 NaN + O13: M24 0 0 0 NaN + O13: C2 0 0 0 NaN + O13: O22 0 0 0 NaN + O13: O23 0 0 0 NaN + O13: O24 0 0 0 NaN + O13: M22:C2 0 0 0 NaN + O13: M23:C2 0 0 0 NaN + O13: M24:C2 0 0 0 NaN + O14: C1 0 0 0 NaN + O14: M22 0 0 0 NaN + O14: M23 0 0 0 NaN + O14: M24 0 0 0 NaN + O14: C2 0 0 0 NaN + O14: O22 0 0 0 NaN + O14: O23 0 0 0 NaN + O14: O24 0 0 0 NaN + O14: M22:C2 0 0 0 NaN + O14: M23:C2 0 0 0 NaN + O14: M24:C2 0 0 0 NaN + gamma_O1[1] 0 0 0 NaN + gamma_O1[2] 0 0 0 NaN + gamma_O1[3] 0 0 0 NaN + + +# summary output remained the same on Windows + + Code + lapply(models0, print) + Output + + Call: + clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 + 0 0 0 + + Call: + clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O2" + + + Coefficients: + O2 > 1 O2 > 2 O2 > 3 + 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 C1 + 0 0 0 0 + + Call: + clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O2" + + + Coefficients: + O2 > 1 O2 > 2 O2 > 3 C1 + 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 C2 + 0 0 0 0 + + Call: + clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O2" + + + Coefficients: + O2 > 1 O2 > 2 O2 > 3 C2 + 0 0 0 0 + + Call: + lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) O1.L O1.Q O1.C + 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) O22 O23 O24 + 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 + 0 0 0 0 + M23 M24 O22 O23 + 0 0 0 0 + O24 abs(C1 - C2) log(C1) O22:abs(C1 - C2) + 0 0 0 0 + O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 + + Call: + clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 + 0 + O1 > 2 + 0 + O1 > 3 + 0 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + + Call: + clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 C2 C1 C2 + 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 + 0 0 0 0 0 0 0 0 0 0 0 + C2 C1 C2 C1 C2 + 0 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * + C2), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 + 0 0 0 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 + 0 0 0 0 0 0 0 0 0 0 0 + M24:C2 C1 C2 C1 C2 C1 C2 + 0 0 0 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + + M2 * C2 + O2, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 + 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 C2 C1 C2 + 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 + 0 0 0 0 0 0 0 0 0 0 0 + C2 C1 C2 C1 C2 + 0 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 + 0 0 0 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 + 0 0 0 0 0 0 0 0 0 0 0 + M24:C2 C1 C2 C1 C2 C1 C2 + 0 0 0 0 0 0 0 + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + + M2 * C2 + O2, rev = "O1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 + 0 0 0 + $m0a + + Call: + clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 + 0 0 0 + + $m0b + + Call: + clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O2" + + + Coefficients: + O2 > 1 O2 > 2 O2 > 3 + 0 0 0 + + $m1a + + Call: + clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 C1 + 0 0 0 0 + + $m1b + + Call: + clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O2" + + + Coefficients: + O2 > 1 O2 > 2 O2 > 3 C1 + 0 0 0 0 + + $m2a + + Call: + clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 C2 + 0 0 0 0 + + $m2b + + Call: + clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O2" + + + Coefficients: + O2 > 1 O2 > 2 O2 > 3 C2 + 0 0 0 0 + + $m3a + + Call: + lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) O1.L O1.Q O1.C + 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m3b + + Call: + lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) O22 O23 O24 + 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m4a + + Call: + clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 + 0 0 0 0 + M23 M24 O22 O23 + 0 0 0 0 + O24 abs(C1 - C2) log(C1) O22:abs(C1 - C2) + 0 0 0 0 + O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 + + $m4b + + Call: + clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 + 0 + O1 > 2 + 0 + O1 > 3 + 0 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + + $m5a + + Call: + clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 C2 C1 C2 + 0 0 0 0 + + $m5b + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 + 0 0 0 0 0 0 0 0 0 0 0 + C2 C1 C2 C1 C2 + 0 0 0 0 0 + + $m5c + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * + C2), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 + 0 0 0 0 0 0 0 + + $m5d + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 + 0 0 0 0 0 0 0 0 0 0 0 + M24:C2 C1 C2 C1 C2 C1 C2 + 0 0 0 0 0 0 0 + + $m5e + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + + M2 * C2 + O2, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 > 1 O1 > 2 O1 > 3 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 + 0 0 0 + + $m6a + + Call: + clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 C2 C1 C2 + 0 0 0 0 + + $m6b + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 + 0 0 0 0 0 0 0 0 0 0 0 + C2 C1 C2 C1 C2 + 0 0 0 0 0 + + $m6c + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 + 0 0 0 0 0 0 0 + + $m6d + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 + 0 0 0 0 0 0 0 0 0 0 0 + M24:C2 C1 C2 C1 C2 C1 C2 + 0 0 0 0 0 0 0 + + $m6e + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + + M2 * C2 + O2, rev = "O1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit model for "O1" + + + Coefficients: + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 + 0 0 0 0 0 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 + 0 0 0 + + +--- + + Code + lapply(models0, coef) + Output + $m0a + $m0a$O1 + O1 > 1 O1 > 2 O1 > 3 + 0 0 0 + + + $m0b + $m0b$O2 + O2 > 1 O2 > 2 O2 > 3 + 0 0 0 + + + $m1a + $m1a$O1 + C1 O1 > 1 O1 > 2 O1 > 3 + 0 0 0 0 + + + $m1b + $m1b$O2 + C1 O2 > 1 O2 > 2 O2 > 3 + 0 0 0 0 + + + $m2a + $m2a$O1 + C2 O1 > 1 O1 > 2 O1 > 3 + 0 0 0 0 + + + $m2b + $m2b$O2 + C2 O2 > 1 O2 > 2 O2 > 3 + 0 0 0 0 + + + $m3a + $m3a$C1 + (Intercept) O1.L O1.Q O1.C sigma_C1 + 0 0 0 0 0 + + + $m3b + $m3b$C1 + (Intercept) O22 O23 O24 sigma_C1 + 0 0 0 0 0 + + + $m4a + $m4a$O1 + M22 M23 M24 O22 + 0 0 0 0 + O23 O24 abs(C1 - C2) log(C1) + 0 0 0 0 + O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) O1 > 1 + 0 0 0 0 + O1 > 2 O1 > 3 + 0 0 + + + $m4b + $m4b$O1 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + O1 > 1 + 0 + O1 > 2 + 0 + O1 > 3 + 0 + + + $m5a + $m5a$O1 + M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1 + 0 0 0 0 0 0 0 0 0 0 0 + C2 O1 > 1 O1 > 2 O1 > 3 + 0 0 0 0 + + + $m5b + $m5b$O1 + M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 C2 O1 > 1 O1 > 2 O1 > 3 + 0 0 0 0 0 + + + $m5c + $m5c$O1 + M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1:C2 C1 C2 C1:C2 O1 > 1 O1 > 2 O1 > 3 + 0 0 0 0 0 0 0 + + + $m5d + $m5d$O1 + M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 C2 C1 C2 O1 > 1 O1 > 2 O1 > 3 + 0 0 0 0 0 0 0 + + + $m5e + $m5e$O1 + C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + O1 > 1 O1 > 2 O1 > 3 + 0 0 0 + + + $m6a + $m6a$O1 + M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1 + 0 0 0 0 0 0 0 0 0 0 0 + C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 + 0 0 0 0 + + + $m6b + $m6b$O1 + M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 + 0 0 0 0 0 + + + $m6c + $m6c$O1 + M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1:C2 C1 C2 C1:C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 + 0 0 0 0 0 0 0 + + + $m6d + $m6d$O1 + M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 C2 C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 + 0 0 0 0 0 0 0 + + + $m6e + $m6e$O1 + C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 + 0 0 0 + + + +--- + + Code + lapply(models0, confint) + Output + $m0a + $m0a$O1 + 2.5% 97.5% + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m0b + $m0b$O2 + 2.5% 97.5% + O2 > 1 0 0 + O2 > 2 0 0 + O2 > 3 0 0 + + + $m1a + $m1a$O1 + 2.5% 97.5% + C1 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m1b + $m1b$O2 + 2.5% 97.5% + C1 0 0 + O2 > 1 0 0 + O2 > 2 0 0 + O2 > 3 0 0 + + + $m2a + $m2a$O1 + 2.5% 97.5% + C2 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m2b + $m2b$O2 + 2.5% 97.5% + C2 0 0 + O2 > 1 0 0 + O2 > 2 0 0 + O2 > 3 0 0 + + + $m3a + $m3a$C1 + 2.5% 97.5% + (Intercept) 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + sigma_C1 0 0 + + + $m3b + $m3b$C1 + 2.5% 97.5% + (Intercept) 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + sigma_C1 0 0 + + + $m4a + $m4a$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + O22:abs(C1 - C2) 0 0 + O23:abs(C1 - C2) 0 0 + O24:abs(C1 - C2) 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m4b + $m4b$O1 + 2.5% 97.5% + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m5a + $m5a$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m5b + $m5b$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + C1:C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m5c + $m5c$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + C1 0 0 + C2 0 0 + C1:C2 0 0 + C1 0 0 + C2 0 0 + C1:C2 0 0 + C1 0 0 + C2 0 0 + C1:C2 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m5d + $m5d$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m5e + $m5e$O1 + 2.5% 97.5% + C1 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + C1 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + C1 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + O1 > 1 0 0 + O1 > 2 0 0 + O1 > 3 0 0 + + + $m6a + $m6a$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + O1 ≤ 1 0 0 + O1 ≤ 2 0 0 + O1 ≤ 3 0 0 + + + $m6b + $m6b$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + C1:C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + O1 ≤ 1 0 0 + O1 ≤ 2 0 0 + O1 ≤ 3 0 0 + + + $m6c + $m6c$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + C1 0 0 + C2 0 0 + C1:C2 0 0 + C1 0 0 + C2 0 0 + C1:C2 0 0 + C1 0 0 + C2 0 0 + C1:C2 0 0 + O1 ≤ 1 0 0 + O1 ≤ 2 0 0 + O1 ≤ 3 0 0 + + + $m6d + $m6d$O1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + O1 ≤ 1 0 0 + O1 ≤ 2 0 0 + O1 ≤ 3 0 0 + + + $m6e + $m6e$O1 + 2.5% 97.5% + C1 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + C1 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + C1 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + O1 ≤ 1 0 0 + O1 ≤ 2 0 0 + O1 ≤ 3 0 0 + + + +--- + + Code + lapply(models0, summary) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m0b + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O2 > 1 0 0 0 0 0 NaN NaN + O2 > 2 0 0 0 0 0 NaN NaN + O2 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m1a + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m1b + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O2 > 1 0 0 0 0 0 NaN NaN + O2 > 2 0 0 0 0 0 NaN NaN + O2 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m2a + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m2b + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O2 > 1 0 0 0 0 0 NaN NaN + O2 > 2 0 0 0 0 0 NaN NaN + O2 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m3a + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m3b + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m4a + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m4b + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 0 + abs(C1 - C2) 0 0 0 0 + log(C1) 0 0 0 0 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0 + tail-prob. GR-crit + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 NaN + abs(C1 - C2) 0 NaN + log(C1) 0 NaN + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN + MCE/SD + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN + abs(C1 - C2) NaN + log(C1) NaN + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m5a + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m5b + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + C1:C2 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m5c + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * + C2), seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O12: C1:C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O13: C1:C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + O14: C1:C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m5d + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + M22:C2 0 0 0 0 0 NaN NaN + M23:C2 0 0 0 0 0 NaN NaN + M24:C2 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m5e + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + + M2 * C2 + O2, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O12: C1 0 0 0 0 0 NaN NaN + O12: M22 0 0 0 0 0 NaN NaN + O12: M23 0 0 0 0 0 NaN NaN + O12: M24 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O12: O22 0 0 0 0 0 NaN NaN + O12: O23 0 0 0 0 0 NaN NaN + O12: O24 0 0 0 0 0 NaN NaN + O12: M22:C2 0 0 0 0 0 NaN NaN + O12: M23:C2 0 0 0 0 0 NaN NaN + O12: M24:C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: M22 0 0 0 0 0 NaN NaN + O13: M23 0 0 0 0 0 NaN NaN + O13: M24 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O13: O22 0 0 0 0 0 NaN NaN + O13: O23 0 0 0 0 0 NaN NaN + O13: O24 0 0 0 0 0 NaN NaN + O13: M22:C2 0 0 0 0 0 NaN NaN + O13: M23:C2 0 0 0 0 0 NaN NaN + O13: M24:C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: M22 0 0 0 0 0 NaN NaN + O14: M23 0 0 0 0 0 NaN NaN + O14: M24 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + O14: O22 0 0 0 0 0 NaN NaN + O14: O23 0 0 0 0 0 NaN NaN + O14: O24 0 0 0 0 0 NaN NaN + O14: M22:C2 0 0 0 0 0 NaN NaN + O14: M23:C2 0 0 0 0 0 NaN NaN + O14: M24:C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 > 1 0 0 0 0 0 NaN NaN + O1 > 2 0 0 0 0 0 NaN NaN + O1 > 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m6a + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 ≤ 1 0 0 0 0 0 NaN NaN + O1 ≤ 2 0 0 0 0 0 NaN NaN + O1 ≤ 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m6b + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + C1:C2 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 ≤ 1 0 0 0 0 0 NaN NaN + O1 ≤ 2 0 0 0 0 0 NaN NaN + O1 ≤ 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m6c + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O12: C1:C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O13: C1:C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + O14: C1:C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 ≤ 1 0 0 0 0 0 NaN NaN + O1 ≤ 2 0 0 0 0 0 NaN NaN + O1 ≤ 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m6d + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + + C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + M22:C2 0 0 0 0 0 NaN NaN + M23:C2 0 0 0 0 0 NaN NaN + M24:C2 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 ≤ 1 0 0 0 0 0 NaN NaN + O1 ≤ 2 0 0 0 0 0 NaN NaN + O1 ≤ 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m6e + + Bayesian cumulative logit model fitted with JointAI + + Call: + clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + + M2 * C2 + O2, rev = "O1", seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O12: C1 0 0 0 0 0 NaN NaN + O12: M22 0 0 0 0 0 NaN NaN + O12: M23 0 0 0 0 0 NaN NaN + O12: M24 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O12: O22 0 0 0 0 0 NaN NaN + O12: O23 0 0 0 0 0 NaN NaN + O12: O24 0 0 0 0 0 NaN NaN + O12: M22:C2 0 0 0 0 0 NaN NaN + O12: M23:C2 0 0 0 0 0 NaN NaN + O12: M24:C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: M22 0 0 0 0 0 NaN NaN + O13: M23 0 0 0 0 0 NaN NaN + O13: M24 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O13: O22 0 0 0 0 0 NaN NaN + O13: O23 0 0 0 0 0 NaN NaN + O13: O24 0 0 0 0 0 NaN NaN + O13: M22:C2 0 0 0 0 0 NaN NaN + O13: M23:C2 0 0 0 0 0 NaN NaN + O13: M24:C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: M22 0 0 0 0 0 NaN NaN + O14: M23 0 0 0 0 0 NaN NaN + O14: M24 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + O14: O22 0 0 0 0 0 NaN NaN + O14: O23 0 0 0 0 0 NaN NaN + O14: O24 0 0 0 0 0 NaN NaN + O14: M22:C2 0 0 0 0 0 NaN NaN + O14: M23:C2 0 0 0 0 0 NaN NaN + O14: M24:C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O1 ≤ 1 0 0 0 0 0 NaN NaN + O1 ≤ 2 0 0 0 0 0 NaN NaN + O1 ≤ 3 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + +--- + + Code + lapply(models0, function(x) coef(summary(x))) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + $m0a$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + + $m0b + $m0b$O2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + + $m1a + $m1a$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + + + $m1b + $m1b$O2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + + + $m2a + $m2a$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m2b + $m2b$O2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m3a + $m3a$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m3b + $m3b$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + + + $m4a + $m4a$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + $m4b + $m4b$O1 + Mean SD 2.5% 97.5% + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 0 + abs(C1 - C2) 0 0 0 0 + log(C1) 0 0 0 0 + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0 + tail-prob. GR-crit + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 NaN + abs(C1 - C2) 0 NaN + log(C1) 0 NaN + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN + MCE/SD + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN + abs(C1 - C2) NaN + log(C1) NaN + ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + + + $m5a + $m5a$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + + $m5b + $m5b$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + C1:C2 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + + $m5c + $m5c$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O12: C1:C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O13: C1:C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + O14: C1:C2 0 0 0 0 0 NaN NaN + + + $m5d + $m5d$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + M22:C2 0 0 0 0 0 NaN NaN + M23:C2 0 0 0 0 0 NaN NaN + M24:C2 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + + $m5e + $m5e$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O12: C1 0 0 0 0 0 NaN NaN + O12: M22 0 0 0 0 0 NaN NaN + O12: M23 0 0 0 0 0 NaN NaN + O12: M24 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O12: O22 0 0 0 0 0 NaN NaN + O12: O23 0 0 0 0 0 NaN NaN + O12: O24 0 0 0 0 0 NaN NaN + O12: M22:C2 0 0 0 0 0 NaN NaN + O12: M23:C2 0 0 0 0 0 NaN NaN + O12: M24:C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: M22 0 0 0 0 0 NaN NaN + O13: M23 0 0 0 0 0 NaN NaN + O13: M24 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O13: O22 0 0 0 0 0 NaN NaN + O13: O23 0 0 0 0 0 NaN NaN + O13: O24 0 0 0 0 0 NaN NaN + O13: M22:C2 0 0 0 0 0 NaN NaN + O13: M23:C2 0 0 0 0 0 NaN NaN + O13: M24:C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: M22 0 0 0 0 0 NaN NaN + O14: M23 0 0 0 0 0 NaN NaN + O14: M24 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + O14: O22 0 0 0 0 0 NaN NaN + O14: O23 0 0 0 0 0 NaN NaN + O14: O24 0 0 0 0 0 NaN NaN + O14: M22:C2 0 0 0 0 0 NaN NaN + O14: M23:C2 0 0 0 0 0 NaN NaN + O14: M24:C2 0 0 0 0 0 NaN NaN + + + $m6a + $m6a$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + + $m6b + $m6b$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + C1:C2 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + + $m6c + $m6c$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O12: C1:C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O13: C1:C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + O14: C1:C2 0 0 0 0 0 NaN NaN + + + $m6d + $m6d$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + M22:C2 0 0 0 0 0 NaN NaN + M23:C2 0 0 0 0 0 NaN NaN + M24:C2 0 0 0 0 0 NaN NaN + O12: C1 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + + + $m6e + $m6e$O1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O12: C1 0 0 0 0 0 NaN NaN + O12: M22 0 0 0 0 0 NaN NaN + O12: M23 0 0 0 0 0 NaN NaN + O12: M24 0 0 0 0 0 NaN NaN + O12: C2 0 0 0 0 0 NaN NaN + O12: O22 0 0 0 0 0 NaN NaN + O12: O23 0 0 0 0 0 NaN NaN + O12: O24 0 0 0 0 0 NaN NaN + O12: M22:C2 0 0 0 0 0 NaN NaN + O12: M23:C2 0 0 0 0 0 NaN NaN + O12: M24:C2 0 0 0 0 0 NaN NaN + O13: C1 0 0 0 0 0 NaN NaN + O13: M22 0 0 0 0 0 NaN NaN + O13: M23 0 0 0 0 0 NaN NaN + O13: M24 0 0 0 0 0 NaN NaN + O13: C2 0 0 0 0 0 NaN NaN + O13: O22 0 0 0 0 0 NaN NaN + O13: O23 0 0 0 0 0 NaN NaN + O13: O24 0 0 0 0 0 NaN NaN + O13: M22:C2 0 0 0 0 0 NaN NaN + O13: M23:C2 0 0 0 0 0 NaN NaN + O13: M24:C2 0 0 0 0 0 NaN NaN + O14: C1 0 0 0 0 0 NaN NaN + O14: M22 0 0 0 0 0 NaN NaN + O14: M23 0 0 0 0 0 NaN NaN + O14: M24 0 0 0 0 0 NaN NaN + O14: C2 0 0 0 0 0 NaN NaN + O14: O22 0 0 0 0 0 NaN NaN + O14: O23 0 0 0 0 0 NaN NaN + O14: O24 0 0 0 0 0 NaN NaN + O14: M22:C2 0 0 0 0 0 NaN NaN + O14: M23:C2 0 0 0 0 0 NaN NaN + O14: M24:C2 0 0 0 0 0 NaN NaN + + + diff --git a/tests/testthat/_snaps/clmm.md b/tests/testthat/_snaps/clmm.md new file mode 100644 index 00000000..389cbd90 --- /dev/null +++ b/tests/testthat/_snaps/clmm.md @@ -0,0 +1,23054 @@ +# data_list remains the same + + Code + lapply(models, "[[", "data_list") + Output + $m0a + $m0a$M_id + (Intercept) + 1 1 + 2 1 + 3 1 + 4 1 + 5 1 + 6 1 + 7 1 + 8 1 + 9 1 + 10 1 + 11 1 + 12 1 + 13 1 + 14 1 + 15 1 + 16 1 + 17 1 + 18 1 + 19 1 + 20 1 + 21 1 + 22 1 + 23 1 + 24 1 + 25 1 + 26 1 + 27 1 + 28 1 + 29 1 + 30 1 + 31 1 + 32 1 + 33 1 + 34 1 + 35 1 + 36 1 + 37 1 + 38 1 + 39 1 + 40 1 + 41 1 + 42 1 + 43 1 + 44 1 + 45 1 + 46 1 + 47 1 + 48 1 + 49 1 + 50 1 + 51 1 + 52 1 + 53 1 + 54 1 + 55 1 + 56 1 + 57 1 + 58 1 + 59 1 + 60 1 + 61 1 + 62 1 + 63 1 + 64 1 + 65 1 + 66 1 + 67 1 + 68 1 + 69 1 + 70 1 + 71 1 + 72 1 + 73 1 + 74 1 + 75 1 + 76 1 + 77 1 + 78 1 + 79 1 + 80 1 + 81 1 + 82 1 + 83 1 + 84 1 + 85 1 + 86 1 + 87 1 + 88 1 + 89 1 + 90 1 + 91 1 + 92 1 + 93 1 + 94 1 + 95 1 + 96 1 + 97 1 + 98 1 + 99 1 + 100 1 + + $m0a$M_lvlone + o1 + 1 2 + 1.1 1 + 1.2 1 + 1.3 2 + 2 3 + 2.1 1 + 2.2 2 + 3 1 + 3.1 3 + 3.2 2 + 4 3 + 4.1 2 + 4.2 1 + 4.3 2 + 5 2 + 5.1 2 + 5.2 3 + 5.3 2 + 6 3 + 7 3 + 7.1 1 + 7.2 1 + 8 2 + 8.1 2 + 8.2 3 + 8.3 3 + 8.4 2 + 8.5 2 + 9 2 + 9.1 2 + 9.2 3 + 10 2 + 10.1 1 + 11 2 + 11.1 3 + 11.2 3 + 11.3 1 + 11.4 3 + 12 1 + 13 2 + 13.1 1 + 14 3 + 14.1 3 + 14.2 2 + 14.3 1 + 15 3 + 15.1 3 + 15.2 2 + 15.3 2 + 16 3 + 16.1 1 + 16.2 3 + 16.3 1 + 16.4 1 + 16.5 2 + 17 2 + 17.1 2 + 17.2 3 + 17.3 2 + 17.4 3 + 18 2 + 19 3 + 19.1 2 + 19.2 2 + 19.3 2 + 20 1 + 20.1 2 + 20.2 2 + 20.3 1 + 20.4 1 + 20.5 2 + 21 2 + 21.1 2 + 21.2 2 + 22 1 + 22.1 1 + 23 1 + 23.1 2 + 24 1 + 25 2 + 25.1 1 + 25.2 1 + 25.3 1 + 25.4 2 + 25.5 2 + 26 2 + 26.1 3 + 26.2 2 + 26.3 1 + 27 2 + 27.1 2 + 28 1 + 28.1 1 + 28.2 1 + 28.3 1 + 29 2 + 29.1 2 + 29.2 1 + 29.3 2 + 30 2 + 30.1 2 + 30.2 2 + 31 2 + 32 3 + 32.1 1 + 32.2 3 + 32.3 3 + 33 2 + 33.1 2 + 34 3 + 34.1 2 + 34.2 2 + 34.3 1 + 35 2 + 35.1 3 + 35.2 2 + 36 3 + 36.1 2 + 36.2 3 + 36.3 3 + 36.4 3 + 37 3 + 37.1 3 + 37.2 3 + 38 2 + 39 1 + 39.1 2 + 39.2 2 + 39.3 1 + 39.4 1 + 39.5 1 + 40 3 + 40.1 1 + 40.2 1 + 40.3 2 + 41 3 + 41.1 2 + 41.2 1 + 41.3 1 + 41.4 3 + 42 2 + 42.1 1 + 43 2 + 43.1 2 + 43.2 1 + 44 3 + 44.1 2 + 44.2 1 + 44.3 1 + 45 1 + 45.1 1 + 46 1 + 46.1 2 + 46.2 3 + 47 1 + 47.1 1 + 47.2 1 + 47.3 3 + 47.4 2 + 48 3 + 48.1 2 + 49 1 + 50 2 + 51 2 + 52 2 + 52.1 3 + 52.2 2 + 52.3 1 + 52.4 3 + 52.5 2 + 53 2 + 53.1 1 + 53.2 1 + 54 1 + 54.1 2 + 54.2 3 + 54.3 1 + 54.4 2 + 55 1 + 55.1 1 + 55.2 2 + 55.3 1 + 55.4 1 + 56 3 + 56.1 2 + 56.2 3 + 56.3 3 + 56.4 1 + 56.5 3 + 57 2 + 57.1 2 + 57.2 2 + 57.3 2 + 58 3 + 58.1 2 + 58.2 3 + 58.3 3 + 58.4 2 + 58.5 3 + 59 3 + 59.1 1 + 60 3 + 61 1 + 61.1 2 + 61.2 2 + 61.3 1 + 61.4 2 + 62 2 + 62.1 3 + 62.2 1 + 62.3 2 + 63 1 + 63.1 3 + 64 2 + 65 1 + 65.1 2 + 65.2 1 + 65.3 1 + 66 1 + 66.1 1 + 66.2 3 + 67 3 + 68 1 + 68.1 2 + 68.2 2 + 68.3 2 + 68.4 2 + 69 2 + 70 1 + 70.1 3 + 71 3 + 71.1 3 + 71.2 3 + 71.3 2 + 71.4 2 + 72 1 + 72.1 1 + 72.2 2 + 72.3 2 + 72.4 1 + 72.5 1 + 73 1 + 74 1 + 75 3 + 76 3 + 76.1 3 + 76.2 1 + 77 2 + 78 2 + 79 1 + 79.1 2 + 79.2 1 + 80 2 + 80.1 2 + 80.2 3 + 81 3 + 81.1 2 + 81.2 3 + 81.3 3 + 82 3 + 82.1 2 + 82.2 2 + 83 3 + 83.1 3 + 83.2 3 + 83.3 2 + 84 3 + 84.1 3 + 85 3 + 85.1 2 + 85.2 2 + 85.3 2 + 85.4 1 + 85.5 3 + 86 2 + 86.1 3 + 86.2 1 + 86.3 1 + 86.4 1 + 86.5 3 + 87 1 + 87.1 1 + 87.2 1 + 88 3 + 88.1 1 + 88.2 1 + 88.3 1 + 89 1 + 90 2 + 90.1 1 + 90.2 3 + 90.3 1 + 91 1 + 91.1 2 + 91.2 1 + 92 2 + 93 1 + 93.1 3 + 93.2 1 + 93.3 3 + 93.4 1 + 94 2 + 94.1 3 + 94.2 1 + 94.3 3 + 94.4 2 + 94.5 3 + 95 3 + 95.1 1 + 95.2 2 + 96 1 + 96.1 3 + 96.2 1 + 96.3 1 + 96.4 2 + 96.5 3 + 97 3 + 97.1 3 + 98 3 + 98.1 1 + 98.2 1 + 99 2 + 99.1 2 + 99.2 1 + 100 1 + 100.1 2 + 100.2 2 + 100.3 1 + 100.4 1 + + $m0a$mu_delta_ordinal + [1] 0 + + $m0a$tau_delta_ordinal + [1] 1e-04 + + $m0a$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m0a$shape_diag_RinvD + [1] "0.01" + + $m0a$rate_diag_RinvD + [1] "0.001" + + + $m0b + $m0b$M_id + (Intercept) + 1 1 + 2 1 + 3 1 + 4 1 + 5 1 + 6 1 + 7 1 + 8 1 + 9 1 + 10 1 + 11 1 + 12 1 + 13 1 + 14 1 + 15 1 + 16 1 + 17 1 + 18 1 + 19 1 + 20 1 + 21 1 + 22 1 + 23 1 + 24 1 + 25 1 + 26 1 + 27 1 + 28 1 + 29 1 + 30 1 + 31 1 + 32 1 + 33 1 + 34 1 + 35 1 + 36 1 + 37 1 + 38 1 + 39 1 + 40 1 + 41 1 + 42 1 + 43 1 + 44 1 + 45 1 + 46 1 + 47 1 + 48 1 + 49 1 + 50 1 + 51 1 + 52 1 + 53 1 + 54 1 + 55 1 + 56 1 + 57 1 + 58 1 + 59 1 + 60 1 + 61 1 + 62 1 + 63 1 + 64 1 + 65 1 + 66 1 + 67 1 + 68 1 + 69 1 + 70 1 + 71 1 + 72 1 + 73 1 + 74 1 + 75 1 + 76 1 + 77 1 + 78 1 + 79 1 + 80 1 + 81 1 + 82 1 + 83 1 + 84 1 + 85 1 + 86 1 + 87 1 + 88 1 + 89 1 + 90 1 + 91 1 + 92 1 + 93 1 + 94 1 + 95 1 + 96 1 + 97 1 + 98 1 + 99 1 + 100 1 + + $m0b$M_lvlone + o2 + 1 1 + 1.1 1 + 1.2 3 + 1.3 1 + 2 4 + 2.1 4 + 2.2 2 + 3 2 + 3.1 4 + 3.2 2 + 4 4 + 4.1 3 + 4.2 NA + 4.3 2 + 5 2 + 5.1 4 + 5.2 2 + 5.3 4 + 6 3 + 7 1 + 7.1 NA + 7.2 4 + 8 1 + 8.1 3 + 8.2 1 + 8.3 4 + 8.4 3 + 8.5 3 + 9 2 + 9.1 2 + 9.2 4 + 10 1 + 10.1 4 + 11 3 + 11.1 1 + 11.2 4 + 11.3 3 + 11.4 3 + 12 3 + 13 NA + 13.1 1 + 14 1 + 14.1 4 + 14.2 3 + 14.3 4 + 15 1 + 15.1 4 + 15.2 NA + 15.3 2 + 16 NA + 16.1 NA + 16.2 1 + 16.3 3 + 16.4 3 + 16.5 1 + 17 3 + 17.1 2 + 17.2 2 + 17.3 3 + 17.4 1 + 18 4 + 19 1 + 19.1 NA + 19.2 NA + 19.3 2 + 20 1 + 20.1 4 + 20.2 3 + 20.3 3 + 20.4 1 + 20.5 3 + 21 3 + 21.1 1 + 21.2 2 + 22 4 + 22.1 NA + 23 4 + 23.1 NA + 24 3 + 25 1 + 25.1 3 + 25.2 2 + 25.3 1 + 25.4 1 + 25.5 NA + 26 3 + 26.1 4 + 26.2 3 + 26.3 1 + 27 4 + 27.1 4 + 28 1 + 28.1 2 + 28.2 3 + 28.3 3 + 29 4 + 29.1 4 + 29.2 3 + 29.3 2 + 30 3 + 30.1 4 + 30.2 4 + 31 2 + 32 NA + 32.1 2 + 32.2 4 + 32.3 3 + 33 4 + 33.1 4 + 34 NA + 34.1 NA + 34.2 NA + 34.3 NA + 35 4 + 35.1 1 + 35.2 NA + 36 1 + 36.1 1 + 36.2 2 + 36.3 2 + 36.4 1 + 37 4 + 37.1 2 + 37.2 2 + 38 NA + 39 NA + 39.1 2 + 39.2 2 + 39.3 3 + 39.4 3 + 39.5 1 + 40 1 + 40.1 2 + 40.2 NA + 40.3 2 + 41 4 + 41.1 3 + 41.2 4 + 41.3 NA + 41.4 4 + 42 4 + 42.1 4 + 43 NA + 43.1 NA + 43.2 3 + 44 2 + 44.1 4 + 44.2 NA + 44.3 1 + 45 3 + 45.1 4 + 46 NA + 46.1 4 + 46.2 4 + 47 3 + 47.1 2 + 47.2 1 + 47.3 2 + 47.4 1 + 48 4 + 48.1 NA + 49 4 + 50 4 + 51 4 + 52 NA + 52.1 NA + 52.2 3 + 52.3 NA + 52.4 4 + 52.5 1 + 53 2 + 53.1 1 + 53.2 3 + 54 3 + 54.1 4 + 54.2 4 + 54.3 3 + 54.4 NA + 55 4 + 55.1 1 + 55.2 4 + 55.3 NA + 55.4 1 + 56 1 + 56.1 2 + 56.2 2 + 56.3 3 + 56.4 4 + 56.5 4 + 57 2 + 57.1 2 + 57.2 4 + 57.3 NA + 58 1 + 58.1 2 + 58.2 2 + 58.3 4 + 58.4 1 + 58.5 NA + 59 4 + 59.1 1 + 60 1 + 61 1 + 61.1 1 + 61.2 NA + 61.3 1 + 61.4 3 + 62 NA + 62.1 NA + 62.2 NA + 62.3 3 + 63 4 + 63.1 3 + 64 4 + 65 2 + 65.1 1 + 65.2 3 + 65.3 NA + 66 1 + 66.1 3 + 66.2 2 + 67 3 + 68 3 + 68.1 4 + 68.2 3 + 68.3 1 + 68.4 4 + 69 4 + 70 4 + 70.1 4 + 71 2 + 71.1 NA + 71.2 4 + 71.3 3 + 71.4 1 + 72 1 + 72.1 NA + 72.2 4 + 72.3 1 + 72.4 3 + 72.5 1 + 73 2 + 74 4 + 75 1 + 76 2 + 76.1 1 + 76.2 1 + 77 3 + 78 3 + 79 NA + 79.1 3 + 79.2 NA + 80 2 + 80.1 3 + 80.2 1 + 81 3 + 81.1 NA + 81.2 3 + 81.3 2 + 82 NA + 82.1 3 + 82.2 1 + 83 4 + 83.1 NA + 83.2 2 + 83.3 NA + 84 2 + 84.1 1 + 85 1 + 85.1 4 + 85.2 3 + 85.3 3 + 85.4 NA + 85.5 2 + 86 1 + 86.1 3 + 86.2 1 + 86.3 2 + 86.4 3 + 86.5 4 + 87 NA + 87.1 3 + 87.2 3 + 88 NA + 88.1 1 + 88.2 2 + 88.3 NA + 89 3 + 90 2 + 90.1 2 + 90.2 2 + 90.3 4 + 91 2 + 91.1 NA + 91.2 3 + 92 2 + 93 3 + 93.1 2 + 93.2 3 + 93.3 2 + 93.4 4 + 94 NA + 94.1 2 + 94.2 NA + 94.3 3 + 94.4 4 + 94.5 3 + 95 NA + 95.1 2 + 95.2 3 + 96 3 + 96.1 NA + 96.2 4 + 96.3 3 + 96.4 NA + 96.5 1 + 97 2 + 97.1 1 + 98 2 + 98.1 1 + 98.2 3 + 99 NA + 99.1 NA + 99.2 4 + 100 1 + 100.1 NA + 100.2 1 + 100.3 4 + 100.4 1 + + $m0b$mu_delta_ordinal + [1] 0 + + $m0b$tau_delta_ordinal + [1] 1e-04 + + $m0b$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m0b$shape_diag_RinvD + [1] "0.01" + + $m0b$rate_diag_RinvD + [1] "0.001" + + + $m1a + $m1a$M_id + (Intercept) C1 + 1 1 0.7175865 + 2 1 0.7507170 + 3 1 0.7255954 + 4 1 0.7469352 + 5 1 0.7139120 + 6 1 0.7332505 + 7 1 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NA NA NA NA + 93.4 1 4 NA NA NA NA NA NA + 94 2 NA NA NA NA NA NA NA + 94.1 3 2 NA NA NA NA NA NA + 94.2 1 NA NA NA NA NA NA NA + 94.3 3 3 NA NA NA NA NA NA + 94.4 2 4 NA NA NA NA NA NA + 94.5 3 3 NA NA NA NA NA NA + 95 3 NA NA NA NA NA NA NA + 95.1 1 2 NA NA NA NA NA NA + 95.2 2 3 NA NA NA NA NA NA + 96 1 3 NA NA NA NA NA NA + 96.1 3 NA NA NA NA NA NA NA + 96.2 1 4 NA NA NA NA NA NA + 96.3 1 3 NA NA NA NA NA NA + 96.4 2 NA NA NA NA NA NA NA + 96.5 3 1 NA NA NA NA NA NA + 97 3 2 NA NA NA NA NA NA + 97.1 3 1 NA NA NA NA NA NA + 98 3 2 NA NA NA NA NA NA + 98.1 1 1 NA NA NA NA NA NA + 98.2 1 3 NA NA NA NA NA NA + 99 2 NA NA NA NA NA NA NA + 99.1 2 NA NA NA NA NA NA NA + 99.2 1 4 NA NA NA NA NA NA + 100 1 1 NA NA NA NA NA NA + 100.1 2 NA NA NA NA NA NA NA + 100.2 2 1 NA NA NA NA NA NA + 100.3 1 4 NA NA NA NA NA NA + 100.4 1 1 NA NA NA NA NA NA + + $m4a$spM_id + center scale + M2 NA NA + C2 -0.6240921 0.68571078 + (Intercept) NA NA + M22 NA NA + M23 NA NA + M24 NA NA + abs(C1 - C2) 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33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m4a$shape_diag_RinvD + [1] "0.01" + + $m4a$rate_diag_RinvD + [1] "0.001" + + + $m4b + $m4b$M_id + C2 (Intercept) abs(C1 - C2) log(C1) M12 M13 M14 C1 M1 + 1 -1.381594459 1 NA -0.3318617 0 0 0 0.7175865 1 + 2 0.344426024 1 NA -0.2867266 0 0 1 0.7507170 4 + 3 NA 1 NA -0.3207627 0 0 0 0.7255954 1 + 4 -0.228910007 1 NA -0.2917769 0 0 0 0.7469352 1 + 5 NA 1 NA -0.3369956 0 0 1 0.7139120 4 + 6 -2.143955482 1 NA -0.3102679 0 0 1 0.7332505 4 + 7 -1.156567023 1 NA -0.3084388 0 0 1 0.7345929 4 + 8 -0.598827660 1 NA -0.2675411 0 0 1 0.7652589 4 + 9 NA 1 NA -0.3284176 1 0 0 0.7200622 2 + 10 -1.006719032 1 NA -0.2978834 0 0 0 0.7423879 1 + 11 0.239801450 1 NA -0.2960573 0 1 0 0.7437448 3 + 12 -1.064969789 1 NA -0.2948450 0 0 0 0.7446470 1 + 13 -0.538082688 1 NA -0.2836654 0 1 0 0.7530186 3 + 14 NA 1 NA -0.3434574 0 1 0 0.7093137 3 + 15 -1.781049276 1 NA -0.3104469 0 0 1 0.7331192 4 + 16 NA 1 NA -0.3550492 0 0 0 0.7011390 1 + 17 NA 1 NA -0.2967369 1 0 0 0.7432395 2 + 18 -0.014579883 1 NA -0.2816747 0 1 0 0.7545191 3 + 19 -2.121550136 1 NA -0.2838910 0 0 0 0.7528487 1 + 20 NA 1 NA -0.2727455 1 0 0 0.7612865 2 + 21 -0.363239698 1 NA -0.3213465 1 0 0 0.7251719 2 + 22 -0.121568514 1 NA -0.3146245 0 0 1 0.7300630 4 + 23 -0.951271111 1 NA -0.3442879 0 0 1 0.7087249 4 + 24 NA 1 NA -0.3021952 0 0 0 0.7391938 1 + 25 -0.974288621 1 NA -0.2458186 0 0 0 0.7820641 1 + 26 -1.130632418 1 NA -0.3399165 0 1 0 0.7118298 3 + 27 0.114339868 1 NA -0.3242275 0 0 0 0.7230857 1 + 28 0.238334648 1 NA -0.2891027 0 0 1 0.7489353 4 + 29 0.840744958 1 NA -0.2862314 0 0 0 0.7510888 1 + 30 NA 1 NA -0.3146125 0 1 0 0.7300717 3 + 31 NA 1 NA -0.2809421 0 1 0 0.7550721 3 + 32 -1.466312154 1 NA -0.3117155 0 0 0 0.7321898 1 + 33 -0.637352277 1 NA -0.3138326 0 1 0 0.7306414 3 + 34 NA 1 NA -0.2974340 0 0 1 0.7427216 4 + 35 NA 1 NA -0.3294709 0 0 1 0.7193042 4 + 36 NA 1 NA -0.3129468 0 0 0 0.7312888 1 + 37 NA 1 NA -0.3424289 1 0 0 0.7100436 2 + 38 NA 1 NA -0.2652444 0 0 1 0.7670184 4 + 39 0.006728205 1 NA -0.3010445 0 1 0 0.7400449 3 + 40 NA 1 NA -0.3014695 1 0 0 0.7397304 2 + 41 -1.663281353 1 NA -0.2888874 1 0 0 0.7490966 2 + 42 0.161184794 1 NA -0.2985038 0 0 0 0.7419274 1 + 43 0.457939180 1 NA -0.2839809 0 0 0 0.7527810 1 + 44 -0.307070331 1 NA -0.2999821 0 1 0 0.7408315 3 + 45 NA 1 NA -0.3082181 1 0 0 0.7347550 2 + 46 -1.071668276 1 NA -0.3102825 1 0 0 0.7332398 2 + 47 -0.814751321 1 NA -0.3042884 0 0 0 0.7376481 1 + 48 -0.547630662 1 NA -0.3084048 0 0 0 0.7346179 1 + 49 NA 1 NA -0.3106911 0 0 0 0.7329402 1 + 50 -1.350213782 1 NA -0.3201451 1 0 0 0.7260436 2 + 51 0.719054706 1 NA -0.3225621 0 0 0 0.7242910 1 + 52 NA 1 NA -0.3149755 0 0 1 0.7298067 4 + 53 -1.207130750 1 NA -0.3209299 0 0 0 0.7254741 1 + 54 NA 1 NA -0.2820889 1 0 0 0.7542067 2 + 55 -0.408600991 1 NA -0.3024638 0 1 0 0.7389952 3 + 56 -0.271380529 1 NA -0.2849341 0 1 0 0.7520638 3 + 57 -1.361925974 1 NA -0.3257359 0 0 1 0.7219958 4 + 58 NA 1 NA -0.3202560 1 0 0 0.7259632 2 + 59 NA 1 NA -0.2932166 0 0 1 0.7458606 4 + 60 -0.323712205 1 NA -0.2649529 0 0 0 0.7672421 1 + 61 NA 1 NA -0.3205938 0 0 0 0.7257179 1 + 62 NA 1 NA -0.3299089 0 0 1 0.7189892 4 + 63 -1.386906880 1 NA -0.3101519 0 0 1 0.7333356 4 + 64 NA 1 NA -0.3119416 0 0 1 0.7320243 4 + 65 NA 1 NA -0.2906584 1 0 0 0.7477711 2 + 66 -0.565191691 1 NA -0.3087049 0 1 0 0.7343974 3 + 67 -0.382899912 1 NA -0.2887994 1 0 0 0.7491624 2 + 68 NA 1 NA -0.2899866 0 0 1 0.7482736 4 + 69 -0.405642769 1 NA -0.3094824 0 0 0 0.7338267 1 + 70 NA 1 NA -0.2734187 0 0 1 0.7607742 4 + 71 -0.843748427 1 NA -0.2513372 0 0 1 0.7777600 4 + 72 0.116003683 1 NA -0.3000053 0 0 1 0.7408143 4 + 73 -0.778634325 1 NA -0.3218221 0 0 0 0.7248271 1 + 74 NA 1 NA -0.3058575 0 1 0 0.7364916 3 + 75 NA 1 NA -0.2923695 0 0 1 0.7464926 4 + 76 NA 1 NA -0.3071463 1 0 0 0.7355430 2 + 77 -0.632974758 1 NA -0.3273313 1 0 0 0.7208449 2 + 78 NA 1 NA -0.3046827 0 0 0 0.7373573 1 + 79 -0.778064615 1 NA -0.2746896 1 0 0 0.7598079 2 + 80 NA 1 NA -0.3064688 0 1 0 0.7360415 3 + 81 NA 1 NA -0.3155423 0 0 0 0.7293932 1 + 82 -0.246123253 1 NA -0.3175491 0 0 0 0.7279309 1 + 83 -1.239659782 1 NA -0.3086139 1 0 0 0.7344643 2 + 84 -0.467772280 1 NA -0.3032222 0 0 1 0.7384350 4 + 85 NA 1 NA -0.3114673 0 1 0 0.7323716 3 + 86 -2.160485036 1 NA -0.2775210 1 0 0 0.7576597 2 + 87 -0.657675572 1 NA -0.2881970 0 0 1 0.7496139 4 + 88 NA 1 NA -0.3181084 0 1 0 0.7275239 3 + 89 -0.696710744 1 NA -0.3214942 0 0 1 0.7250648 4 + 90 NA 1 NA -0.3098919 1 0 0 0.7335262 2 + 91 -0.179395847 1 NA -0.3087042 0 0 1 0.7343980 4 + 92 -0.441545568 1 NA -0.3037539 1 0 0 0.7380425 2 + 93 -0.685799334 1 NA -0.3025305 0 0 1 0.7389460 4 + 94 NA 1 NA -0.3202120 0 0 1 0.7259951 4 + 95 0.191929445 1 NA -0.3170642 0 0 0 0.7282840 1 + 96 NA 1 NA -0.3172240 1 0 0 0.7281676 2 + 97 -0.069760671 1 NA -0.3221849 0 0 0 0.7245642 1 + 98 NA 1 NA -0.2840967 0 0 1 0.7526938 4 + 99 NA 1 NA -0.3185112 1 0 0 0.7272309 2 + 100 NA 1 NA -0.3033427 1 0 0 0.7383460 2 + + $m4b$M_lvlone + o1 o2 ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) + 1 2 1 NA + 1.1 1 1 NA + 1.2 1 3 NA + 1.3 2 1 NA + 2 3 4 NA + 2.1 1 4 NA + 2.2 2 2 NA + 3 1 2 NA + 3.1 3 4 NA + 3.2 2 2 NA + 4 3 4 NA + 4.1 2 3 NA + 4.2 1 NA NA + 4.3 2 2 NA + 5 2 2 NA + 5.1 2 4 NA + 5.2 3 2 NA + 5.3 2 4 NA + 6 3 3 NA + 7 3 1 NA + 7.1 1 NA NA + 7.2 1 4 NA + 8 2 1 NA + 8.1 2 3 NA + 8.2 3 1 NA + 8.3 3 4 NA + 8.4 2 3 NA + 8.5 2 3 NA + 9 2 2 NA + 9.1 2 2 NA + 9.2 3 4 NA + 10 2 1 NA + 10.1 1 4 NA + 11 2 3 NA + 11.1 3 1 NA + 11.2 3 4 NA + 11.3 1 3 NA + 11.4 3 3 NA + 12 1 3 NA + 13 2 NA NA + 13.1 1 1 NA + 14 3 1 NA + 14.1 3 4 NA + 14.2 2 3 NA + 14.3 1 4 NA + 15 3 1 NA + 15.1 3 4 NA + 15.2 2 NA NA + 15.3 2 2 NA + 16 3 NA NA + 16.1 1 NA NA + 16.2 3 1 NA + 16.3 1 3 NA + 16.4 1 3 NA + 16.5 2 1 NA + 17 2 3 NA + 17.1 2 2 NA + 17.2 3 2 NA + 17.3 2 3 NA + 17.4 3 1 NA + 18 2 4 NA + 19 3 1 NA + 19.1 2 NA NA + 19.2 2 NA NA + 19.3 2 2 NA + 20 1 1 NA + 20.1 2 4 NA + 20.2 2 3 NA + 20.3 1 3 NA + 20.4 1 1 NA + 20.5 2 3 NA + 21 2 3 NA + 21.1 2 1 NA + 21.2 2 2 NA + 22 1 4 NA + 22.1 1 NA NA + 23 1 4 NA + 23.1 2 NA NA + 24 1 3 NA + 25 2 1 NA + 25.1 1 3 NA + 25.2 1 2 NA + 25.3 1 1 NA + 25.4 2 1 NA + 25.5 2 NA NA + 26 2 3 NA + 26.1 3 4 NA + 26.2 2 3 NA + 26.3 1 1 NA + 27 2 4 NA + 27.1 2 4 NA + 28 1 1 NA + 28.1 1 2 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93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m4c$shape_diag_RinvD + [1] "0.01" + + $m4c$rate_diag_RinvD + [1] "0.001" + + $m4c$RinvD_o1_id + [,1] [,2] [,3] [,4] + [1,] NA 0 0 0 + [2,] 0 NA 0 0 + [3,] 0 0 NA 0 + [4,] 0 0 0 NA + + $m4c$KinvD_o1_id + id + 5 + + + $m4d + $m4d$M_id + (Intercept) C1 + 1 1 0.7175865 + 2 1 0.7507170 + 3 1 0.7255954 + 4 1 0.7469352 + 5 1 0.7139120 + 6 1 0.7332505 + 7 1 0.7345929 + 8 1 0.7652589 + 9 1 0.7200622 + 10 1 0.7423879 + 11 1 0.7437448 + 12 1 0.7446470 + 13 1 0.7530186 + 14 1 0.7093137 + 15 1 0.7331192 + 16 1 0.7011390 + 17 1 0.7432395 + 18 1 0.7545191 + 19 1 0.7528487 + 20 1 0.7612865 + 21 1 0.7251719 + 22 1 0.7300630 + 23 1 0.7087249 + 24 1 0.7391938 + 25 1 0.7820641 + 26 1 0.7118298 + 27 1 0.7230857 + 28 1 0.7489353 + 29 1 0.7510888 + 30 1 0.7300717 + 31 1 0.7550721 + 32 1 0.7321898 + 33 1 0.7306414 + 34 1 0.7427216 + 35 1 0.7193042 + 36 1 0.7312888 + 37 1 0.7100436 + 38 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0.4182528210 2.4403039888 NA + 82 3 NA 1.3791010005 1.901920e+00 NA 1.1088801952 1.0038902706 NA + 82.1 2 0 1.7601010622 3.097956e+00 NA 0.9334157763 1.2812319990 NA + 82.2 2 1 2.6233131927 6.881772e+00 NA 0.4958140634 1.9095908060 NA + 83 3 NA 0.0537394290 2.887926e-03 NA 0.5104724530 0.0394696928 NA + 83.1 3 0 2.9061570496 8.445749e+00 NA -0.0513309106 2.1344686414 NA + 83.2 3 0 3.1189457362 9.727823e+00 NA -0.2067792494 2.2907543380 NA + 83.3 2 NA 4.7663642222 2.271823e+01 NA -0.0534169155 3.5007244248 NA + 84 3 0 2.7254060237 7.427838e+00 NA -0.0255753653 2.0125352642 NA + 84.1 3 NA 3.3364784659 1.113209e+01 NA -1.8234189877 2.4637725582 NA + 85 3 1 0.2977756259 8.867032e-02 NA -0.0114038622 0.2180824084 NA + 85.1 2 NA 1.7394116637 3.025553e+00 NA -0.0577615939 1.2738956847 NA + 85.2 2 0 2.6846330194 7.207254e+00 NA -0.2241856342 1.9661489512 NA + 85.3 2 0 3.1608762743 9.991139e+00 NA -0.0520175929 2.3149359808 NA + 85.4 1 0 3.9452053758 1.556465e+01 NA 0.2892733846 2.8893563314 NA 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1.1955102731 1.429245e+00 NA 1.3775130083 0.8662239621 NA + 97.1 3 0 4.9603108643 2.460468e+01 NA -1.7323228619 3.5940637456 NA + 98 3 0 0.2041732438 4.168671e-02 NA -1.2648518889 0.1536799385 NA + 98.1 1 0 0.4309578973 1.857247e-01 NA -0.9042716241 0.3243793452 NA + 98.2 1 1 3.5172611906 1.237113e+01 NA -0.1560385207 2.6474207553 NA + 99 2 0 0.3531786101 1.247351e-01 NA 0.7993356425 0.2568424071 NA + 99.1 2 0 4.6789444226 2.189252e+01 NA 1.0355522332 3.4026730772 NA + 99.2 1 0 4.9927084171 2.492714e+01 NA -0.1150895843 3.6308519569 NA + 100 1 NA 1.0691387602 1.143058e+00 NA 0.0369067906 0.7893943379 NA + 100.1 2 NA 1.5109344281 2.282923e+00 NA 1.6023713093 1.1155924065 NA + 100.2 2 0 2.1502332564 4.623503e+00 NA 0.8861545820 1.5876161457 NA + 100.3 1 NA 3.8745574222 1.501220e+01 NA 0.1277046316 2.8607640137 NA + 100.4 1 0 4.6567608765 2.168542e+01 NA -0.0834577654 3.4383008132 NA + + $m4d$spM_id + center scale + (Intercept) NA NA + C1 0.7372814 0.01472882 + + $m4d$spM_lvlone + center scale + o1 NA NA + b2 NA NA + time 2.53394028 1.3818094 + I(time^2) 8.32444679 7.0900029 + b21 NA NA + c1 0.25599956 0.6718095 + C1:time 1.86876118 1.0180574 + b21:c1 0.04082297 0.2677776 + + $m4d$mu_reg_binom + [1] 0 + + $m4d$tau_reg_binom + [1] 1e-04 + + $m4d$mu_reg_ordinal + [1] 0 + + $m4d$tau_reg_ordinal + [1] 1e-04 + + $m4d$mu_delta_ordinal + [1] 0 + + $m4d$tau_delta_ordinal + [1] 1e-04 + + $m4d$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m4d$shape_diag_RinvD + [1] "0.01" + + $m4d$rate_diag_RinvD + [1] "0.001" + + $m4d$RinvD_o1_id + [,1] [,2] + [1,] NA 0 + [2,] 0 NA + + $m4d$KinvD_o1_id + id + 3 + + + $m4e + $m4e$M_id + (Intercept) C1 + 1 1 0.7175865 + 2 1 0.7507170 + 3 1 0.7255954 + 4 1 0.7469352 + 5 1 0.7139120 + 6 1 0.7332505 + 7 1 0.7345929 + 8 1 0.7652589 + 9 1 0.7200622 + 10 1 0.7423879 + 11 1 0.7437448 + 12 1 0.7446470 + 13 1 0.7530186 + 14 1 0.7093137 + 15 1 0.7331192 + 16 1 0.7011390 + 17 1 0.7432395 + 18 1 0.7545191 + 19 1 0.7528487 + 20 1 0.7612865 + 21 1 0.7251719 + 22 1 0.7300630 + 23 1 0.7087249 + 24 1 0.7391938 + 25 1 0.7820641 + 26 1 0.7118298 + 27 1 0.7230857 + 28 1 0.7489353 + 29 1 0.7510888 + 30 1 0.7300717 + 31 1 0.7550721 + 32 1 0.7321898 + 33 1 0.7306414 + 34 1 0.7427216 + 35 1 0.7193042 + 36 1 0.7312888 + 37 1 0.7100436 + 38 1 0.7670184 + 39 1 0.7400449 + 40 1 0.7397304 + 41 1 0.7490966 + 42 1 0.7419274 + 43 1 0.7527810 + 44 1 0.7408315 + 45 1 0.7347550 + 46 1 0.7332398 + 47 1 0.7376481 + 48 1 0.7346179 + 49 1 0.7329402 + 50 1 0.7260436 + 51 1 0.7242910 + 52 1 0.7298067 + 53 1 0.7254741 + 54 1 0.7542067 + 55 1 0.7389952 + 56 1 0.7520638 + 57 1 0.7219958 + 58 1 0.7259632 + 59 1 0.7458606 + 60 1 0.7672421 + 61 1 0.7257179 + 62 1 0.7189892 + 63 1 0.7333356 + 64 1 0.7320243 + 65 1 0.7477711 + 66 1 0.7343974 + 67 1 0.7491624 + 68 1 0.7482736 + 69 1 0.7338267 + 70 1 0.7607742 + 71 1 0.7777600 + 72 1 0.7408143 + 73 1 0.7248271 + 74 1 0.7364916 + 75 1 0.7464926 + 76 1 0.7355430 + 77 1 0.7208449 + 78 1 0.7373573 + 79 1 0.7598079 + 80 1 0.7360415 + 81 1 0.7293932 + 82 1 0.7279309 + 83 1 0.7344643 + 84 1 0.7384350 + 85 1 0.7323716 + 86 1 0.7576597 + 87 1 0.7496139 + 88 1 0.7275239 + 89 1 0.7250648 + 90 1 0.7335262 + 91 1 0.7343980 + 92 1 0.7380425 + 93 1 0.7389460 + 94 1 0.7259951 + 95 1 0.7282840 + 96 1 0.7281676 + 97 1 0.7245642 + 98 1 0.7526938 + 99 1 0.7272309 + 100 1 0.7383460 + + $m4e$M_lvlone + o1 log(time) I(time^2) p1 time + 1 2 -0.67522439 2.591239e-01 5 0.5090421822 + 1.1 1 -0.40555367 4.443657e-01 3 0.6666076288 + 1.2 1 0.75635394 4.539005e+00 8 2.1304941282 + 1.3 2 0.91446673 6.227241e+00 6 2.4954441458 + 2 3 1.10409692 9.099267e+00 5 3.0164990982 + 2.1 1 1.19382570 1.088789e+01 3 3.2996806887 + 2.2 2 1.42905614 1.742860e+01 2 4.1747569619 + 3 1 -0.16502467 7.188883e-01 7 0.8478727890 + 3.1 3 1.12018813 9.396866e+00 2 3.0654308549 + 3.2 2 1.55564789 2.245012e+01 8 4.7381553578 + 4 3 -1.08724748 1.136655e-01 2 0.3371432109 + 4.1 2 0.06700602 1.143407e+00 4 1.0693019140 + 4.2 1 0.96122482 6.837688e+00 2 2.6148973033 + 4.3 2 1.14219951 9.819783e+00 6 3.1336532847 + 5 2 0.07348511 1.158319e+00 6 1.0762525082 + 5.1 2 0.58291628 3.208593e+00 2 1.7912546196 + 5.2 3 1.02819270 7.817661e+00 3 2.7960080339 + 5.3 2 1.03389386 7.907311e+00 2 2.8119940578 + 6 3 0.57748169 3.173907e+00 4 1.7815462884 + 7 3 1.19616503 1.093895e+01 2 3.3074087673 + 7.1 1 1.30855992 1.369622e+01 6 3.7008403614 + 7.2 1 1.56269618 2.276883e+01 4 4.7716691741 + 8 2 0.11746285 1.264815e+00 2 1.1246398522 + 8.1 2 0.58928609 3.249731e+00 2 1.8027009873 + 8.2 3 0.59750733 3.303606e+00 1 1.8175825174 + 8.3 3 1.04324992 8.056666e+00 2 2.8384267003 + 8.4 2 1.21284162 1.130995e+01 2 3.3630275307 + 8.5 2 1.48977222 1.967885e+01 4 4.4360849704 + 9 2 -0.04000943 9.230989e-01 3 0.9607803822 + 9.1 2 1.07082146 8.513413e+00 3 2.9177753383 + 9.2 3 1.57071564 2.313696e+01 2 4.8100892501 + 10 2 0.83184373 5.278740e+00 4 2.2975509102 + 10.1 1 1.42873389 1.741737e+01 5 4.1734118364 + 11 2 0.16827866 1.400119e+00 2 1.1832662905 + 11.1 3 0.21075122 1.524250e+00 4 1.2346051680 + 11.2 3 0.49684736 2.701196e+00 6 1.6435316263 + 11.3 1 1.21962028 1.146433e+01 2 3.3859017969 + 11.4 3 1.57107306 2.315350e+01 1 4.8118087661 + 12 1 -0.04165702 9.200622e-01 5 0.9591987054 + 13 2 -2.78209660 3.832672e-03 2 0.0619085738 + 13.1 1 1.27035199 1.268860e+01 6 3.5621061502 + 14 3 1.39536386 1.629287e+01 3 4.0364430007 + 14.1 3 1.49762465 1.999034e+01 2 4.4710561272 + 14.2 2 1.53383464 2.149175e+01 4 4.6359198843 + 14.3 1 1.54513729 2.198311e+01 2 4.6886152599 + 15 3 -0.61580408 2.918229e-01 4 0.5402063532 + 15.1 3 0.17338010 1.414477e+00 7 1.1893180816 + 15.2 2 0.41176123 2.278512e+00 4 1.5094739688 + 15.3 2 1.59317589 2.419998e+01 3 4.9193474615 + 16 3 0.21655500 1.542046e+00 3 1.2417913869 + 16.1 1 0.94296095 6.592429e+00 2 2.5675726333 + 16.2 3 0.97546872 7.035280e+00 5 2.6524101500 + 16.3 1 1.26933963 1.266294e+01 3 3.5585018690 + 16.4 1 1.32475013 1.414697e+01 2 3.7612454291 + 16.5 2 1.38257779 1.588151e+01 6 3.9851612889 + 17 2 0.46532748 2.536170e+00 3 1.5925356350 + 17.1 2 0.89093325 5.940935e+00 1 2.4374032998 + 17.2 3 1.10712558 9.154551e+00 4 3.0256489082 + 17.3 2 1.20384548 1.110828e+01 5 3.3329089405 + 17.4 3 1.35309323 1.497207e+01 5 3.8693758985 + 18 2 0.89094389 5.941061e+00 8 2.4374292302 + 19 3 -0.02304702 9.549522e-01 5 0.9772165376 + 19.1 2 0.13683034 1.314769e+00 6 1.1466335913 + 19.2 2 0.81532616 5.107205e+00 4 2.2599126538 + 19.3 2 1.43780097 1.773610e+01 3 4.2114245973 + 20 1 0.54058790 2.948144e+00 5 1.7170160066 + 20.1 2 0.56320376 3.084555e+00 8 1.7562902288 + 20.2 2 0.81162182 5.069507e+00 3 2.2515566566 + 20.3 1 0.81576844 5.111725e+00 3 2.2609123867 + 20.4 1 1.25028462 1.218943e+01 3 3.4913365287 + 20.5 2 1.42865863 1.741475e+01 3 4.1730977828 + 21 2 0.52689085 2.868478e+00 3 1.6936582839 + 21.1 2 1.08421553 8.744554e+00 3 2.9571191233 + 21.2 2 1.33203313 1.435454e+01 4 3.7887385779 + 22 1 0.90406535 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-7.850462e-17 -0.8164966 NA + 85.3 -6.3296340 0 NA 3 NA NA NA -7.850462e-17 -0.8164966 NA + 85.4 -7.0405525 0 -0.30730810 NA NA NA NA -7.071068e-01 0.4082483 NA + 85.5 -13.6714939 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA + 86 -10.8756412 0 -0.10854862 1 NA NA NA -7.850462e-17 -0.8164966 NA + 86.1 -12.0055331 NA -0.25751662 3 NA NA NA 7.071068e-01 0.4082483 NA + 86.2 -13.3724699 NA -0.38943076 1 NA NA NA -7.071068e-01 0.4082483 NA + 86.3 -13.3252145 0 -0.24454702 2 NA NA NA -7.071068e-01 0.4082483 NA + 86.4 -14.9191290 NA -0.12338992 3 NA NA NA -7.071068e-01 0.4082483 NA + 86.5 -17.7515546 0 -0.23976984 4 NA NA NA 7.071068e-01 0.4082483 NA + 87 -10.7027963 NA NA NA NA NA NA -7.071068e-01 0.4082483 NA + 87.1 -22.4941954 NA -0.34366972 3 NA NA NA -7.071068e-01 0.4082483 NA + 87.2 -14.9616716 NA NA 3 NA NA NA -7.071068e-01 0.4082483 NA + 88 -2.2264493 0 -0.31563888 NA NA NA NA 7.071068e-01 0.4082483 NA + 88.1 -8.9626474 NA -0.20304028 1 NA NA NA -7.071068e-01 0.4082483 NA + 88.2 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0.4082483 NA + 93.4 -11.1877696 0 -0.23618836 4 NA NA NA -7.071068e-01 0.4082483 NA + 94 -6.9602492 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA + 94.1 -7.4318416 0 -0.10217284 2 NA NA NA 7.071068e-01 0.4082483 NA + 94.2 -4.3498045 0 -0.36713471 NA NA NA NA -7.071068e-01 0.4082483 NA + 94.3 -11.6340088 NA -0.13806763 3 NA NA NA 7.071068e-01 0.4082483 NA + 94.4 -12.9357964 0 -0.42353804 4 NA NA NA -7.850462e-17 -0.8164966 NA + 94.5 -14.7648530 1 -0.15513707 3 NA NA NA 7.071068e-01 0.4082483 NA + 95 -12.8849309 0 -0.24149687 NA NA NA NA 7.071068e-01 0.4082483 NA + 95.1 -9.7451502 NA -0.21315958 2 NA NA NA -7.071068e-01 0.4082483 NA + 95.2 -0.8535063 0 -0.15777208 3 NA NA NA -7.850462e-17 -0.8164966 NA + 96 -4.9139832 0 -0.16780948 3 NA NA NA -7.071068e-01 0.4082483 NA + 96.1 -3.9582653 0 -0.32504815 NA NA NA NA 7.071068e-01 0.4082483 NA + 96.2 -9.6555492 0 -0.20395970 4 NA NA NA -7.071068e-01 0.4082483 NA + 96.3 -11.8690793 NA -0.06221501 3 NA NA NA -7.071068e-01 0.4082483 NA + 96.4 -11.0224373 1 -0.14801097 NA NA NA NA -7.850462e-17 -0.8164966 NA + 96.5 -10.9530403 1 -0.28658893 1 NA NA NA 7.071068e-01 0.4082483 NA + 97 -9.8540471 0 -0.34484656 2 NA NA NA 7.071068e-01 0.4082483 NA + 97.1 -19.2262840 0 -0.35658805 1 NA NA NA 7.071068e-01 0.4082483 NA + 98 -11.9651231 0 -0.36913003 2 NA NA NA 7.071068e-01 0.4082483 NA + 98.1 -2.6515128 0 NA 1 NA NA NA -7.071068e-01 0.4082483 NA + 98.2 -12.2606382 1 -0.17154225 3 NA NA NA -7.071068e-01 0.4082483 NA + 99 -11.4720500 0 -0.24753132 NA NA NA NA -7.850462e-17 -0.8164966 NA + 99.1 -14.0596866 0 -0.27947829 NA NA NA NA -7.850462e-17 -0.8164966 NA + 99.2 -17.3939469 0 -0.09033035 4 NA NA NA -7.071068e-01 0.4082483 NA + 100 1.1005874 NA -0.17326698 1 NA NA NA -7.071068e-01 0.4082483 NA + 100.1 -3.8226248 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA + 100.2 -0.9123182 0 -0.12072016 1 NA NA NA -7.850462e-17 -0.8164966 NA + 100.3 -15.8389474 NA -0.27657520 4 NA NA NA -7.071068e-01 0.4082483 NA + 100.4 -12.8093826 0 -0.14631556 1 NA NA NA -7.071068e-01 0.4082483 NA + + $m7b$spM_lvlone + center scale + y -11.1733710 6.2496619 + b2 NA NA + c2 -0.2237158 0.1059527 + o2 NA NA + o22 NA NA + o23 NA NA + o24 NA NA + o1.L NA NA + o1.Q NA NA + b21 NA NA + + $m7b$mu_reg_norm + [1] 0 + + $m7b$tau_reg_norm + [1] 1e-04 + + $m7b$shape_tau_norm + [1] 0.01 + + $m7b$rate_tau_norm + [1] 0.01 + + $m7b$mu_reg_binom + [1] 0 + + $m7b$tau_reg_binom + [1] 1e-04 + + $m7b$mu_delta_ordinal + [1] 0 + + $m7b$tau_delta_ordinal + [1] 1e-04 + + $m7b$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m7b$shape_diag_RinvD + [1] "0.01" + + $m7b$rate_diag_RinvD + [1] "0.001" + + + +# jagsmodel remains the same + + Code + lapply(models, "[[", "jagsmodel") + Output + $m0a + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + } + $m0b + model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } + $m1a + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] + } + + + + # Priors for the model for o1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + } + $m1b + model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] + } + + + + # Priors for the model for o2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } + $m1c + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + } + $m1d + model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } + $m2a + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + + + # Priors for the model for o1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2b + model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + + + # Priors for the model for o2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2c + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m2d + model { + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m3a + model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 2] + + beta[3] * M_lvlone[i, 3] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + } + $m3b + model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 3] + + beta[3] * M_lvlone[i, 4] + beta[4] * M_lvlone[i, 5] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } + $m4a + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + beta[6] * M_lvlone[i, 3] + + beta[7] * M_lvlone[i, 4] + beta[8] * M_lvlone[i, 5] + + beta[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[10] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + + beta[11] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 4] * beta[1] + M_id[ii, 5] * beta[2] + + M_id[ii, 6] * beta[3] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * beta[4] + + (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + } + + + + # Priors for the model for o2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 3] * alpha[6] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[7] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[8] + log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[9] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[10] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[11] + log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[12] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[13] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 6:14) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 3] * alpha[15] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[16] + + M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2]) + + + } + + # Priors for the model for C2 + for (k in 15:16) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 6] <- M_lvlone[i, 3] * M_id[group_id[i], 7] + M_lvlone[i, 7] <- M_lvlone[i, 4] * M_id[group_id[i], 7] + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_id[group_id[i], 7] + } + + } + $m4b + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[2] + } + + + + # Priors for the model for o1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + + M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0) + + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- M_id[ii, 5] * alpha[1] + M_id[ii, 6] * alpha[2] + + M_id[ii, 7] * alpha[3] + + (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[4] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[5] + } + + + + # Priors for the model for o2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[6] + M_id[ii, 5] * alpha[7] + M_id[ii, 6] * alpha[8] + + M_id[ii, 7] * alpha[9] + + (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[10] + + M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1]) + + + } + + # Priors for the model for C2 + for (k in 6:10) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3] + } + + } + $m4c + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_o1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_o1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:4] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] + + M_id[ii, 4] * beta[2] + mu_b_o1_id[ii, 2] <- beta[4] + mu_b_o1_id[ii, 3] <- beta[3] + mu_b_o1_id[ii, 4] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + for (k in 1:4) { + RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_o1_id[1:4, 1:4] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id) + D_o1_id[1:4, 1:4] <- inverse(invD_o1_id[ , ]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for time + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + M_id[ii, 4] * alpha[7] + } + + # Priors for the model for c1 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m4d + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[4] * M_lvlone[i, 5] + + beta[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[7] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:2] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] + mu_b_o1_id[ii, 2] <- beta[2] + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[6] + } + + + + # Priors for the model for o1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + for (k in 1:2) { + RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_o1_id[1:2, 1:2] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id) + D_o1_id[1:2, 1:2] <- inverse(invD_o1_id[ , ]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + } + + # Priors for the model for b2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] + } + + } + $m4e + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] + } + + + + # Priors for the model for o1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal_ridge_beta[k]) + tau_reg_ordinal_ridge_beta[k] ~ dgamma(0.01, 0.01) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + } + $m5a + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[7] * M_lvlone[i, 3] + eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[8] * M_lvlone[i, 3] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + } + + + + # Priors for the model for o1 + for (k in 1:8) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + + M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5] + } + + # Priors for the model for b2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 3] * alpha[6] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] + + M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9] + } + + # Priors for the model for C2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10] + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0) + } + + # Priors for the model for O2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + } + $m5b + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + + M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + + M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] + } + + } + $m5c + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + + M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + + M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] + } + + } + $m5d + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + + (M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] + + (M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] + + (M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8] + } + + + + # Priors for the model for o1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + + M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (ii in 1:100) { + M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] + M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] + M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] + } + + } + $m5e + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] + + beta[3] * M_id[group_id[i], 7] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] + + beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + + beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + + beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + + beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] + + beta[12] * M_id[group_id[i], 7] + + beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] + + beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + + beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + + beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + + beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) + p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:20) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + + M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (ii in 1:100) { + M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] + M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] + M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] + } + + } + $m6a + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[7] * M_lvlone[i, 3] + eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[8] * M_lvlone[i, 3] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + } + + + + # Priors for the model for o1 + for (k in 1:8) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + + M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5] + } + + # Priors for the model for b2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 3] * alpha[6] + + (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] + + M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9] + } + + # Priors for the model for C2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10] + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0) + } + + # Priors for the model for O2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + } + $m6b + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + + M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + + M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] + } + + } + $m6c + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + } + + + + # Priors for the model for o1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + + M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + + M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] + } + + } + $m6d + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + + M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + + M_id[ii, 9] * beta[5] + + (M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] + + (M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] + + (M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8] + } + + + + # Priors for the model for o1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + + M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (ii in 1:100) { + M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] + M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] + M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] + } + + } + $m6e + model { + + # Cumulative logit mixed effects model for o1 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) + eta_o1[i] <- b_o1_id[group_id[i], 1] + + eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] + + beta[3] * M_id[group_id[i], 7] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] + + beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + + beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + + beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + + beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] + + beta[12] * M_id[group_id[i], 7] + + beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] + + beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + + beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + + beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + + beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) + p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) + p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) + + logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] + logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] + + } + + for (ii in 1:100) { + b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) + mu_b_o1_id[ii, 1] <- 0 + } + + + + # Priors for the model for o1 + for (k in 1:20) { + beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) + + invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + + M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + + M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] + } + + # Priors for the model for c1 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + + M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] + log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + + M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] + log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + + M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] + + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 8:19) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + + M_id[ii, 9] * alpha[22] + } + + # Priors for the model for C2 + for (k in 20:22) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) + eta_O2[ii] <- 0 + + p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) + p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) + p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) + + logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] + logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] + + M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) + M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) + } + + # Priors for the model for O2 + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + + # Re-calculate interaction terms + for (ii in 1:100) { + M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] + M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] + M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] + } + + } + $m7a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + beta[3] * M_lvlone[i, 4] + + beta[4] * M_lvlone[i, 5] + beta[5] * M_lvlone[i, 6] + + beta[6] * M_lvlone[i, 7] + beta[7] * M_lvlone[i, 8] + + beta[8] * M_lvlone[i, 9] + beta[9] * M_lvlone[i, 10] + + beta[10] * M_lvlone[i, 11] + + beta[11] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for y + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 9] + + alpha[3] * M_lvlone[i, 10] + alpha[4] * M_lvlone[i, 11] + + alpha[5] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[1] + } + + + + # Priors for the model for o2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Cumulative logit mixed effects model for x ------------------------------------ + for (i in 1:329) { + M_lvlone[i, 3] ~ dcat(p_x[i, 1:4]) + eta_x[i] <- b_x_id[group_id[i], 1] + + alpha[7] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] + + p_x[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_x[i, 2:4]))) + p_x[i, 2] <- max(1e-10, min(1-1e-10, psum_x[i, 1] - psum_x[i, 2])) + p_x[i, 3] <- max(1e-10, min(1-1e-10, psum_x[i, 2] - psum_x[i, 3])) + p_x[i, 4] <- max(1e-10, min(1-1e-10, psum_x[i, 3])) + + logit(psum_x[i, 1]) <- gamma_x[1] + eta_x[i] + logit(psum_x[i, 2]) <- gamma_x[2] + eta_x[i] + logit(psum_x[i, 3]) <- gamma_x[3] + eta_x[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_x_id[ii, 1:1] ~ dnorm(mu_b_x_id[ii, ], invD_x_id[ , ]) + mu_b_x_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[6] + } + + + + # Priors for the model for x + for (k in 6:7) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_x[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_x[2] <- gamma_x[1] - exp(delta_x[1]) + gamma_x[3] <- gamma_x[2] - exp(delta_x[2]) + + invD_x_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_x_id[1, 1] <- 1 / (invD_x_id[1, 1]) + } + $m7b + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + beta[2] * M_lvlone[i, 5] + + beta[3] * M_lvlone[i, 6] + beta[4] * M_lvlone[i, 7] + + beta[5] * M_lvlone[i, 8] + beta[6] * M_lvlone[i, 9] + + beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[8] * M_lvlone[i, 10] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:8) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 5] + + alpha[3] * M_lvlone[i, 6] + alpha[4] * M_lvlone[i, 7] + + alpha[5] * M_lvlone[i, 8] + alpha[6] * M_lvlone[i, 9] + + alpha[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for b2 + for (k in 1:7) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] + + alpha[10] * M_lvlone[i, 6] + alpha[11] * M_lvlone[i, 7] + + alpha[12] * M_lvlone[i, 8] + alpha[13] * M_lvlone[i, 9] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] + } + + # Priors for the model for c2 + for (k in 8:13) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- 0 + } + + + + # Priors for the model for o2 + delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + } + +# GRcrit and MCerror give same result + + Code + lapply(models0, GR_crit, multivariate = FALSE) + Output + $m0a + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m0b + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o2[1] NaN NaN + gamma_o2[2] NaN NaN + gamma_o2[3] NaN NaN + D_o2_id[1,1] NaN NaN + + + $m1a + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + C1 NaN NaN + D_o1_id[1,1] NaN NaN + + + $m1b + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o2[1] NaN NaN + gamma_o2[2] NaN NaN + gamma_o2[3] NaN NaN + C1 NaN NaN + D_o2_id[1,1] NaN NaN + + + $m1c + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + c1 NaN NaN + D_o1_id[1,1] NaN NaN + + + $m1d + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o2[1] NaN NaN + gamma_o2[2] NaN NaN + gamma_o2[3] NaN NaN + c1 NaN NaN + D_o2_id[1,1] NaN NaN + + + $m2a + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + C2 NaN NaN + D_o1_id[1,1] NaN NaN + + + $m2b + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o2[1] NaN NaN + gamma_o2[2] NaN NaN + gamma_o2[3] NaN NaN + C2 NaN NaN + D_o2_id[1,1] NaN NaN + + + $m2c + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + c2 NaN NaN + D_o1_id[1,1] NaN NaN + + + $m2d + Potential scale reduction factors: + + Point est. Upper C.I. + gamma_o2[1] NaN NaN + gamma_o2[2] NaN NaN + gamma_o2[3] NaN NaN + c2 NaN NaN + D_o2_id[1,1] NaN NaN + + + $m3a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + o1.L NaN NaN + o1.Q NaN NaN + sigma_c1 NaN NaN + D_c1_id[1,1] NaN NaN + + + $m3b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + o22 NaN NaN + o23 NaN NaN + o24 NaN NaN + sigma_c1 NaN NaN + D_c1_id[1,1] NaN NaN + + + $m4a + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + abs(C1 - C2) NaN NaN + log(C1) NaN NaN + o22 NaN NaN + o23 NaN NaN + o24 NaN NaN + o22:abs(C1 - C2) NaN NaN + o23:abs(C1 - C2) NaN NaN + o24:abs(C1 - C2) NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m4b + Potential scale reduction factors: + + Point est. + abs(C1 - C2) NaN + log(C1) NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + gamma_o1[1] NaN + gamma_o1[2] NaN + D_o1_id[1,1] NaN + Upper C.I. + abs(C1 - C2) NaN + log(C1) NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + gamma_o1[1] NaN + gamma_o1[2] NaN + D_o1_id[1,1] NaN + + + $m4c + Potential scale reduction factors: + + Point est. Upper C.I. + C1 NaN NaN + B21 NaN NaN + time NaN NaN + c1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + D_o1_id[1,2] NaN NaN + D_o1_id[2,2] NaN NaN + D_o1_id[1,3] NaN NaN + D_o1_id[2,3] NaN NaN + D_o1_id[3,3] NaN NaN + D_o1_id[1,4] NaN NaN + D_o1_id[2,4] NaN NaN + D_o1_id[3,4] NaN NaN + D_o1_id[4,4] NaN NaN + + + $m4d + Potential scale reduction factors: + + Point est. Upper C.I. + C1 NaN NaN + time NaN NaN + I(time^2) NaN NaN + b21 NaN NaN + c1 NaN NaN + C1:time NaN NaN + b21:c1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + D_o1_id[1,2] NaN NaN + D_o1_id[2,2] NaN NaN + + + $m4e + Potential scale reduction factors: + + Point est. Upper C.I. + C1 NaN NaN + log(time) NaN NaN + I(time^2) NaN NaN + p1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m5a + Potential scale reduction factors: + + Point est. Upper C.I. + O22 NaN NaN + O23 NaN NaN + o12: C1 NaN NaN + o12: C2 NaN NaN + o13: C1 NaN NaN + o13: C2 NaN NaN + o12: b21 NaN NaN + o13: b21 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m5b + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + o13: C2 NaN NaN + c1:C2 NaN NaN + o12: C2 NaN NaN + o12: c1 NaN NaN + o13: c1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m5c + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + o12: C2 NaN NaN + o13: C2 NaN NaN + o12: c1 NaN NaN + o12: c1:C2 NaN NaN + o13: c1 NaN NaN + o13: c1:C2 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m5d + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + M22:C2 NaN NaN + M23:C2 NaN NaN + M24:C2 NaN NaN + o12: C2 NaN NaN + o13: C2 NaN NaN + o12: c1 NaN NaN + o13: c1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m5e + Potential scale reduction factors: + + Point est. Upper C.I. + o12: M22 NaN NaN + o12: M23 NaN NaN + o12: M24 NaN NaN + o12: C2 NaN NaN + o12: O22 NaN NaN + o12: O23 NaN NaN + o12: M22:C2 NaN NaN + o12: M23:C2 NaN NaN + o12: M24:C2 NaN NaN + o13: M22 NaN NaN + o13: M23 NaN NaN + o13: M24 NaN NaN + o13: C2 NaN NaN + o13: O22 NaN NaN + o13: O23 NaN NaN + o13: M22:C2 NaN NaN + o13: M23:C2 NaN NaN + o13: M24:C2 NaN NaN + o12: c1 NaN NaN + o13: c1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m6a + Potential scale reduction factors: + + Point est. Upper C.I. + O22 NaN NaN + O23 NaN NaN + o12: C1 NaN NaN + o12: C2 NaN NaN + o13: C1 NaN NaN + o13: C2 NaN NaN + o12: b21 NaN NaN + o13: b21 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m6b + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + o13: C2 NaN NaN + c1:C2 NaN NaN + o12: C2 NaN NaN + o12: c1 NaN NaN + o13: c1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m6c + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + o12: C2 NaN NaN + o13: C2 NaN NaN + o12: c1 NaN NaN + o12: c1:C2 NaN NaN + o13: c1 NaN NaN + o13: c1:C2 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m6d + Potential scale reduction factors: + + Point est. Upper C.I. + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + M22:C2 NaN NaN + M23:C2 NaN NaN + M24:C2 NaN NaN + o12: C2 NaN NaN + o13: C2 NaN NaN + o12: c1 NaN NaN + o13: c1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m6e + Potential scale reduction factors: + + Point est. Upper C.I. + o12: M22 NaN NaN + o12: M23 NaN NaN + o12: M24 NaN NaN + o12: C2 NaN NaN + o12: O22 NaN NaN + o12: O23 NaN NaN + o12: M22:C2 NaN NaN + o12: M23:C2 NaN NaN + o12: M24:C2 NaN NaN + o13: M22 NaN NaN + o13: M23 NaN NaN + o13: M24 NaN NaN + o13: C2 NaN NaN + o13: O22 NaN NaN + o13: O23 NaN NaN + o13: M22:C2 NaN NaN + o13: M23:C2 NaN NaN + o13: M24:C2 NaN NaN + o12: c1 NaN NaN + o13: c1 NaN NaN + gamma_o1[1] NaN NaN + gamma_o1[2] NaN NaN + D_o1_id[1,1] NaN NaN + + + $m7a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + o1.L NaN NaN + o1.Q NaN NaN + o22 NaN NaN + o23 NaN NaN + o24 NaN NaN + x2 NaN NaN + x3 NaN NaN + x4 NaN NaN + time NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m7b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + o22 NaN NaN + o23 NaN NaN + o24 NaN NaN + o1.L NaN NaN + o1.Q NaN NaN + c2 NaN NaN + b21 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + +--- + + Code + lapply(models0, MC_error) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + est MCSE SD MCSE/SD + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m0b + est MCSE SD MCSE/SD + gamma_o2[1] 0 0 0 NaN + gamma_o2[2] 0 0 0 NaN + gamma_o2[3] 0 0 0 NaN + D_o2_id[1,1] 0 0 0 NaN + + $m1a + est MCSE SD MCSE/SD + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + C1 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m1b + est MCSE SD MCSE/SD + gamma_o2[1] 0 0 0 NaN + gamma_o2[2] 0 0 0 NaN + gamma_o2[3] 0 0 0 NaN + C1 0 0 0 NaN + D_o2_id[1,1] 0 0 0 NaN + + $m1c + est MCSE SD MCSE/SD + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + c1 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m1d + est MCSE SD MCSE/SD + gamma_o2[1] 0 0 0 NaN + gamma_o2[2] 0 0 0 NaN + gamma_o2[3] 0 0 0 NaN + c1 0 0 0 NaN + D_o2_id[1,1] 0 0 0 NaN + + $m2a + est MCSE SD MCSE/SD + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + C2 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m2b + est MCSE SD MCSE/SD + gamma_o2[1] 0 0 0 NaN + gamma_o2[2] 0 0 0 NaN + gamma_o2[3] 0 0 0 NaN + C2 0 0 0 NaN + D_o2_id[1,1] 0 0 0 NaN + + $m2c + est MCSE SD MCSE/SD + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + c2 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m2d + est MCSE SD MCSE/SD + gamma_o2[1] 0 0 0 NaN + gamma_o2[2] 0 0 0 NaN + gamma_o2[3] 0 0 0 NaN + c2 0 0 0 NaN + D_o2_id[1,1] 0 0 0 NaN + + $m3a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + o1.L 0 0 0 NaN + o1.Q 0 0 0 NaN + sigma_c1 0 0 0 NaN + D_c1_id[1,1] 0 0 0 NaN + + $m3b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + o22 0 0 0 NaN + o23 0 0 0 NaN + o24 0 0 0 NaN + sigma_c1 0 0 0 NaN + D_c1_id[1,1] 0 0 0 NaN + + $m4a + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + abs(C1 - C2) 0 0 0 NaN + log(C1) 0 0 0 NaN + o22 0 0 0 NaN + o23 0 0 0 NaN + o24 0 0 0 NaN + o22:abs(C1 - C2) 0 0 0 NaN + o23:abs(C1 - C2) 0 0 0 NaN + o24:abs(C1 - C2) 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m4b + est MCSE SD MCSE/SD + abs(C1 - C2) 0 0 0 NaN + log(C1) 0 0 0 NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m4c + est MCSE SD MCSE/SD + C1 0 0 0 NaN + B21 0 0 0 NaN + time 0 0 0 NaN + c1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + D_o1_id[1,2] 0 0 0 NaN + D_o1_id[2,2] 0 0 0 NaN + D_o1_id[1,3] 0 0 0 NaN + D_o1_id[2,3] 0 0 0 NaN + D_o1_id[3,3] 0 0 0 NaN + D_o1_id[1,4] 0 0 0 NaN + D_o1_id[2,4] 0 0 0 NaN + D_o1_id[3,4] 0 0 0 NaN + D_o1_id[4,4] 0 0 0 NaN + + $m4d + est MCSE SD MCSE/SD + C1 0 0 0 NaN + time 0 0 0 NaN + I(time^2) 0 0 0 NaN + b21 0 0 0 NaN + c1 0 0 0 NaN + C1:time 0 0 0 NaN + b21:c1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + D_o1_id[1,2] 0 0 0 NaN + D_o1_id[2,2] 0 0 0 NaN + + $m4e + est MCSE SD MCSE/SD + C1 0 0 0 NaN + log(time) 0 0 0 NaN + I(time^2) 0 0 0 NaN + p1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m5a + est MCSE SD MCSE/SD + O22 0 0 0 NaN + O23 0 0 0 NaN + o12: C1 0 0 0 NaN + o12: C2 0 0 0 NaN + o13: C1 0 0 0 NaN + o13: C2 0 0 0 NaN + o12: b21 0 0 0 NaN + o13: b21 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m5b + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + o13: C2 0 0 0 NaN + c1:C2 0 0 0 NaN + o12: C2 0 0 0 NaN + o12: c1 0 0 0 NaN + o13: c1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m5c + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + o12: C2 0 0 0 NaN + o13: C2 0 0 0 NaN + o12: c1 0 0 0 NaN + o12: c1:C2 0 0 0 NaN + o13: c1 0 0 0 NaN + o13: c1:C2 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m5d + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + M22:C2 0 0 0 NaN + M23:C2 0 0 0 NaN + M24:C2 0 0 0 NaN + o12: C2 0 0 0 NaN + o13: C2 0 0 0 NaN + o12: c1 0 0 0 NaN + o13: c1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m5e + est MCSE SD MCSE/SD + o12: M22 0 0 0 NaN + o12: M23 0 0 0 NaN + o12: M24 0 0 0 NaN + o12: C2 0 0 0 NaN + o12: O22 0 0 0 NaN + o12: O23 0 0 0 NaN + o12: M22:C2 0 0 0 NaN + o12: M23:C2 0 0 0 NaN + o12: M24:C2 0 0 0 NaN + o13: M22 0 0 0 NaN + o13: M23 0 0 0 NaN + o13: M24 0 0 0 NaN + o13: C2 0 0 0 NaN + o13: O22 0 0 0 NaN + o13: O23 0 0 0 NaN + o13: M22:C2 0 0 0 NaN + o13: M23:C2 0 0 0 NaN + o13: M24:C2 0 0 0 NaN + o12: c1 0 0 0 NaN + o13: c1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m6a + est MCSE SD MCSE/SD + O22 0 0 0 NaN + O23 0 0 0 NaN + o12: C1 0 0 0 NaN + o12: C2 0 0 0 NaN + o13: C1 0 0 0 NaN + o13: C2 0 0 0 NaN + o12: b21 0 0 0 NaN + o13: b21 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m6b + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + o13: C2 0 0 0 NaN + c1:C2 0 0 0 NaN + o12: C2 0 0 0 NaN + o12: c1 0 0 0 NaN + o13: c1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m6c + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + o12: C2 0 0 0 NaN + o13: C2 0 0 0 NaN + o12: c1 0 0 0 NaN + o12: c1:C2 0 0 0 NaN + o13: c1 0 0 0 NaN + o13: c1:C2 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m6d + est MCSE SD MCSE/SD + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + M22:C2 0 0 0 NaN + M23:C2 0 0 0 NaN + M24:C2 0 0 0 NaN + o12: C2 0 0 0 NaN + o13: C2 0 0 0 NaN + o12: c1 0 0 0 NaN + o13: c1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m6e + est MCSE SD MCSE/SD + o12: M22 0 0 0 NaN + o12: M23 0 0 0 NaN + o12: M24 0 0 0 NaN + o12: C2 0 0 0 NaN + o12: O22 0 0 0 NaN + o12: O23 0 0 0 NaN + o12: M22:C2 0 0 0 NaN + o12: M23:C2 0 0 0 NaN + o12: M24:C2 0 0 0 NaN + o13: M22 0 0 0 NaN + o13: M23 0 0 0 NaN + o13: M24 0 0 0 NaN + o13: C2 0 0 0 NaN + o13: O22 0 0 0 NaN + o13: O23 0 0 0 NaN + o13: M22:C2 0 0 0 NaN + o13: M23:C2 0 0 0 NaN + o13: M24:C2 0 0 0 NaN + o12: c1 0 0 0 NaN + o13: c1 0 0 0 NaN + gamma_o1[1] 0 0 0 NaN + gamma_o1[2] 0 0 0 NaN + D_o1_id[1,1] 0 0 0 NaN + + $m7a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + o1.L 0 0 0 NaN + o1.Q 0 0 0 NaN + o22 0 0 0 NaN + o23 0 0 0 NaN + o24 0 0 0 NaN + x2 0 0 0 NaN + x3 0 0 0 NaN + x4 0 0 0 NaN + time 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m7b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + o22 0 0 0 NaN + o23 0 0 0 NaN + o24 0 0 0 NaN + o1.L 0 0 0 NaN + o1.Q 0 0 0 NaN + c2 0 0 0 NaN + b21 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + +# summary output remained the same on Windows + + Code + lapply(models0, print) + Output + + Call: + clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 + 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 + 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C1 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 C1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 c1 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C2 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 C2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 c2 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 c2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + Call: + lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) o1.L o1.Q + 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + Call: + lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) o22 o23 o24 + 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + Call: + clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 | + id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 + 0 0 0 0 + M24 abs(C1 - C2) log(C1) o22 + 0 0 0 0 + o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2) + 0 0 0 0 + o24:abs(C1 - C2) + 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 + 0 + o1 > 2 + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id), + data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C1 B21 time c1 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 o1 o1 o1 + (Intercept) c1 time c1:time + o1 (Intercept) 0 0 0 0 + o1 c1 0 0 0 0 + o1 time 0 0 0 0 + o1 c1:time 0 0 0 0 + + + Call: + clmm_imp(fixed = o1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C1 time I(time^2) b21 c1 C1:time + 0 0 0 0 0 0 0 0 + b21:c1 + 0 + + + Random effects covariance matrix: + $id + o1 o1 + (Intercept) time + o1 (Intercept) 0 0 + o1 time 0 0 + + + Call: + clmm_imp(fixed = o1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, + random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C1 log(time) I(time^2) p1 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 O22 O23 C1 C2 C1 C2 b21 b21 + 0 0 0 0 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 + 0 0 0 0 0 0 0 0 0 0 0 + c1 + 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 + 0 0 0 0 0 0 0 0 0 0 0 + c1 c1:C2 + 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C2 c1 c1 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 + 0 0 0 0 0 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21 + 0 0 0 0 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 + 0 0 0 0 0 0 0 0 0 0 0 + c1 + 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 + 0 0 0 0 0 0 0 0 0 0 0 + c1 c1:C2 + 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C2 c1 c1 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 + 0 0 0 0 0 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + Call: + lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 | + id, n.adapt = 5, n.iter = 10, seed = 2020) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 o1.L o1.Q o22 o23 + 0 0 0 0 0 0 + o24 x2 x3 x4 time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 | + id, n.adapt = 5, n.iter = 10, seed = 2020) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) o22 o23 o24 o1.L o1.Q + 0 0 0 0 0 0 + c2 b21 + 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + $m0a + + Call: + clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 + 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m0b + + Call: + clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 + 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + $m1a + + Call: + clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C1 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m1b + + Call: + clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 C1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + $m1c + + Call: + clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 c1 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m1d + + Call: + clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + $m2a + + Call: + clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C2 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m2b + + Call: + clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 C2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + $m2c + + Call: + clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 c2 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m2d + + Call: + clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o2" + + Fixed effects: + o2 > 1 o2 > 2 o2 > 3 c2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + o2 + (Intercept) + o2 (Intercept) 0 + + + $m3a + + Call: + lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) o1.L o1.Q + 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + $m3b + + Call: + lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) o22 o23 o24 + 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + $m4a + + Call: + clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 | + id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 + 0 0 0 0 + M24 abs(C1 - C2) log(C1) o22 + 0 0 0 0 + o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2) + 0 0 0 0 + o24:abs(C1 - C2) + 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m4b + + Call: + clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 + 0 + o1 > 2 + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m4c + + Call: + clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id), + data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C1 B21 time c1 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 o1 o1 o1 + (Intercept) c1 time c1:time + o1 (Intercept) 0 0 0 0 + o1 c1 0 0 0 0 + o1 time 0 0 0 0 + o1 c1:time 0 0 0 0 + + + $m4d + + Call: + clmm_imp(fixed = o1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C1 time I(time^2) b21 c1 C1:time + 0 0 0 0 0 0 0 0 + b21:c1 + 0 + + + Random effects covariance matrix: + $id + o1 o1 + (Intercept) time + o1 (Intercept) 0 0 + o1 time 0 0 + + + $m4e + + Call: + clmm_imp(fixed = o1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, + random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 C1 log(time) I(time^2) p1 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m5a + + Call: + clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 O22 O23 C1 C2 C1 C2 b21 b21 + 0 0 0 0 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m5b + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 + 0 0 0 0 0 0 0 0 0 0 0 + c1 + 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m5c + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 + 0 0 0 0 0 0 0 0 0 0 0 + c1 c1:C2 + 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m5d + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C2 c1 c1 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m5e + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 > 1 o1 > 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 + 0 0 0 0 0 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m6a + + Call: + clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21 + 0 0 0 0 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m6b + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 + 0 0 0 0 0 0 0 0 0 0 0 + c1 + 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m6c + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 + 0 0 0 0 0 0 0 0 0 0 0 + c1 c1:C2 + 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m6d + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 + 0 0 0 0 0 0 0 0 0 0 0 + C2 c1 c1 + 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m6e + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian cumulative logit mixed model for "o1" + + Fixed effects: + o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 0 0 0 0 0 + M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 + 0 0 0 0 0 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + o1 + (Intercept) + o1 (Intercept) 0 + + + $m7a + + Call: + lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 | + id, n.adapt = 5, n.iter = 10, seed = 2020) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 o1.L o1.Q o22 o23 + 0 0 0 0 0 0 + o24 x2 x3 x4 time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m7b + + Call: + lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 | + id, n.adapt = 5, n.iter = 10, seed = 2020) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) o22 o23 o24 o1.L o1.Q + 0 0 0 0 0 0 + c2 b21 + 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + +--- + + Code + lapply(models0, coef) + Output + $m0a + $m0a$o1 + D_o1_id[1,1] o1 > 1 o1 > 2 + 0 0 0 + + + $m0b + $m0b$o2 + D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 + 0 0 0 0 + + + $m1a + $m1a$o1 + C1 D_o1_id[1,1] o1 > 1 o1 > 2 + 0 0 0 0 + + + $m1b + $m1b$o2 + C1 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 + 0 0 0 0 0 + + + $m1c + $m1c$o1 + c1 D_o1_id[1,1] o1 > 1 o1 > 2 + 0 0 0 0 + + + $m1d + $m1d$o2 + c1 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 + 0 0 0 0 0 + + + $m2a + $m2a$o1 + C2 D_o1_id[1,1] o1 > 1 o1 > 2 + 0 0 0 0 + + + $m2b + $m2b$o2 + C2 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 + 0 0 0 0 0 + + + $m2c + $m2c$o1 + c2 D_o1_id[1,1] o1 > 1 o1 > 2 + 0 0 0 0 + + + $m2d + $m2d$o2 + c2 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 + 0 0 0 0 0 + + + $m3a + $m3a$c1 + (Intercept) o1.L o1.Q sigma_c1 D_c1_id[1,1] + 0 0 0 0 0 + + + $m3b + $m3b$c1 + (Intercept) o22 o23 o24 sigma_c1 D_c1_id[1,1] + 0 0 0 0 0 0 + + + $m4a + $m4a$o1 + M22 M23 M24 abs(C1 - C2) + 0 0 0 0 + log(C1) o22 o23 o24 + 0 0 0 0 + o22:abs(C1 - C2) o23:abs(C1 - C2) o24:abs(C1 - C2) D_o1_id[1,1] + 0 0 0 0 + o1 > 1 o1 > 2 + 0 0 + + + $m4b + $m4b$o1 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + D_o1_id[1,1] + 0 + o1 > 1 + 0 + o1 > 2 + 0 + + + $m4c + $m4c$o1 + C1 B21 time c1 D_o1_id[1,1] D_o1_id[1,2] + 0 0 0 0 0 0 + D_o1_id[2,2] D_o1_id[1,3] D_o1_id[2,3] D_o1_id[3,3] D_o1_id[1,4] D_o1_id[2,4] + 0 0 0 0 0 0 + D_o1_id[3,4] D_o1_id[4,4] o1 > 1 o1 > 2 + 0 0 0 0 + + + $m4d + $m4d$o1 + C1 time I(time^2) b21 c1 C1:time + 0 0 0 0 0 0 + b21:c1 D_o1_id[1,1] D_o1_id[1,2] D_o1_id[2,2] o1 > 1 o1 > 2 + 0 0 0 0 0 0 + + + $m4e + $m4e$o1 + C1 log(time) I(time^2) p1 D_o1_id[1,1] o1 > 1 + 0 0 0 0 0 0 + o1 > 2 + 0 + + + $m5a + $m5a$o1 + O22 O23 C1 C2 C1 C2 + 0 0 0 0 0 0 + b21 b21 D_o1_id[1,1] o1 > 1 o1 > 2 + 0 0 0 0 0 + + + $m5b + $m5b$o1 + M22 M23 M24 O22 O23 c1:C2 + 0 0 0 0 0 0 + C2 C2 c1 c1 D_o1_id[1,1] o1 > 1 + 0 0 0 0 0 0 + o1 > 2 + 0 + + + $m5c + $m5c$o1 + M22 M23 M24 O22 O23 C2 + 0 0 0 0 0 0 + C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1] + 0 0 0 0 0 0 + o1 > 1 o1 > 2 + 0 0 + + + $m5d + $m5d$o1 + M22 M23 M24 O22 O23 M22:C2 + 0 0 0 0 0 0 + M23:C2 M24:C2 C2 C2 c1 c1 + 0 0 0 0 0 0 + D_o1_id[1,1] o1 > 1 o1 > 2 + 0 0 0 + + + $m5e + $m5e$o1 + M22 M23 M24 C2 O22 O23 + 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 M22 M23 M24 + 0 0 0 0 0 0 + C2 O22 O23 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 + c1 c1 D_o1_id[1,1] o1 > 1 o1 > 2 + 0 0 0 0 0 + + + $m6a + $m6a$o1 + O22 O23 C1 C2 C1 C2 + 0 0 0 0 0 0 + b21 b21 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 + 0 0 0 0 0 + + + $m6b + $m6b$o1 + M22 M23 M24 O22 O23 c1:C2 + 0 0 0 0 0 0 + C2 C2 c1 c1 D_o1_id[1,1] o1 ≤ 1 + 0 0 0 0 0 0 + o1 ≤ 2 + 0 + + + $m6c + $m6c$o1 + M22 M23 M24 O22 O23 C2 + 0 0 0 0 0 0 + C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1] + 0 0 0 0 0 0 + o1 ≤ 1 o1 ≤ 2 + 0 0 + + + $m6d + $m6d$o1 + M22 M23 M24 O22 O23 M22:C2 + 0 0 0 0 0 0 + M23:C2 M24:C2 C2 C2 c1 c1 + 0 0 0 0 0 0 + D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 + 0 0 0 + + + $m6e + $m6e$o1 + M22 M23 M24 C2 O22 O23 + 0 0 0 0 0 0 + M22:C2 M23:C2 M24:C2 M22 M23 M24 + 0 0 0 0 0 0 + C2 O22 O23 M22:C2 M23:C2 M24:C2 + 0 0 0 0 0 0 + c1 c1 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 + 0 0 0 0 0 + + + $m7a + $m7a$y + (Intercept) C1 o1.L o1.Q o22 o23 + 0 0 0 0 0 0 + o24 x2 x3 x4 time sigma_y + 0 0 0 0 0 0 + D_y_id[1,1] + 0 + + + $m7b + $m7b$y + (Intercept) o22 o23 o24 o1.L o1.Q + 0 0 0 0 0 0 + c2 b21 sigma_y D_y_id[1,1] + 0 0 0 0 + + + +--- + + Code + lapply(models0, confint) + Output + $m0a + $m0a$o1 + 2.5% 97.5% + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m0b + $m0b$o2 + 2.5% 97.5% + D_o2_id[1,1] 0 0 + o2 > 1 0 0 + o2 > 2 0 0 + o2 > 3 0 0 + + + $m1a + $m1a$o1 + 2.5% 97.5% + C1 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m1b + $m1b$o2 + 2.5% 97.5% + C1 0 0 + D_o2_id[1,1] 0 0 + o2 > 1 0 0 + o2 > 2 0 0 + o2 > 3 0 0 + + + $m1c + $m1c$o1 + 2.5% 97.5% + c1 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m1d + $m1d$o2 + 2.5% 97.5% + c1 0 0 + D_o2_id[1,1] 0 0 + o2 > 1 0 0 + o2 > 2 0 0 + o2 > 3 0 0 + + + $m2a + $m2a$o1 + 2.5% 97.5% + C2 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m2b + $m2b$o2 + 2.5% 97.5% + C2 0 0 + D_o2_id[1,1] 0 0 + o2 > 1 0 0 + o2 > 2 0 0 + o2 > 3 0 0 + + + $m2c + $m2c$o1 + 2.5% 97.5% + c2 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m2d + $m2d$o2 + 2.5% 97.5% + c2 0 0 + D_o2_id[1,1] 0 0 + o2 > 1 0 0 + o2 > 2 0 0 + o2 > 3 0 0 + + + $m3a + $m3a$c1 + 2.5% 97.5% + (Intercept) 0 0 + o1.L 0 0 + o1.Q 0 0 + sigma_c1 0 0 + D_c1_id[1,1] 0 0 + + + $m3b + $m3b$c1 + 2.5% 97.5% + (Intercept) 0 0 + o22 0 0 + o23 0 0 + o24 0 0 + sigma_c1 0 0 + D_c1_id[1,1] 0 0 + + + $m4a + $m4a$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + o22 0 0 + o23 0 0 + o24 0 0 + o22:abs(C1 - C2) 0 0 + o23:abs(C1 - C2) 0 0 + o24:abs(C1 - C2) 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m4b + $m4b$o1 + 2.5% 97.5% + abs(C1 - C2) 0 0 + log(C1) 0 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m4c + $m4c$o1 + 2.5% 97.5% + C1 0 0 + B21 0 0 + time 0 0 + c1 0 0 + D_o1_id[1,1] 0 0 + D_o1_id[1,2] 0 0 + D_o1_id[2,2] 0 0 + D_o1_id[1,3] 0 0 + D_o1_id[2,3] 0 0 + D_o1_id[3,3] 0 0 + D_o1_id[1,4] 0 0 + D_o1_id[2,4] 0 0 + D_o1_id[3,4] 0 0 + D_o1_id[4,4] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m4d + $m4d$o1 + 2.5% 97.5% + C1 0 0 + time 0 0 + I(time^2) 0 0 + b21 0 0 + c1 0 0 + C1:time 0 0 + b21:c1 0 0 + D_o1_id[1,1] 0 0 + D_o1_id[1,2] 0 0 + D_o1_id[2,2] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m4e + $m4e$o1 + 2.5% 97.5% + C1 0 0 + log(time) 0 0 + I(time^2) 0 0 + p1 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m5a + $m5a$o1 + 2.5% 97.5% + O22 0 0 + O23 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + b21 0 0 + b21 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m5b + $m5b$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + c1:C2 0 0 + C2 0 0 + C2 0 0 + c1 0 0 + c1 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m5c + $m5c$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + C2 0 0 + C2 0 0 + c1 0 0 + c1:C2 0 0 + c1 0 0 + c1:C2 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m5d + $m5d$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + C2 0 0 + C2 0 0 + c1 0 0 + c1 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m5e + $m5e$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + c1 0 0 + c1 0 0 + D_o1_id[1,1] 0 0 + o1 > 1 0 0 + o1 > 2 0 0 + + + $m6a + $m6a$o1 + 2.5% 97.5% + O22 0 0 + O23 0 0 + C1 0 0 + C2 0 0 + C1 0 0 + C2 0 0 + b21 0 0 + b21 0 0 + D_o1_id[1,1] 0 0 + o1 ≤ 1 0 0 + o1 ≤ 2 0 0 + + + $m6b + $m6b$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + c1:C2 0 0 + C2 0 0 + C2 0 0 + c1 0 0 + c1 0 0 + D_o1_id[1,1] 0 0 + o1 ≤ 1 0 0 + o1 ≤ 2 0 0 + + + $m6c + $m6c$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + C2 0 0 + C2 0 0 + c1 0 0 + c1:C2 0 0 + c1 0 0 + c1:C2 0 0 + D_o1_id[1,1] 0 0 + o1 ≤ 1 0 0 + o1 ≤ 2 0 0 + + + $m6d + $m6d$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + C2 0 0 + C2 0 0 + c1 0 0 + c1 0 0 + D_o1_id[1,1] 0 0 + o1 ≤ 1 0 0 + o1 ≤ 2 0 0 + + + $m6e + $m6e$o1 + 2.5% 97.5% + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + C2 0 0 + O22 0 0 + O23 0 0 + M22:C2 0 0 + M23:C2 0 0 + M24:C2 0 0 + c1 0 0 + c1 0 0 + D_o1_id[1,1] 0 0 + o1 ≤ 1 0 0 + o1 ≤ 2 0 0 + + + $m7a + $m7a$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + o1.L 0 0 + o1.Q 0 0 + o22 0 0 + o23 0 0 + o24 0 0 + x2 0 0 + x3 0 0 + x4 0 0 + time 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m7b + $m7b$y + 2.5% 97.5% + (Intercept) 0 0 + o22 0 0 + o23 0 0 + o24 0 0 + o1.L 0 0 + o1.Q 0 0 + c2 0 0 + b21 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + +--- + + Code + lapply(models0, summary) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m0b + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o2 > 1 0 0 0 0 0 NaN NaN + o2 > 2 0 0 0 0 0 NaN NaN + o2 > 3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m1a + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m1b + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o2 > 1 0 0 0 0 0 NaN NaN + o2 > 2 0 0 0 0 0 NaN NaN + o2 > 3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m1c + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m1d + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o2 > 1 0 0 0 0 0 NaN NaN + o2 > 2 0 0 0 0 0 NaN NaN + o2 > 3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m2a + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m2b + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o2 > 1 0 0 0 0 0 NaN NaN + o2 > 2 0 0 0 0 0 NaN NaN + o2 > 3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m2c + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + c2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m2d + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + c2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o2 > 1 0 0 0 0 0 NaN NaN + o2 > 2 0 0 0 0 0 NaN NaN + o2 > 3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m3a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + o1.L 0 0 0 0 0 NaN NaN + o1.Q 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_c1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_c1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m3b + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_c1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_c1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4a + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 | + id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + o22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + o23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + o24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4b + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% + abs(C1 - C2) 0 0 0 0 + log(C1) 0 0 0 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0 + tail-prob. GR-crit + abs(C1 - C2) 0 NaN + log(C1) 0 NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN + MCE/SD + abs(C1 - C2) NaN + log(C1) NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4c + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id), + data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + D_o1_id[1,2] 0 0 0 0 0 NaN NaN + D_o1_id[2,2] 0 0 0 0 NaN NaN + D_o1_id[1,3] 0 0 0 0 0 NaN NaN + D_o1_id[2,3] 0 0 0 0 0 NaN NaN + D_o1_id[3,3] 0 0 0 0 NaN NaN + D_o1_id[1,4] 0 0 0 0 0 NaN NaN + D_o1_id[2,4] 0 0 0 0 0 NaN NaN + D_o1_id[3,4] 0 0 0 0 0 NaN NaN + D_o1_id[4,4] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4d + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + I(time^2) 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + C1:time 0 0 0 0 0 NaN NaN + b21:c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + D_o1_id[1,2] 0 0 0 0 0 NaN NaN + D_o1_id[2,2] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4e + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, + random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + log(time) 0 0 0 0 0 NaN NaN + I(time^2) 0 0 0 0 0 NaN NaN + p1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m5a + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C1 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C1 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: b21 0 0 0 0 0 NaN NaN + o13: b21 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m5b + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + c1:C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m5c + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o12: c1:C2 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + o13: c1:C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m5d + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + M22:C2 0 0 0 0 0 NaN NaN + M23:C2 0 0 0 0 0 NaN NaN + M24:C2 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m5e + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o12: M22 0 0 0 0 0 NaN NaN + o12: M23 0 0 0 0 0 NaN NaN + o12: M24 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o12: O22 0 0 0 0 0 NaN NaN + o12: O23 0 0 0 0 0 NaN NaN + o12: M22:C2 0 0 0 0 0 NaN NaN + o12: M23:C2 0 0 0 0 0 NaN NaN + o12: M24:C2 0 0 0 0 0 NaN NaN + o13: M22 0 0 0 0 0 NaN NaN + o13: M23 0 0 0 0 0 NaN NaN + o13: M24 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o13: O22 0 0 0 0 0 NaN NaN + o13: O23 0 0 0 0 0 NaN NaN + o13: M22:C2 0 0 0 0 0 NaN NaN + o13: M23:C2 0 0 0 0 0 NaN NaN + o13: M24:C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 > 1 0 0 0 0 0 NaN NaN + o1 > 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m6a + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C1 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C1 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: b21 0 0 0 0 0 NaN NaN + o13: b21 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 ≤ 1 0 0 0 0 0 NaN NaN + o1 ≤ 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m6b + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + c1:C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 ≤ 1 0 0 0 0 0 NaN NaN + o1 ≤ 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m6c + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o12: c1:C2 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + o13: c1:C2 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 ≤ 1 0 0 0 0 0 NaN NaN + o1 ≤ 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m6d + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + M22:C2 0 0 0 0 0 NaN NaN + M23:C2 0 0 0 0 0 NaN NaN + M24:C2 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 ≤ 1 0 0 0 0 0 NaN NaN + o1 ≤ 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m6e + + Bayesian cumulative logit mixed model fitted with JointAI + + Call: + clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), + nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o12: M22 0 0 0 0 0 NaN NaN + o12: M23 0 0 0 0 0 NaN NaN + o12: M24 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o12: O22 0 0 0 0 0 NaN NaN + o12: O23 0 0 0 0 0 NaN NaN + o12: M22:C2 0 0 0 0 0 NaN NaN + o12: M23:C2 0 0 0 0 0 NaN NaN + o12: M24:C2 0 0 0 0 0 NaN NaN + o13: M22 0 0 0 0 0 NaN NaN + o13: M23 0 0 0 0 0 NaN NaN + o13: M24 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o13: O22 0 0 0 0 0 NaN NaN + o13: O23 0 0 0 0 0 NaN NaN + o13: M22:C2 0 0 0 0 0 NaN NaN + o13: M23:C2 0 0 0 0 0 NaN NaN + o13: M24:C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + Posterior summary of the intercepts: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o1 ≤ 1 0 0 0 0 0 NaN NaN + o1 ≤ 2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_o1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m7a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 | + id, n.adapt = 5, n.iter = 10, seed = 2020) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + o1.L 0 0 0 0 0 NaN NaN + o1.Q 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + x2 0 0 0 0 0 NaN NaN + x3 0 0 0 0 0 NaN NaN + x4 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m7b + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 | + id, n.adapt = 5, n.iter = 10, seed = 2020) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + o1.L 0 0 0 0 0 NaN NaN + o1.Q 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + +--- + + Code + lapply(models0, function(x) coef(summary(x))) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + $m0a$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + + $m0b + $m0b$o2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + + $m1a + $m1a$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + + + $m1b + $m1b$o2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + + + $m1c + $m1c$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + c1 0 0 0 0 0 NaN NaN + + + $m1d + $m1d$o2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + c1 0 0 0 0 0 NaN NaN + + + $m2a + $m2a$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m2b + $m2b$o2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m2c + $m2c$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + c2 0 0 0 0 0 NaN NaN + + + $m2d + $m2d$o2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + c2 0 0 0 0 0 NaN NaN + + + $m3a + $m3a$c1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + o1.L 0 0 0 0 0 NaN NaN + o1.Q 0 0 0 0 0 NaN NaN + + + $m3b + $m3b$c1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + + + $m4a + $m4a$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + o22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + o23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + o24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + $m4b + $m4b$o1 + Mean SD 2.5% 97.5% + abs(C1 - C2) 0 0 0 0 + log(C1) 0 0 0 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 0 + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0 + tail-prob. GR-crit + abs(C1 - C2) 0 NaN + log(C1) 0 NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN + MCE/SD + abs(C1 - C2) NaN + log(C1) NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN + ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + + + $m4c + $m4c$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + + + $m4d + $m4d$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + I(time^2) 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + C1:time 0 0 0 0 0 NaN NaN + b21:c1 0 0 0 0 0 NaN NaN + + + $m4e + $m4e$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C1 0 0 0 0 0 NaN NaN + log(time) 0 0 0 0 0 NaN NaN + I(time^2) 0 0 0 0 0 NaN NaN + p1 0 0 0 0 0 NaN NaN + + + $m5a + $m5a$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C1 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C1 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: b21 0 0 0 0 0 NaN NaN + o13: b21 0 0 0 0 0 NaN NaN + + + $m5b + $m5b$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + c1:C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + + $m5c + $m5c$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o12: c1:C2 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + o13: c1:C2 0 0 0 0 0 NaN NaN + + + $m5d + $m5d$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + M22:C2 0 0 0 0 0 NaN NaN + M23:C2 0 0 0 0 0 NaN NaN + M24:C2 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + + $m5e + $m5e$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o12: M22 0 0 0 0 0 NaN NaN + o12: M23 0 0 0 0 0 NaN NaN + o12: M24 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o12: O22 0 0 0 0 0 NaN NaN + o12: O23 0 0 0 0 0 NaN NaN + o12: M22:C2 0 0 0 0 0 NaN NaN + o12: M23:C2 0 0 0 0 0 NaN NaN + o12: M24:C2 0 0 0 0 0 NaN NaN + o13: M22 0 0 0 0 0 NaN NaN + o13: M23 0 0 0 0 0 NaN NaN + o13: M24 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o13: O22 0 0 0 0 0 NaN NaN + o13: O23 0 0 0 0 0 NaN NaN + o13: M22:C2 0 0 0 0 0 NaN NaN + o13: M23:C2 0 0 0 0 0 NaN NaN + o13: M24:C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + + $m6a + $m6a$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C1 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C1 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: b21 0 0 0 0 0 NaN NaN + o13: b21 0 0 0 0 0 NaN NaN + + + $m6b + $m6b$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + c1:C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + + $m6c + $m6c$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o12: c1:C2 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + o13: c1:C2 0 0 0 0 0 NaN NaN + + + $m6d + $m6d$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + M22:C2 0 0 0 0 0 NaN NaN + M23:C2 0 0 0 0 0 NaN NaN + M24:C2 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + + $m6e + $m6e$o1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + o12: M22 0 0 0 0 0 NaN NaN + o12: M23 0 0 0 0 0 NaN NaN + o12: M24 0 0 0 0 0 NaN NaN + o12: C2 0 0 0 0 0 NaN NaN + o12: O22 0 0 0 0 0 NaN NaN + o12: O23 0 0 0 0 0 NaN NaN + o12: M22:C2 0 0 0 0 0 NaN NaN + o12: M23:C2 0 0 0 0 0 NaN NaN + o12: M24:C2 0 0 0 0 0 NaN NaN + o13: M22 0 0 0 0 0 NaN NaN + o13: M23 0 0 0 0 0 NaN NaN + o13: M24 0 0 0 0 0 NaN NaN + o13: C2 0 0 0 0 0 NaN NaN + o13: O22 0 0 0 0 0 NaN NaN + o13: O23 0 0 0 0 0 NaN NaN + o13: M22:C2 0 0 0 0 0 NaN NaN + o13: M23:C2 0 0 0 0 0 NaN NaN + o13: M24:C2 0 0 0 0 0 NaN NaN + o12: c1 0 0 0 0 0 NaN NaN + o13: c1 0 0 0 0 0 NaN NaN + + + $m7a + $m7a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + o1.L 0 0 0 0 0 NaN NaN + o1.Q 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + x2 0 0 0 0 0 NaN NaN + x3 0 0 0 0 0 NaN NaN + x4 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + $m7b + $m7b$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + o1.L 0 0 0 0 0 NaN NaN + o1.Q 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + + + diff --git a/tests/testthat/_snaps/coxph.md b/tests/testthat/_snaps/coxph.md new file mode 100644 index 00000000..e4f2e189 --- /dev/null +++ b/tests/testthat/_snaps/coxph.md @@ -0,0 +1,2161 @@ +# jagsmodel remains the same + + Code + lapply(models, "[[", "jagsmodel") + Output + $m0a + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (i in 1:312) { + logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) + eta_Srv_ftm_stts_cn[i] <- 0 + logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] + + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) + + log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) + phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) + zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + } + $m1a + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (i in 1:312) { + logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) + eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] + + M_lvlone[i, 5] * beta[2] + logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] + + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) + + log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) + phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) + zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + } + $m1b + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (i in 1:312) { + logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) + eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] + + M_lvlone[i, 5] * beta[2] + logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] + + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) + + log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) + phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) + zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + } + $m2a + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (i in 1:312) { + logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) + eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] + logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] + + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) + + log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) + phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) + zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + + + # Normal model for copper ------------------------------------------------------- + for (i in 1:312) { + M_lvlone[i, 3] ~ dnorm(mu_copper[i], tau_copper) + mu_copper[i] <- M_lvlone[i, 4] * alpha[1] + } + + # Priors for the model for copper + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_copper <- sqrt(1/tau_copper) + + } + $m3a + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (i in 1:312) { + logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) + eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] + + M_lvlone[i, 6] * beta[2] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4] + + (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * beta[5] + logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] + + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) + + log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) + phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) + zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + + + # Normal model for trig --------------------------------------------------------- + for (i in 1:312) { + M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, ) + mu_trig[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[4] + + M_lvlone[i, 9] <- log(M_lvlone[i, 3]) + + + } + + # Priors for the model for trig + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_trig <- sqrt(1/tau_trig) + + + + # Normal model for copper ------------------------------------------------------- + for (i in 1:312) { + M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper) + mu_copper[i] <- M_lvlone[i, 5] * alpha[5] + M_lvlone[i, 6] * alpha[6] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[7] + + M_lvlone[i, 8] <- abs(M_lvlone[i, 7] - M_lvlone[i, 4]) + + + } + + # Priors for the model for copper + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_copper <- sqrt(1/tau_copper) + + } + $m3b + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (i in 1:312) { + logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) + eta_Srv_ftm_stts_cn[i] <- b_Srv_ftm_stts_cn_center[group_center[i], 1] + + beta[1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[2] * M_lvlone[i, 5] + + beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + + beta[5] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] + + logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) + + log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) + phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) + zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) + } + + for (ii in 1:10) { + b_Srv_ftm_stts_cn_center[ii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[ii, ], invD_Srv_ftm_stts_cn_center[ , ]) + mu_b_Srv_ftm_stts_cn_center[ii, 1] <- 0 + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1]) + + + # Normal mixed effects model for trig ------------------------------------------- + for (i in 1:312) { + M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, ) + mu_trig[i] <- b_trig_center[group_center[i], 1] + + alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[3] * M_lvlone[i, 5] + + alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 8] <- log(M_lvlone[i, 3]) + + } + + for (ii in 1:10) { + b_trig_center[ii, 1:1] ~ dnorm(mu_b_trig_center[ii, ], invD_trig_center[ , ]) + mu_b_trig_center[ii, 1] <- M_center[ii, 1] * alpha[1] + } + + # Priors for the model for trig + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_trig <- sqrt(1/tau_trig) + + invD_trig_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_trig_center[1, 1] <- 1 / (invD_trig_center[1, 1]) + + + # Normal mixed effects model for copper ----------------------------------------- + for (i in 1:312) { + M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper) + mu_copper[i] <- b_copper_center[group_center[i], 1] + alpha[6] * M_lvlone[i, 5] + + alpha[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 7] <- abs(M_lvlone[i, 6] - M_lvlone[i, 4]) + + } + + for (ii in 1:10) { + b_copper_center[ii, 1:1] ~ dnorm(mu_b_copper_center[ii, ], invD_copper_center[ , ]) + mu_b_copper_center[ii, 1] <- M_center[ii, 1] * alpha[5] + } + + # Priors for the model for copper + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_copper <- sqrt(1/tau_copper) + + invD_copper_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_copper_center[1, 1] <- 1 / (invD_copper_center[1, 1]) + } + $m4a + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (ii in 1:312) { + logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ]) + eta_Srv_ftm_stts_cn[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[1] + + M_id[ii, 5] * beta[2] + M_id[ii, 6] * beta[3] + logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + + M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] * beta[6] + + M_lvlone[srow_Srv_ftm_stts_cn[ii], 4] * beta[7] + + M_lvlone[srow_Srv_ftm_stts_cn[ii], 5] * beta[8] + + logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + + (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] + + (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + + M_lvlonegk[ii, 3, 1:15] * beta[6] + + M_lvlonegk[ii, 4, 1:15] * beta[7] + + M_lvlonegk[ii, 5, 1:15] * beta[8]) + + log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) + phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) + zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii]) + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:8) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + } + $m4b + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (ii in 1:312) { + logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ]) + eta_Srv_ftm_stts_cn[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[1] + + M_id[ii, 5] * beta[2] + M_id[ii, 6] * beta[3] + + M_id[ii, 7] * beta[4] + logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] + + logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + + (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] + + (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]) + + log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) + phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) + zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii]) + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + } + $m4c + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (ii in 1:312) { + logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ]) + eta_Srv_ftm_stts_cn[ii] <- b_Srv_ftm_stts_cn_center[group_center[pos_id[ii]], 1] + + beta[1] * (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[2] * M_id[ii, 4] + logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + + logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + + (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + + (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]) + + log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) + phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) + zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii]) + } + + for (iii in 1:10) { + b_Srv_ftm_stts_cn_center[iii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[iii, ], invD_Srv_ftm_stts_cn_center[ , ]) + mu_b_Srv_ftm_stts_cn_center[iii, 1] <- 0 + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1]) + } + $m4d + model { + + # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- + for (ii in 1:312) { + logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ]) + eta_Srv_ftm_stts_cn[ii] <- b_Srv_ftm_stts_cn_center[group_center[pos_id[ii]], 1] + + beta[1] * (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[2] * M_id[ii, 4] + logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + + (M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5] + + logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] + Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + + (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + + (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + + (M_lvlonegk[ii, 3, 1:15] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5]) + + log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) + phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) + zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii]) + } + + for (iii in 1:10) { + b_Srv_ftm_stts_cn_center[iii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[iii, ], invD_Srv_ftm_stts_cn_center[ , ]) + mu_b_Srv_ftm_stts_cn_center[iii, 1] <- 0 + } + + + # Priors for the coefficients in the model for Srv_ftm_stts_cn + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + for (k in 1:6) { + beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1]) + } + +# GRcrit and MCerror give same result + + Code + lapply(models0, GR_crit, multivariate = FALSE) + Output + $m0a + Potential scale reduction factors: + + Point est. Upper C.I. + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + + + $m1a + Potential scale reduction factors: + + Point est. Upper C.I. + age NaN NaN + sexfemale NaN NaN + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + + + $m1b + Potential scale reduction factors: + + Point est. Upper C.I. + age NaN NaN + sexfemale NaN NaN + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + + + $m2a + Potential scale reduction factors: + + Point est. Upper C.I. + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + copper NaN NaN + + + $m3a + Potential scale reduction factors: + + Point est. Upper C.I. + copper NaN NaN + sexfemale NaN NaN + age NaN NaN + abs(age - copper) NaN NaN + log(trig) NaN NaN + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + + + $m3b + Potential scale reduction factors: + + Point est. Upper C.I. + copper NaN NaN + sexfemale NaN NaN + age NaN NaN + abs(age - copper) NaN NaN + log(trig) NaN NaN + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + D_Srv_ftm_stts_cn_center[1,1] NaN NaN + + + $m4a + Potential scale reduction factors: + + Point est. Upper C.I. + age NaN NaN + sexfemale NaN NaN + trtplacebo NaN NaN + albumin NaN NaN + platelet NaN NaN + stage.L NaN NaN + stage.Q NaN NaN + stage.C NaN NaN + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + + + $m4b + Potential scale reduction factors: + + Point est. Upper C.I. + age NaN NaN + sexfemale NaN NaN + trtplacebo NaN NaN + sexfemale:trtplacebo NaN NaN + albumin NaN NaN + log(platelet) NaN NaN + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + + + $m4c + Potential scale reduction factors: + + Point est. Upper C.I. + age NaN NaN + sexfemale NaN NaN + albumin NaN NaN + log(platelet) NaN NaN + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + D_Srv_ftm_stts_cn_center[1,1] NaN NaN + + + $m4d + Potential scale reduction factors: + + Point est. Upper C.I. + age NaN NaN + sexfemale NaN NaN + albumin NaN NaN + ns(platelet, df = 2)1 NaN NaN + ns(platelet, df = 2)2 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN + D_Srv_ftm_stts_cn_center[1,1] NaN NaN + + + +--- + + Code + lapply(models0, MC_error) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + est MCSE SD MCSE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + + $m1a + est MCSE SD MCSE/SD + age 0 0 0 NaN + sexfemale 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + + $m1b + est MCSE SD MCSE/SD + age 0 0 0 NaN + sexfemale 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + + $m2a + est MCSE SD MCSE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + copper 0 0 0 NaN + + $m3a + est MCSE SD MCSE/SD + copper 0 0 0 NaN + sexfemale 0 0 0 NaN + age 0 0 0 NaN + abs(age - copper) 0 0 0 NaN + log(trig) 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + + $m3b + est MCSE SD MCSE/SD + copper 0 0 0 NaN + sexfemale 0 0 0 NaN + age 0 0 0 NaN + abs(age - copper) 0 0 0 NaN + log(trig) 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN + + $m4a + est MCSE SD MCSE/SD + age 0 0 0 NaN + sexfemale 0 0 0 NaN + trtplacebo 0 0 0 NaN + albumin 0 0 0 NaN + platelet 0 0 0 NaN + stage.L 0 0 0 NaN + stage.Q 0 0 0 NaN + stage.C 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + + $m4b + est MCSE SD MCSE/SD + age 0 0 0 NaN + sexfemale 0 0 0 NaN + trtplacebo 0 0 0 NaN + sexfemale:trtplacebo 0 0 0 NaN + albumin 0 0 0 NaN + log(platelet) 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + + $m4c + est MCSE SD MCSE/SD + age 0 0 0 NaN + sexfemale 0 0 0 NaN + albumin 0 0 0 NaN + log(platelet) 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN + + $m4d + est MCSE SD MCSE/SD + age 0 0 0 NaN + sexfemale 0 0 0 NaN + albumin 0 0 0 NaN + ns(platelet, df = 2)1 0 0 0 NaN + ns(platelet, df = 2)2 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN + D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN + + +# summary output remained the same + + Code + lapply(models0, print) + Output + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2, + n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale + 0 0 + + Call: + coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age + + sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))" + + + Coefficients: + age sexfemale + 0 0 + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper, + data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + copper + 0 + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, + NA))) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + copper sexfemale age abs(age - copper) + 0 0 0 0 + log(trig) + 0 + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig) + (1 | center), + data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(trig = c(1e-04, NA))) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + copper sexfemale age abs(age - copper) + 0 0 0 0 + log(trig) + 0 + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + trt + albumin + platelet + stage + (1 | id), data = PBC, + n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, + timevar = "day") + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale trtplacebo albumin platelet stage.L stage.Q + 0 0 0 0 0 0 0 + stage.C + 0 + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex * trt + albumin + log(platelet) + (1 | id), data = PBC, + n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, + timevar = "day") + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale trtplacebo + 0 0 0 + sexfemale:trtplacebo albumin log(platelet) + 0 0 0 + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + albumin + log(platelet) + (1 | id) + (1 | center), + data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, timevar = "day") + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale albumin log(platelet) + 0 0 0 0 + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center), + data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, timevar = "day") + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale albumin + 0 0 0 + ns(platelet, df = 2)1 ns(platelet, df = 2)2 + 0 0 + $m0a + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2, + n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + $m1a + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale + 0 0 + + $m1b + + Call: + coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age + + sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))" + + + Coefficients: + age sexfemale + 0 0 + + $m2a + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper, + data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + copper + 0 + + $m3a + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, + NA))) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + copper sexfemale age abs(age - copper) + 0 0 0 0 + log(trig) + 0 + + $m3b + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig) + (1 | center), + data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(trig = c(1e-04, NA))) + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + copper sexfemale age abs(age - copper) + 0 0 0 0 + log(trig) + 0 + + $m4a + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + trt + albumin + platelet + stage + (1 | id), data = PBC, + n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, + timevar = "day") + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale trtplacebo albumin platelet stage.L stage.Q + 0 0 0 0 0 0 0 + stage.C + 0 + + $m4b + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex * trt + albumin + log(platelet) + (1 | id), data = PBC, + n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, + timevar = "day") + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale trtplacebo + 0 0 0 + sexfemale:trtplacebo albumin log(platelet) + 0 0 0 + + $m4c + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + albumin + log(platelet) + (1 | id) + (1 | center), + data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, timevar = "day") + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale albumin log(platelet) + 0 0 0 0 + + $m4d + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center), + data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, timevar = "day") + + Bayesian proportional hazards model for "Surv(futime, status != "censored")" + + + Coefficients: + age sexfemale albumin + 0 0 0 + ns(platelet, df = 2)1 ns(platelet, df = 2)2 + 0 0 + + +--- + + Code + lapply(models0, coef) + Output + $m0a + $m0a$`Surv(futime, status != "censored")` + beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] + 0 0 + + + $m1a + $m1a$`Surv(futime, status != "censored")` + age sexfemale + 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] + 0 0 + + + $m1b + $m1b$`Surv(futime, I(status != "censored"))` + age sexfemale + 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] + 0 0 + + + $m2a + $m2a$`Surv(futime, status != "censored")` + copper beta_Bh0_Srv_ftm_stts_cn[1] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] + 0 + + + $m3a + $m3a$`Surv(futime, status != "censored")` + copper sexfemale + 0 0 + age abs(age - copper) + 0 0 + log(trig) beta_Bh0_Srv_ftm_stts_cn[1] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] + 0 + + + $m3b + $m3b$`Surv(futime, status != "censored")` + copper sexfemale + 0 0 + age abs(age - copper) + 0 0 + log(trig) D_Srv_ftm_stts_cn_center[1,1] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] + 0 0 + + + $m4a + $m4a$`Surv(futime, status != "censored")` + age sexfemale + 0 0 + trtplacebo albumin + 0 0 + platelet stage.L + 0 0 + stage.Q stage.C + 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] + 0 0 + + + $m4b + $m4b$`Surv(futime, status != "censored")` + age sexfemale + 0 0 + trtplacebo sexfemale:trtplacebo + 0 0 + albumin log(platelet) + 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] + 0 0 + + + $m4c + $m4c$`Surv(futime, status != "censored")` + age sexfemale + 0 0 + albumin log(platelet) + 0 0 + D_Srv_ftm_stts_cn_center[1,1] beta_Bh0_Srv_ftm_stts_cn[1] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] + 0 + + + $m4d + $m4d$`Surv(futime, status != "censored")` + age sexfemale + 0 0 + albumin ns(platelet, df = 2)1 + 0 0 + ns(platelet, df = 2)2 D_Srv_ftm_stts_cn_center[1,1] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] + 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] + 0 0 + + + +--- + + Code + lapply(models0, confint) + Output + $m0a + $m0a$`Surv(futime, status != "censored")` + 2.5% 97.5% + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m1a + $m1a$`Surv(futime, status != "censored")` + 2.5% 97.5% + age 0 0 + sexfemale 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m1b + $m1b$`Surv(futime, I(status != "censored"))` + 2.5% 97.5% + age 0 0 + sexfemale 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m2a + $m2a$`Surv(futime, status != "censored")` + 2.5% 97.5% + copper 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m3a + $m3a$`Surv(futime, status != "censored")` + 2.5% 97.5% + copper 0 0 + sexfemale 0 0 + age 0 0 + abs(age - copper) 0 0 + log(trig) 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m3b + $m3b$`Surv(futime, status != "censored")` + 2.5% 97.5% + copper 0 0 + sexfemale 0 0 + age 0 0 + abs(age - copper) 0 0 + log(trig) 0 0 + D_Srv_ftm_stts_cn_center[1,1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m4a + $m4a$`Surv(futime, status != "censored")` + 2.5% 97.5% + age 0 0 + sexfemale 0 0 + trtplacebo 0 0 + albumin 0 0 + platelet 0 0 + stage.L 0 0 + stage.Q 0 0 + stage.C 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m4b + $m4b$`Surv(futime, status != "censored")` + 2.5% 97.5% + age 0 0 + sexfemale 0 0 + trtplacebo 0 0 + sexfemale:trtplacebo 0 0 + albumin 0 0 + log(platelet) 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m4c + $m4c$`Surv(futime, status != "censored")` + 2.5% 97.5% + age 0 0 + sexfemale 0 0 + albumin 0 0 + log(platelet) 0 0 + D_Srv_ftm_stts_cn_center[1,1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + $m4d + $m4d$`Surv(futime, status != "censored")` + 2.5% 97.5% + age 0 0 + sexfemale 0 0 + albumin 0 0 + ns(platelet, df = 2)1 0 0 + ns(platelet, df = 2)2 0 0 + D_Srv_ftm_stts_cn_center[1,1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 + + + +--- + + Code + lapply(models0, summary) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2, + n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 2:5 + Sample size per chain = 4 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m1a + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:6 + Sample size per chain = 4 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m1b + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age + + sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:6 + Sample size per chain = 4 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m2a + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper, + data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, + mess = FALSE) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + copper 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:6 + Sample size per chain = 4 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m3a + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, + NA))) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + copper 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + abs(age - copper) 0 0 0 0 0 NaN NaN + log(trig) 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:12 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m3b + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig) + (1 | center), + data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(trig = c(1e-04, NA))) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + copper 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + abs(age - copper) 0 0 0 0 0 NaN NaN + log(trig) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:12 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + Number of groups: + - center: 10 + + $m4a + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + trt + albumin + platelet + stage + (1 | id), data = PBC, + n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, + timevar = "day") + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + trtplacebo 0 0 0 0 0 NaN NaN + albumin 0 0 0 0 0 NaN NaN + platelet 0 0 0 0 0 NaN NaN + stage.L 0 0 0 0 0 NaN NaN + stage.Q 0 0 0 0 0 NaN NaN + stage.C 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:12 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 2257 + Number of groups: + - id: 312 + + $m4b + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex * trt + albumin + log(platelet) + (1 | id), data = PBC, + n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, + timevar = "day") + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + trtplacebo 0 0 0 0 0 NaN NaN + sexfemale:trtplacebo 0 0 0 0 0 NaN NaN + albumin 0 0 0 0 0 NaN NaN + log(platelet) 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:12 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 2257 + Number of groups: + - id: 312 + + $m4c + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + albumin + log(platelet) + (1 | id) + (1 | center), + data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, timevar = "day") + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + albumin 0 0 0 0 0 NaN NaN + log(platelet) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:12 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 2257 + Number of groups: + - center: 10 + - id: 312 + + $m4d + + Bayesian proportional hazards model fitted with JointAI + + Call: + coxph_imp(formula = Surv(futime, status != "censored") ~ age + + sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center), + data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, timevar = "day") + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + albumin 0 0 0 0 0 NaN NaN + ns(platelet, df = 2)1 0 0 0 0 0 NaN NaN + ns(platelet, df = 2)2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN + beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 3:12 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 2257 + Number of groups: + - center: 10 + - id: 312 + + +--- + + Code + lapply(models0, function(x) coef(summary(x))) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + $m0a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + + + $m1a + $m1a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + + + $m1b + $m1b$`Surv(futime, I(status != "censored"))` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + + + $m2a + $m2a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + copper 0 0 0 0 0 NaN NaN + + + $m3a + $m3a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + copper 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + abs(age - copper) 0 0 0 0 0 NaN NaN + log(trig) 0 0 0 0 0 NaN NaN + + + $m3b + $m3b$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + copper 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + abs(age - copper) 0 0 0 0 0 NaN NaN + log(trig) 0 0 0 0 0 NaN NaN + + + $m4a + $m4a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + trtplacebo 0 0 0 0 0 NaN NaN + albumin 0 0 0 0 0 NaN NaN + platelet 0 0 0 0 0 NaN NaN + stage.L 0 0 0 0 0 NaN NaN + stage.Q 0 0 0 0 0 NaN NaN + stage.C 0 0 0 0 0 NaN NaN + + + $m4b + $m4b$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + trtplacebo 0 0 0 0 0 NaN NaN + sexfemale:trtplacebo 0 0 0 0 0 NaN NaN + albumin 0 0 0 0 0 NaN NaN + log(platelet) 0 0 0 0 0 NaN NaN + + + $m4c + $m4c$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + albumin 0 0 0 0 0 NaN NaN + log(platelet) 0 0 0 0 0 NaN NaN + + + $m4d + $m4d$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + albumin 0 0 0 0 0 NaN NaN + ns(platelet, df = 2)1 0 0 0 0 0 NaN NaN + ns(platelet, df = 2)2 0 0 0 0 0 NaN NaN + + + diff --git a/tests/testthat/_snaps/glm.md b/tests/testthat/_snaps/glm.md new file mode 100644 index 00000000..460597df --- /dev/null +++ b/tests/testthat/_snaps/glm.md @@ -0,0 +1,17588 @@ +# data_list remains the same + + Code + lapply(models, "[[", "data_list") + Output + $m0a1 + $m0a1$M_lvlone + y (Intercept) + 1 -4.76915977 1 + 2 -2.69277172 1 + 3 -1.17551547 1 + 4 -4.57464473 1 + 5 -2.20260004 1 + 6 -3.48995315 1 + 7 -0.44987258 1 + 8 -2.29588848 1 + 9 -4.49135812 1 + 10 -5.52545368 1 + 11 -4.16286741 1 + 12 -2.93455761 1 + 13 -0.04202496 1 + 14 -1.63149775 1 + 15 -0.97786151 1 + 16 -1.79100431 1 + 17 -6.26520032 1 + 18 -1.36028709 1 + 19 -1.15396597 1 + 20 -3.21707239 1 + 21 -1.59389898 1 + 22 -5.50335066 1 + 23 0.57290123 1 + 24 -8.22270323 1 + 25 -1.41364158 1 + 26 -6.28031574 1 + 27 -3.15624425 1 + 28 -3.55693639 1 + 29 -1.11821124 1 + 30 -2.82834175 1 + 31 -3.72259860 1 + 32 -1.75256656 1 + 33 -5.55044409 1 + 34 -7.45068147 1 + 35 -0.97491919 1 + 36 -2.98356481 1 + 37 -1.86039471 1 + 38 -7.28754607 1 + 39 -8.66234796 1 + 40 -4.16291375 1 + 41 -3.48250771 1 + 42 -7.27930410 1 + 43 -6.12866190 1 + 44 -4.96880803 1 + 45 -4.76746713 1 + 46 -1.91249177 1 + 47 -0.61884029 1 + 48 -0.20496175 1 + 49 -7.12636055 1 + 50 -6.23103837 1 + 51 -3.32561065 1 + 52 -2.95942339 1 + 53 -4.44915114 1 + 54 -0.81566463 1 + 55 -6.50029573 1 + 56 -2.74718050 1 + 57 -6.35015663 1 + 58 -2.69505883 1 + 59 -1.55660833 1 + 60 -3.76240209 1 + 61 -3.92885797 1 + 62 -1.72044748 1 + 63 -0.56602625 1 + 64 -4.42235015 1 + 65 -2.39122287 1 + 66 -0.81807247 1 + 67 -6.48196782 1 + 68 -1.37306273 1 + 69 -4.99886487 1 + 70 -5.82288217 1 + 71 -2.68234219 1 + 72 -3.96170442 1 + 73 -7.19573667 1 + 74 -5.08799713 1 + 75 -1.32967262 1 + 76 -2.56532332 1 + 77 -3.21002900 1 + 78 -3.40559790 1 + 79 -4.56223913 1 + 80 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71 1.425058 1 3 NA 0.72818697 0.006827078 1 NA + 72 1.432371 1 2 NA 0.54568411 -0.456055171 1 NA + 73 1.441656 1 3 0.6767456 0.14601189 0.346486708 1 NA + 74 1.434952 1 2 0.7328840 NA 0.205092215 1 NA + 75 1.402860 1 NA 0.7946099 0.83970611 -0.136596858 1 NA + 76 1.453363 1 3 NA NA -0.500179043 1 NA + 77 1.432909 1 2 0.5296147 0.23594577 0.527352086 1 NA + 78 1.435103 1 2 0.7723288 0.40933787 0.022742250 1 NA + 79 1.434462 1 0 0.8079308 0.58749646 NA 1 NA + 80 1.434661 1 NA 0.5214822 0.57911860 -0.002032440 1 NA + 81 1.445881 0 2 NA 0.35894249 -0.154246160 1 NA + 82 1.442548 NA 1 0.8332107 0.27822593 0.140201825 1 NA + 83 1.430097 1 2 0.4544158 NA -0.141417121 1 NA + 84 1.430119 1 0 0.6482660 0.73298261 NA 1 NA + 85 1.430315 1 5 0.7272109 0.50590638 -0.021285339 1 NA + 86 1.437584 NA 0 0.7302426 0.34320856 -0.010196306 1 NA + 87 1.409738 NA 3 0.6768061 0.37651326 -0.089747520 1 NA + 88 1.422388 1 2 0.8115758 NA -0.083699898 1 NA + 89 1.422509 1 1 0.9775567 0.39008384 -0.044061996 1 NA + 90 1.439432 1 NA 0.6408465 NA -0.209291697 1 NA + 91 1.430175 1 2 0.5917453 NA 0.639036426 1 NA + 92 1.418002 NA 6 0.7224845 0.60913196 0.094698299 1 NA + 93 1.423812 1 0 0.4501596 0.39610480 -0.055510622 1 NA + 94 1.423473 1 4 0.5190455 0.49000093 -0.421318463 1 NA + 95 1.434412 1 3 0.7305821 0.37935661 0.125295503 1 NA + 96 1.450844 1 NA 0.9696445 0.77341877 0.213084904 1 NA + 97 1.433371 NA 3 0.7087457 NA -0.161914659 1 NA + 98 1.444378 1 3 NA 0.82491133 -0.034767685 1 NA + 99 1.422523 0 5 0.9084899 0.36317443 -0.320681689 1 NA + 100 1.410394 NA 2 0.9296776 0.34363945 0.058192962 1 NA + + $m3a$spM_lvlone + center scale + C1 1.43410054 0.01299651 + B2 NA NA + P2 2.17500000 1.68969325 + L1mis 0.72626070 0.15364470 + Be2 0.52042833 0.19439164 + C2 -0.06490582 0.33317347 + (Intercept) NA NA + B21 NA NA + + $m3a$mu_reg_norm + [1] 0 + + $m3a$tau_reg_norm + [1] 1e-04 + + $m3a$shape_tau_norm + [1] 0.01 + + $m3a$rate_tau_norm + [1] 0.01 + + $m3a$mu_reg_gamma + [1] 0 + + $m3a$tau_reg_gamma + [1] 1e-04 + + $m3a$shape_tau_gamma + [1] 0.01 + + $m3a$rate_tau_gamma + [1] 0.01 + + $m3a$mu_reg_beta + [1] 0 + + $m3a$tau_reg_beta + [1] 1e-04 + + $m3a$shape_tau_beta + [1] 0.01 + + $m3a$rate_tau_beta + [1] 0.01 + + $m3a$mu_reg_binom + [1] 0 + + $m3a$tau_reg_binom + [1] 1e-04 + + $m3a$mu_reg_poisson + [1] 0 + + $m3a$tau_reg_poisson + [1] 1e-04 + + + $m3b + $m3b$M_lvlone + C1 B2 P2 L1mis C2 (Intercept) B21 + 1 1.410531 1 0 0.9364352 0.144065882 1 NA + 2 1.434183 1 2 0.8943541 0.032778478 1 NA + 3 1.430994 1 1 0.2868460 0.343008492 1 NA + 4 1.453096 1 1 NA -0.361887858 1 NA + 5 1.438344 1 0 0.7621346 -0.389600647 1 NA + 6 1.453207 NA 1 0.5858621 -0.205306841 1 NA + 7 1.425176 1 1 0.7194403 0.079434830 1 NA + 8 1.437908 1 0 0.7593154 -0.331246757 1 NA + 9 1.416911 1 2 0.5863705 -0.329638800 1 NA + 10 1.448638 NA 0 NA 0.167597533 1 NA + 11 1.428375 1 3 0.7218028 0.860207989 1 NA + 12 1.450130 1 0 0.7241254 0.022730640 1 NA + 13 1.420545 1 5 NA 0.217171172 1 NA + 14 1.423005 1 0 0.5289014 -0.403002412 1 NA + 15 1.435902 1 1 0.7322482 0.087369742 1 NA + 16 1.423901 1 4 0.7462471 -0.183870429 1 NA + 17 1.457208 1 NA 0.9119922 -0.194577002 1 NA + 18 1.414280 1 1 0.6262513 -0.349718516 1 NA + 19 1.443383 NA NA NA -0.508781244 1 NA + 20 1.434954 NA 3 0.7173364 0.494883111 1 NA + 21 1.429499 1 3 0.7288999 0.258041067 1 NA + 22 1.441897 NA 4 0.7160420 -0.922621989 1 NA + 23 1.423713 NA 6 0.5795514 0.431254949 1 NA + 24 1.435395 1 4 0.7210413 -0.294218881 1 NA + 25 1.425944 NA NA 0.7816086 -0.425548895 1 NA + 26 1.437115 NA 1 NA 0.057176054 1 NA + 27 1.441326 1 1 0.4746725 0.289090158 1 NA + 28 1.422953 1 2 0.9270652 -0.473079489 1 NA + 29 1.437797 1 NA 0.5306249 -0.385664863 1 NA + 30 1.472121 1 1 0.8913764 -0.154780107 1 NA + 31 1.421782 NA 5 0.8090308 0.100536296 1 NA + 32 1.457672 1 NA NA 0.634791958 1 NA + 33 1.430842 1 0 NA -0.387252617 1 NA + 34 1.431523 0 2 0.6375974 -0.181741088 1 NA + 35 1.421395 1 4 0.9202563 -0.311562695 1 NA + 36 1.434496 1 2 0.7263222 -0.044115907 1 NA + 37 1.425383 1 4 1.0638781 -0.657409991 1 NA + 38 1.421802 NA NA 0.6053893 0.159577214 1 NA + 39 1.430094 1 2 0.7945509 -0.460416933 1 NA + 40 1.447621 NA NA 0.6355032 NA 1 NA + 41 1.434797 1 2 NA -0.248909867 1 NA + 42 1.446091 1 6 1.0690739 -0.609021545 1 NA + 43 1.445306 1 1 NA 0.025471883 1 NA + 44 1.448783 1 2 0.7595403 0.066648592 1 NA + 45 1.450617 1 1 NA -0.276108719 1 NA + 46 1.415055 1 2 0.4929132 -0.179737577 1 NA + 47 1.436590 0 3 NA 0.181190937 1 NA + 48 1.433938 1 2 NA -0.453871693 1 NA + 49 1.414941 0 NA NA 0.448629602 1 NA + 50 1.421807 1 2 0.6292812 -0.529811821 1 NA + 51 1.453203 1 NA NA -0.028304571 1 NA + 52 1.452129 1 1 0.9735411 -0.520318482 1 NA + 53 1.431510 1 NA 0.7156259 0.171317619 1 NA + 54 1.430082 1 NA 0.5184434 0.432732046 1 NA + 55 1.443492 1 1 0.7948965 -0.346286005 1 NA + 56 1.436460 1 6 0.5191792 -0.469375653 1 NA + 57 1.418119 1 2 0.9233108 0.031021711 1 NA + 58 1.434971 NA 7 0.8025356 -0.118837515 1 NA + 59 1.445599 1 NA 0.8546624 0.507769984 1 NA + 60 1.437097 NA 2 0.8639819 0.271797031 1 NA + 61 1.428360 1 2 0.7521237 -0.124442204 1 NA + 62 1.440550 1 2 0.5590215 0.277677389 1 NA + 63 1.443014 1 2 NA -0.102893730 1 NA + 64 1.424298 1 1 0.6071272 NA 1 NA + 65 1.448823 1 0 0.8837829 -0.678303052 1 NA + 66 1.425834 0 NA 0.7775301 0.478880037 1 NA + 67 1.427102 1 NA 0.6756191 -0.428028760 1 NA + 68 1.414240 1 0 0.7857549 0.048119185 1 NA + 69 1.456218 NA NA 0.9119262 0.216932805 1 NA + 70 1.470594 1 NA 0.5816103 -0.234575269 1 NA + 71 1.425058 1 3 NA 0.006827078 1 NA + 72 1.432371 1 2 NA -0.456055171 1 NA + 73 1.441656 1 3 0.6767456 0.346486708 1 NA + 74 1.434952 1 2 0.7328840 0.205092215 1 NA + 75 1.402860 1 NA 0.7946099 -0.136596858 1 NA + 76 1.453363 1 3 NA -0.500179043 1 NA + 77 1.432909 1 2 0.5296147 0.527352086 1 NA + 78 1.435103 1 2 0.7723288 0.022742250 1 NA + 79 1.434462 1 0 0.8079308 NA 1 NA + 80 1.434661 1 NA 0.5214822 -0.002032440 1 NA + 81 1.445881 0 2 NA -0.154246160 1 NA + 82 1.442548 NA 1 0.8332107 0.140201825 1 NA + 83 1.430097 1 2 0.4544158 -0.141417121 1 NA + 84 1.430119 1 0 0.6482660 NA 1 NA + 85 1.430315 1 5 0.7272109 -0.021285339 1 NA + 86 1.437584 NA 0 0.7302426 -0.010196306 1 NA + 87 1.409738 NA 3 0.6768061 -0.089747520 1 NA + 88 1.422388 1 2 0.8115758 -0.083699898 1 NA + 89 1.422509 1 1 0.9775567 -0.044061996 1 NA + 90 1.439432 1 NA 0.6408465 -0.209291697 1 NA + 91 1.430175 1 2 0.5917453 0.639036426 1 NA + 92 1.418002 NA 6 0.7224845 0.094698299 1 NA + 93 1.423812 1 0 0.4501596 -0.055510622 1 NA + 94 1.423473 1 4 0.5190455 -0.421318463 1 NA + 95 1.434412 1 3 0.7305821 0.125295503 1 NA + 96 1.450844 1 NA 0.9696445 0.213084904 1 NA + 97 1.433371 NA 3 0.7087457 -0.161914659 1 NA + 98 1.444378 1 3 NA -0.034767685 1 NA + 99 1.422523 0 5 0.9084899 -0.320681689 1 NA + 100 1.410394 NA 2 0.9296776 0.058192962 1 NA + + $m3b$spM_lvlone + center scale + C1 1.43410054 0.01299651 + B2 NA NA + P2 2.17500000 1.68969325 + L1mis 0.72626070 0.15364470 + C2 -0.06490582 0.33317347 + (Intercept) NA NA + B21 NA NA + + $m3b$mu_reg_norm + [1] 0 + + $m3b$tau_reg_norm + [1] 1e-04 + + $m3b$shape_tau_norm + [1] 0.01 + + $m3b$rate_tau_norm + [1] 0.01 + + $m3b$mu_reg_binom + [1] 0 + + $m3b$tau_reg_binom + [1] 1e-04 + + $m3b$mu_reg_poisson + [1] 0 + + $m3b$tau_reg_poisson + [1] 1e-04 + + + $m3c + $m3c$M_lvlone + C1 B2 P2 L1mis C2 (Intercept) B21 + 1 1.410531 1 0 0.9364352 0.144065882 1 NA + 2 1.434183 1 2 0.8943541 0.032778478 1 NA + 3 1.430994 1 1 0.2868460 0.343008492 1 NA + 4 1.453096 1 1 NA -0.361887858 1 NA + 5 1.438344 1 0 0.7621346 -0.389600647 1 NA + 6 1.453207 NA 1 0.5858621 -0.205306841 1 NA + 7 1.425176 1 1 0.7194403 0.079434830 1 NA + 8 1.437908 1 0 0.7593154 -0.331246757 1 NA + 9 1.416911 1 2 0.5863705 -0.329638800 1 NA + 10 1.448638 NA 0 NA 0.167597533 1 NA + 11 1.428375 1 3 0.7218028 0.860207989 1 NA + 12 1.450130 1 0 0.7241254 0.022730640 1 NA + 13 1.420545 1 5 NA 0.217171172 1 NA + 14 1.423005 1 0 0.5289014 -0.403002412 1 NA + 15 1.435902 1 1 0.7322482 0.087369742 1 NA + 16 1.423901 1 4 0.7462471 -0.183870429 1 NA + 17 1.457208 1 NA 0.9119922 -0.194577002 1 NA + 18 1.414280 1 1 0.6262513 -0.349718516 1 NA + 19 1.443383 NA NA NA -0.508781244 1 NA + 20 1.434954 NA 3 0.7173364 0.494883111 1 NA + 21 1.429499 1 3 0.7288999 0.258041067 1 NA + 22 1.441897 NA 4 0.7160420 -0.922621989 1 NA + 23 1.423713 NA 6 0.5795514 0.431254949 1 NA + 24 1.435395 1 4 0.7210413 -0.294218881 1 NA + 25 1.425944 NA NA 0.7816086 -0.425548895 1 NA + 26 1.437115 NA 1 NA 0.057176054 1 NA + 27 1.441326 1 1 0.4746725 0.289090158 1 NA + 28 1.422953 1 2 0.9270652 -0.473079489 1 NA + 29 1.437797 1 NA 0.5306249 -0.385664863 1 NA + 30 1.472121 1 1 0.8913764 -0.154780107 1 NA + 31 1.421782 NA 5 0.8090308 0.100536296 1 NA + 32 1.457672 1 NA NA 0.634791958 1 NA + 33 1.430842 1 0 NA -0.387252617 1 NA + 34 1.431523 0 2 0.6375974 -0.181741088 1 NA + 35 1.421395 1 4 0.9202563 -0.311562695 1 NA + 36 1.434496 1 2 0.7263222 -0.044115907 1 NA + 37 1.425383 1 4 1.0638781 -0.657409991 1 NA + 38 1.421802 NA NA 0.6053893 0.159577214 1 NA + 39 1.430094 1 2 0.7945509 -0.460416933 1 NA + 40 1.447621 NA NA 0.6355032 NA 1 NA + 41 1.434797 1 2 NA -0.248909867 1 NA + 42 1.446091 1 6 1.0690739 -0.609021545 1 NA + 43 1.445306 1 1 NA 0.025471883 1 NA + 44 1.448783 1 2 0.7595403 0.066648592 1 NA + 45 1.450617 1 1 NA -0.276108719 1 NA + 46 1.415055 1 2 0.4929132 -0.179737577 1 NA + 47 1.436590 0 3 NA 0.181190937 1 NA + 48 1.433938 1 2 NA -0.453871693 1 NA + 49 1.414941 0 NA NA 0.448629602 1 NA + 50 1.421807 1 2 0.6292812 -0.529811821 1 NA + 51 1.453203 1 NA NA -0.028304571 1 NA + 52 1.452129 1 1 0.9735411 -0.520318482 1 NA + 53 1.431510 1 NA 0.7156259 0.171317619 1 NA + 54 1.430082 1 NA 0.5184434 0.432732046 1 NA + 55 1.443492 1 1 0.7948965 -0.346286005 1 NA + 56 1.436460 1 6 0.5191792 -0.469375653 1 NA + 57 1.418119 1 2 0.9233108 0.031021711 1 NA + 58 1.434971 NA 7 0.8025356 -0.118837515 1 NA + 59 1.445599 1 NA 0.8546624 0.507769984 1 NA + 60 1.437097 NA 2 0.8639819 0.271797031 1 NA + 61 1.428360 1 2 0.7521237 -0.124442204 1 NA + 62 1.440550 1 2 0.5590215 0.277677389 1 NA + 63 1.443014 1 2 NA -0.102893730 1 NA + 64 1.424298 1 1 0.6071272 NA 1 NA + 65 1.448823 1 0 0.8837829 -0.678303052 1 NA + 66 1.425834 0 NA 0.7775301 0.478880037 1 NA + 67 1.427102 1 NA 0.6756191 -0.428028760 1 NA + 68 1.414240 1 0 0.7857549 0.048119185 1 NA + 69 1.456218 NA NA 0.9119262 0.216932805 1 NA + 70 1.470594 1 NA 0.5816103 -0.234575269 1 NA + 71 1.425058 1 3 NA 0.006827078 1 NA + 72 1.432371 1 2 NA -0.456055171 1 NA + 73 1.441656 1 3 0.6767456 0.346486708 1 NA + 74 1.434952 1 2 0.7328840 0.205092215 1 NA + 75 1.402860 1 NA 0.7946099 -0.136596858 1 NA + 76 1.453363 1 3 NA -0.500179043 1 NA + 77 1.432909 1 2 0.5296147 0.527352086 1 NA + 78 1.435103 1 2 0.7723288 0.022742250 1 NA + 79 1.434462 1 0 0.8079308 NA 1 NA + 80 1.434661 1 NA 0.5214822 -0.002032440 1 NA + 81 1.445881 0 2 NA -0.154246160 1 NA + 82 1.442548 NA 1 0.8332107 0.140201825 1 NA + 83 1.430097 1 2 0.4544158 -0.141417121 1 NA + 84 1.430119 1 0 0.6482660 NA 1 NA + 85 1.430315 1 5 0.7272109 -0.021285339 1 NA + 86 1.437584 NA 0 0.7302426 -0.010196306 1 NA + 87 1.409738 NA 3 0.6768061 -0.089747520 1 NA + 88 1.422388 1 2 0.8115758 -0.083699898 1 NA + 89 1.422509 1 1 0.9775567 -0.044061996 1 NA + 90 1.439432 1 NA 0.6408465 -0.209291697 1 NA + 91 1.430175 1 2 0.5917453 0.639036426 1 NA + 92 1.418002 NA 6 0.7224845 0.094698299 1 NA + 93 1.423812 1 0 0.4501596 -0.055510622 1 NA + 94 1.423473 1 4 0.5190455 -0.421318463 1 NA + 95 1.434412 1 3 0.7305821 0.125295503 1 NA + 96 1.450844 1 NA 0.9696445 0.213084904 1 NA + 97 1.433371 NA 3 0.7087457 -0.161914659 1 NA + 98 1.444378 1 3 NA -0.034767685 1 NA + 99 1.422523 0 5 0.9084899 -0.320681689 1 NA + 100 1.410394 NA 2 0.9296776 0.058192962 1 NA + + $m3c$spM_lvlone + center scale + C1 1.43410054 0.01299651 + B2 NA NA + P2 2.17500000 1.68969325 + L1mis 0.72626070 0.15364470 + C2 -0.06490582 0.33317347 + (Intercept) NA NA + B21 NA NA + + $m3c$mu_reg_norm + [1] 0 + + $m3c$tau_reg_norm + [1] 1e-04 + + $m3c$shape_tau_norm + [1] 0.01 + + $m3c$rate_tau_norm + [1] 0.01 + + $m3c$mu_reg_gamma + [1] 0 + + $m3c$tau_reg_gamma + [1] 1e-04 + + $m3c$shape_tau_gamma + [1] 0.01 + + $m3c$rate_tau_gamma + [1] 0.01 + + $m3c$mu_reg_binom + [1] 0 + + $m3c$tau_reg_binom + [1] 1e-04 + + $m3c$mu_reg_poisson + [1] 0 + + $m3c$tau_reg_poisson + [1] 1e-04 + + + $m3d + $m3d$M_lvlone + C1 B2 P2 L1mis Be2 C2 (Intercept) B21 + 1 1.410531 1 0 0.9364352 0.70995633 0.144065882 1 NA + 2 1.434183 1 2 0.8943541 0.65930815 0.032778478 1 NA + 3 1.430994 1 1 0.2868460 NA 0.343008492 1 NA + 4 1.453096 1 1 NA 0.76377664 -0.361887858 1 NA + 5 1.438344 1 0 0.7621346 0.57143534 -0.389600647 1 NA + 6 1.453207 NA 1 0.5858621 NA -0.205306841 1 NA + 7 1.425176 1 1 0.7194403 0.94878453 0.079434830 1 NA + 8 1.437908 1 0 0.7593154 0.66316162 -0.331246757 1 NA + 9 1.416911 1 2 0.5863705 0.33529773 -0.329638800 1 NA + 10 1.448638 NA 0 NA 0.54648836 0.167597533 1 NA + 11 1.428375 1 3 0.7218028 0.41960544 0.860207989 1 NA + 12 1.450130 1 0 0.7241254 0.43175258 0.022730640 1 NA + 13 1.420545 1 5 NA 0.28221450 0.217171172 1 NA + 14 1.423005 1 0 0.5289014 0.52917815 -0.403002412 1 NA + 15 1.435902 1 1 0.7322482 NA 0.087369742 1 NA + 16 1.423901 1 4 0.7462471 0.25397143 -0.183870429 1 NA + 17 1.457208 1 NA 0.9119922 0.03793703 -0.194577002 1 NA + 18 1.414280 1 1 0.6262513 0.43662405 -0.349718516 1 NA + 19 1.443383 NA NA NA NA -0.508781244 1 NA + 20 1.434954 NA 3 0.7173364 0.64434515 0.494883111 1 NA + 21 1.429499 1 3 0.7288999 0.51477949 0.258041067 1 NA + 22 1.441897 NA 4 0.7160420 NA -0.922621989 1 NA + 23 1.423713 NA 6 0.5795514 0.25015755 0.431254949 1 NA + 24 1.435395 1 4 0.7210413 0.67066080 -0.294218881 1 NA + 25 1.425944 NA NA 0.7816086 0.67236226 -0.425548895 1 NA + 26 1.437115 NA 1 NA 0.56258284 0.057176054 1 NA + 27 1.441326 1 1 0.4746725 0.79531637 0.289090158 1 NA + 28 1.422953 1 2 0.9270652 0.59976078 -0.473079489 1 NA + 29 1.437797 1 NA 0.5306249 0.56570882 -0.385664863 1 NA + 30 1.472121 1 1 0.8913764 NA -0.154780107 1 NA + 31 1.421782 NA 5 0.8090308 0.60752741 0.100536296 1 NA + 32 1.457672 1 NA NA 0.74271422 0.634791958 1 NA + 33 1.430842 1 0 NA 0.61115989 -0.387252617 1 NA + 34 1.431523 0 2 0.6375974 NA -0.181741088 1 NA + 35 1.421395 1 4 0.9202563 0.24298680 -0.311562695 1 NA + 36 1.434496 1 2 0.7263222 0.40842081 -0.044115907 1 NA + 37 1.425383 1 4 1.0638781 0.81411442 -0.657409991 1 NA + 38 1.421802 NA NA 0.6053893 0.22725019 0.159577214 1 NA + 39 1.430094 1 2 0.7945509 0.56416140 -0.460416933 1 NA + 40 1.447621 NA NA 0.6355032 NA NA 1 NA + 41 1.434797 1 2 NA 0.72627471 -0.248909867 1 NA + 42 1.446091 1 6 1.0690739 0.41365484 -0.609021545 1 NA + 43 1.445306 1 1 NA 0.53957342 0.025471883 1 NA + 44 1.448783 1 2 0.7595403 NA 0.066648592 1 NA + 45 1.450617 1 1 NA 0.58384945 -0.276108719 1 NA + 46 1.415055 1 2 0.4929132 0.33926641 -0.179737577 1 NA + 47 1.436590 0 3 NA 0.44061990 0.181190937 1 NA + 48 1.433938 1 2 NA NA -0.453871693 1 NA + 49 1.414941 0 NA NA 0.59506289 0.448629602 1 NA + 50 1.421807 1 2 0.6292812 NA -0.529811821 1 NA + 51 1.453203 1 NA NA 0.42442997 -0.028304571 1 NA + 52 1.452129 1 1 0.9735411 0.86116633 -0.520318482 1 NA + 53 1.431510 1 NA 0.7156259 0.66162840 0.171317619 1 NA + 54 1.430082 1 NA 0.5184434 0.72084009 0.432732046 1 NA + 55 1.443492 1 1 0.7948965 0.88588728 -0.346286005 1 NA + 56 1.436460 1 6 0.5191792 NA -0.469375653 1 NA + 57 1.418119 1 2 0.9233108 0.69181520 0.031021711 1 NA + 58 1.434971 NA 7 0.8025356 0.72841200 -0.118837515 1 NA + 59 1.445599 1 NA 0.8546624 0.49845731 0.507769984 1 NA + 60 1.437097 NA 2 0.8639819 0.57909329 0.271797031 1 NA + 61 1.428360 1 2 0.7521237 0.26662345 -0.124442204 1 NA + 62 1.440550 1 2 0.5590215 NA 0.277677389 1 NA + 63 1.443014 1 2 NA 0.33159713 -0.102893730 1 NA + 64 1.424298 1 1 0.6071272 0.69289374 NA 1 NA + 65 1.448823 1 0 0.8837829 0.30758569 -0.678303052 1 NA + 66 1.425834 0 NA 0.7775301 0.57917741 0.478880037 1 NA + 67 1.427102 1 NA 0.6756191 0.30016282 -0.428028760 1 NA + 68 1.414240 1 0 0.7857549 0.43886660 0.048119185 1 NA + 69 1.456218 NA NA 0.9119262 0.17467372 0.216932805 1 NA + 70 1.470594 1 NA 0.5816103 0.53206444 -0.234575269 1 NA + 71 1.425058 1 3 NA 0.72818697 0.006827078 1 NA + 72 1.432371 1 2 NA 0.54568411 -0.456055171 1 NA + 73 1.441656 1 3 0.6767456 0.14601189 0.346486708 1 NA + 74 1.434952 1 2 0.7328840 NA 0.205092215 1 NA + 75 1.402860 1 NA 0.7946099 0.83970611 -0.136596858 1 NA + 76 1.453363 1 3 NA NA -0.500179043 1 NA + 77 1.432909 1 2 0.5296147 0.23594577 0.527352086 1 NA + 78 1.435103 1 2 0.7723288 0.40933787 0.022742250 1 NA + 79 1.434462 1 0 0.8079308 0.58749646 NA 1 NA + 80 1.434661 1 NA 0.5214822 0.57911860 -0.002032440 1 NA + 81 1.445881 0 2 NA 0.35894249 -0.154246160 1 NA + 82 1.442548 NA 1 0.8332107 0.27822593 0.140201825 1 NA + 83 1.430097 1 2 0.4544158 NA -0.141417121 1 NA + 84 1.430119 1 0 0.6482660 0.73298261 NA 1 NA + 85 1.430315 1 5 0.7272109 0.50590638 -0.021285339 1 NA + 86 1.437584 NA 0 0.7302426 0.34320856 -0.010196306 1 NA + 87 1.409738 NA 3 0.6768061 0.37651326 -0.089747520 1 NA + 88 1.422388 1 2 0.8115758 NA -0.083699898 1 NA + 89 1.422509 1 1 0.9775567 0.39008384 -0.044061996 1 NA + 90 1.439432 1 NA 0.6408465 NA -0.209291697 1 NA + 91 1.430175 1 2 0.5917453 NA 0.639036426 1 NA + 92 1.418002 NA 6 0.7224845 0.60913196 0.094698299 1 NA + 93 1.423812 1 0 0.4501596 0.39610480 -0.055510622 1 NA + 94 1.423473 1 4 0.5190455 0.49000093 -0.421318463 1 NA + 95 1.434412 1 3 0.7305821 0.37935661 0.125295503 1 NA + 96 1.450844 1 NA 0.9696445 0.77341877 0.213084904 1 NA + 97 1.433371 NA 3 0.7087457 NA -0.161914659 1 NA + 98 1.444378 1 3 NA 0.82491133 -0.034767685 1 NA + 99 1.422523 0 5 0.9084899 0.36317443 -0.320681689 1 NA + 100 1.410394 NA 2 0.9296776 0.34363945 0.058192962 1 NA + + $m3d$spM_lvlone + center scale + C1 1.43410054 0.01299651 + B2 NA NA + P2 2.17500000 1.68969325 + L1mis 0.72626070 0.15364470 + Be2 0.52042833 0.19439164 + C2 -0.06490582 0.33317347 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29 -1.11821124 -0.385664863 4 3 1 NA NA NA NA NA NA + 30 -2.82834175 -0.154780107 2 3 1 NA NA NA NA NA NA + 31 -3.72259860 0.100536296 NA 2 1 NA NA NA NA NA NA + 32 -1.75256656 0.634791958 4 2 1 NA NA NA NA NA NA + 33 -5.55044409 -0.387252617 4 1 1 NA NA NA NA NA NA + 34 -7.45068147 -0.181741088 4 1 1 NA NA NA NA NA NA + 35 -0.97491919 -0.311562695 2 4 1 NA NA NA NA NA NA + 36 -2.98356481 -0.044115907 1 3 1 NA NA NA NA NA NA + 37 -1.86039471 -0.657409991 3 3 1 NA NA NA NA NA NA + 38 -7.28754607 0.159577214 4 1 1 NA NA NA NA NA NA + 39 -8.66234796 -0.460416933 3 2 1 NA NA NA NA NA NA + 40 -4.16291375 NA 3 3 1 NA NA NA NA NA NA + 41 -3.48250771 -0.248909867 1 3 1 NA NA NA NA NA NA + 42 -7.27930410 -0.609021545 4 3 1 NA NA NA NA NA NA + 43 -6.12866190 0.025471883 1 3 1 NA NA NA NA NA NA + 44 -4.96880803 0.066648592 2 4 1 NA NA NA NA NA NA + 45 -4.76746713 -0.276108719 2 4 1 NA NA NA NA NA NA + 46 -1.91249177 -0.179737577 1 1 1 NA NA NA NA NA NA + 47 -0.61884029 0.181190937 4 4 1 NA NA NA 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NA 0.3688639 NA NA NA + 43 NA 0.3683210 NA NA NA + 44 NA 0.3707242 NA NA NA + 45 NA 0.3719890 NA NA NA + 46 NA 0.3471687 NA NA NA + 47 NA 0.3622725 NA NA NA + 48 NA 0.3604242 NA NA NA + 49 NA 0.3470878 NA NA NA + 50 NA 0.3519288 NA NA NA + 51 NA 0.3737703 NA NA NA + 52 NA 0.3730309 NA NA NA + 53 NA 0.3587298 NA NA NA + 54 NA 0.3577317 NA NA NA + 55 NA 0.3670651 NA NA NA + 56 NA 0.3621821 NA NA NA + 57 NA 0.3493310 NA NA NA + 58 NA 0.3611449 NA NA NA + 59 NA 0.3685236 NA NA NA + 60 NA 0.3626252 NA NA NA + 61 NA 0.3565271 NA NA NA + 62 NA 0.3650248 NA NA NA + 63 NA 0.3667342 NA NA NA + 64 NA 0.3536790 NA NA NA + 65 NA 0.3707512 NA NA NA + 66 NA 0.3547570 NA NA NA + 67 NA 0.3556460 NA NA NA + 68 NA 0.3465922 NA NA NA + 69 NA 0.3758430 NA NA NA + 70 NA 0.3856661 NA NA NA + 71 NA 0.3542125 NA NA NA + 72 NA 0.3593309 NA NA NA + 73 NA 0.3657925 NA NA NA + 74 NA 0.3611311 NA NA NA + 75 NA 0.3385130 NA NA NA + 76 NA 0.3738804 NA NA NA + 77 NA 0.3597065 NA NA NA + 78 NA 0.3612366 NA NA NA + 79 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1.472121 + 31 1.421782 + 32 1.457672 + 33 1.430842 + 34 1.431523 + 35 1.421395 + 36 1.434496 + 37 1.425383 + 38 1.421802 + 39 1.430094 + 40 1.447621 + 41 1.434797 + 42 1.446091 + 43 1.445306 + 44 1.448783 + 45 1.450617 + 46 1.415055 + 47 1.436590 + 48 1.433938 + 49 1.414941 + 50 1.421807 + 51 1.453203 + 52 1.452129 + 53 1.431510 + 54 1.430082 + 55 1.443492 + 56 1.436460 + 57 1.418119 + 58 1.434971 + 59 1.445599 + 60 1.437097 + 61 1.428360 + 62 1.440550 + 63 1.443014 + 64 1.424298 + 65 1.448823 + 66 1.425834 + 67 1.427102 + 68 1.414240 + 69 1.456218 + 70 1.470594 + 71 1.425058 + 72 1.432371 + 73 1.441656 + 74 1.434952 + 75 1.402860 + 76 1.453363 + 77 1.432909 + 78 1.435103 + 79 1.434462 + 80 1.434661 + 81 1.445881 + 82 1.442548 + 83 1.430097 + 84 1.430119 + 85 1.430315 + 86 1.437584 + 87 1.409738 + 88 1.422388 + 89 1.422509 + 90 1.439432 + 91 1.430175 + 92 1.418002 + 93 1.423812 + 94 1.423473 + 95 1.434412 + 96 1.450844 + 97 1.433371 + 98 1.444378 + 99 1.422523 + 100 1.410394 + + $m4a$spM_lvlone + center scale + y -3.34428345 2.276495066 + C2 -0.06490582 0.333173465 + M2 NA NA + O2 NA NA + (Intercept) NA NA + M22 NA NA + M23 NA NA + M24 NA NA + O22 NA NA + O23 NA NA + O24 NA NA + abs(C1 - C2) 1.49900534 0.334214181 + log(C1) 0.36049727 0.009050336 + O22:abs(C1 - C2) 0.31342466 0.618807150 + O23:abs(C1 - C2) 0.47068368 0.762352624 + O24:abs(C1 - C2) 0.40568706 0.692690317 + C1 1.43410054 0.012996511 + + $m4a$mu_reg_norm + [1] 0 + + $m4a$tau_reg_norm + [1] 1e-04 + + $m4a$shape_tau_norm + [1] 0.01 + + $m4a$rate_tau_norm + [1] 0.01 + + $m4a$mu_reg_multinomial + [1] 0 + + $m4a$tau_reg_multinomial + [1] 1e-04 + + $m4a$mu_reg_ordinal + [1] 0 + + $m4a$tau_reg_ordinal + [1] 1e-04 + + $m4a$mu_delta_ordinal + [1] 0 + + $m4a$tau_delta_ordinal + [1] 1e-04 + + + $m4b + $m4b$M_lvlone + B1 L1mis Be2 C2 (Intercept) abs(C1 - C2) log(Be2) + 1 1 0.9364352 0.70995633 0.144065882 1 NA NA + 2 1 0.8943541 0.65930815 0.032778478 1 NA NA + 3 1 0.2868460 NA 0.343008492 1 NA NA + 4 1 NA 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0.7 0.88 1 20 0 NA NA + 2222 140.66667 0.6 0.66 1 75 1 NA NA + 2231 104.00000 0.8 0.83 1 32 0 NA NA + 2248 107.33333 0.5 0.82 1 29 1 NA NA + 2260 142.00000 0.5 0.76 1 45 1 NA NA + 2265 93.33333 0.6 0.56 1 40 1 NA NA + 2268 110.00000 0.8 0.82 1 61 1 NA NA + 2306 106.66667 0.9 0.95 1 32 0 NA NA + 2313 138.00000 0.6 0.86 1 48 1 NA NA + 2333 126.00000 0.7 1.06 1 70 0 NA NA + 2337 124.00000 0.4 0.50 1 43 1 NA NA + 2351 136.00000 0.6 1.03 1 33 0 NA NA + 2375 98.66667 1.0 0.82 1 34 0 NA NA + 2378 134.66667 0.6 0.77 1 25 0 NA NA + 2385 101.33333 0.5 0.74 1 48 1 NA NA + 2401 114.66667 0.7 0.84 1 69 1 NA NA + 2417 122.66667 0.7 0.68 1 68 1 NA NA + 2428 140.66667 0.6 0.74 1 65 0 NA NA + 2431 115.33333 0.6 0.69 1 22 1 NA NA + 2440 116.66667 0.4 0.65 1 44 1 NA NA + 2446 132.00000 0.5 0.73 1 30 0 NA NA + 2453 127.33333 0.7 0.80 1 60 0 NA NA + 2460 94.66667 0.5 0.65 1 22 1 NA NA + 2475 116.00000 0.8 0.92 1 39 0 NA NA + 2491 102.66667 0.7 0.64 1 43 1 NA NA + 2493 114.00000 0.5 0.83 1 46 1 NA NA + 2519 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2.3601663 0.4232889 + + $m6d$mu_reg_norm + [1] 0 + + $m6d$tau_reg_norm + [1] 1e-04 + + $m6d$shape_tau_norm + [1] 0.01 + + $m6d$rate_tau_norm + [1] 0.01 + + + $m6e + $m6e$M_lvlone + SBP bili creat (Intercept) age genderfemale log(bili) exp(creat) + 10 108.00000 0.9 1.10 1 35 0 NA NA + 14 105.33333 1.0 0.77 1 38 0 NA NA + 41 110.00000 0.9 1.14 1 78 1 NA NA + 77 106.00000 0.7 0.99 1 23 0 NA NA + 91 114.66667 0.6 0.90 1 40 0 NA NA + 105 139.33333 1.2 0.88 1 54 0 NA NA + 114 124.00000 0.3 0.68 1 31 1 NA NA + 135 100.00000 0.5 0.66 1 27 1 NA NA + 149 114.66667 0.4 1.05 1 37 0 NA NA + 154 156.66667 NA NA 1 50 1 NA NA + 155 127.33333 0.8 0.98 1 63 0 NA NA + 176 106.66667 0.6 0.67 1 26 1 NA NA + 215 114.00000 0.9 0.74 1 35 1 NA NA + 220 126.00000 NA NA 1 44 0 NA NA + 224 86.00000 1.0 0.76 1 34 1 NA NA + 226 117.33333 0.6 0.93 1 60 0 NA NA + 264 128.00000 0.6 0.79 1 24 0 NA NA + 282 113.33333 NA NA 1 48 0 NA NA + 286 117.33333 0.4 0.57 1 68 1 NA NA + 300 115.33333 0.7 0.83 1 37 0 NA NA + 301 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116.66667 0.7 0.72 1 45 1 NA NA + 1997 123.33333 0.5 1.01 1 56 0 NA NA + 2005 122.00000 0.6 0.93 1 78 1 NA NA + 2032 126.66667 0.7 0.77 1 20 0 NA NA + 2034 116.00000 0.6 0.98 1 25 0 NA NA + 2036 122.00000 0.4 0.67 1 52 1 NA NA + 2054 111.33333 0.7 0.64 1 43 1 NA NA + 2086 124.66667 0.3 0.56 1 47 1 NA NA + 2122 141.33333 0.7 0.68 1 71 1 NA NA + 2124 115.33333 0.5 0.96 1 27 0 NA NA + 2133 134.66667 0.5 1.38 1 60 0 NA NA + 2163 128.66667 0.5 0.64 1 53 1 NA NA + 2174 148.66667 0.6 0.85 1 55 1 NA NA + 2175 125.33333 1.0 0.72 1 64 0 NA NA + 2195 109.33333 1.3 0.85 1 42 1 NA NA + 2197 94.00000 0.7 0.87 1 22 0 NA NA + 2202 118.66667 0.7 0.88 1 20 0 NA NA + 2222 140.66667 0.6 0.66 1 75 1 NA NA + 2231 104.00000 0.8 0.83 1 32 0 NA NA + 2248 107.33333 0.5 0.82 1 29 1 NA NA + 2260 142.00000 0.5 0.76 1 45 1 NA NA + 2265 93.33333 0.6 0.56 1 40 1 NA NA + 2268 110.00000 0.8 0.82 1 61 1 NA NA + 2306 106.66667 0.9 0.95 1 32 0 NA NA + 2313 138.00000 0.6 0.86 1 48 1 NA NA + 2333 126.00000 0.7 1.06 1 70 0 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98.66667 1.0 0.75 1 31 0 NA NA + 862 108.66667 1.1 0.93 1 43 0 NA NA + 866 108.00000 0.5 0.69 1 35 1 NA NA + 867 109.33333 0.7 0.80 1 23 1 NA NA + 887 160.66667 0.7 0.64 1 54 1 NA NA + 894 138.66667 0.7 0.61 1 45 1 NA NA + 913 99.33333 0.9 0.72 1 27 1 NA NA + 974 114.00000 0.6 0.58 1 23 0 NA NA + 976 137.33333 0.8 1.07 1 57 0 NA NA + 980 117.33333 0.5 0.69 1 41 1 NA NA + 1028 118.66667 0.8 0.74 1 30 0 NA NA + 1039 124.66667 1.0 1.07 1 58 0 NA NA + 1040 112.00000 0.7 0.97 1 29 1 NA NA + 1046 110.66667 1.0 0.62 1 43 1 NA NA + 1055 112.00000 1.0 0.69 1 35 0 NA NA + 1092 114.66667 0.5 0.68 1 37 1 NA NA + 1108 108.66667 0.7 1.03 1 21 0 NA NA + 1150 141.33333 1.1 1.15 1 71 0 NA NA + 1153 122.00000 0.4 0.94 1 26 0 NA NA + 1165 98.00000 1.1 0.92 1 45 0 NA NA + 1174 116.66667 0.7 0.84 1 63 1 NA NA + 1212 124.66667 0.5 1.35 1 61 0 NA NA + 1231 134.66667 0.9 1.10 1 56 0 NA NA + 1245 130.00000 NA NA 1 66 1 NA NA + 1247 108.66667 0.6 0.82 1 52 1 NA NA + 1273 126.66667 0.4 1.00 1 42 0 NA NA + 1278 103.33333 0.6 0.69 1 29 0 NA NA + 1299 112.00000 0.7 1.10 1 39 0 NA NA + 1346 99.33333 0.5 0.77 1 23 1 NA NA + 1352 102.00000 0.4 1.04 1 46 1 NA NA + 1360 103.00000 0.5 1.02 1 42 0 NA NA + 1397 106.66667 0.7 0.66 1 31 1 NA NA + 1399 106.66667 0.5 1.15 1 33 1 NA NA + 1410 167.33333 0.6 0.72 1 70 1 NA NA + 1439 130.00000 1.1 0.69 1 44 0 NA NA + 1481 93.33333 0.7 0.77 1 58 1 NA NA + 1494 120.66667 0.7 1.05 1 70 0 NA NA + 1499 130.00000 0.8 1.29 1 38 0 NA NA + 1509 111.33333 1.1 0.88 1 73 0 NA NA + 1512 127.33333 0.4 0.77 1 47 1 NA NA + 1520 120.00000 0.8 0.71 1 56 1 NA NA + 1560 144.00000 0.8 1.08 1 32 0 NA NA + 1602 118.00000 0.5 1.15 1 28 0 NA NA + 1608 140.66667 0.7 0.89 1 58 0 NA NA + 1619 122.00000 0.8 0.90 1 34 0 NA NA + 1642 128.66667 0.8 1.18 1 30 0 NA NA + 1648 100.00000 1.0 0.73 1 33 1 NA NA + 1663 124.00000 0.4 0.96 1 51 0 NA NA + 1671 140.66667 0.5 0.86 1 74 1 NA NA + 1691 122.00000 1.0 1.12 1 56 0 NA NA + 1701 119.33333 0.7 0.77 1 56 1 NA NA + 1726 154.66667 0.6 1.12 1 31 0 NA NA + 1733 106.66667 0.5 0.93 1 38 1 NA NA + 1743 114.66667 0.5 1.13 1 74 0 NA NA + 1753 118.66667 1.4 0.85 1 42 1 NA NA + 1761 112.66667 0.6 0.68 1 47 1 NA NA + 1765 125.33333 0.6 0.99 1 49 0 NA NA + 1766 114.00000 1.2 0.98 1 61 0 NA NA + 1795 177.33333 0.8 0.63 1 65 1 NA NA + 1804 122.66667 0.8 1.01 1 43 0 NA NA + 1809 116.00000 0.7 0.79 1 26 0 NA NA + 1813 96.66667 NA NA 1 36 0 NA NA + 1858 97.33333 1.1 0.83 1 43 1 NA NA + 1878 122.00000 0.6 0.96 1 51 0 NA NA + 1889 128.00000 0.7 0.98 1 34 0 NA NA + 1933 104.66667 1.2 0.52 1 77 1 NA NA + 1940 110.66667 0.7 0.83 1 48 1 NA NA + 1988 136.00000 0.7 0.64 1 62 1 NA NA + 1993 116.66667 0.7 0.72 1 45 1 NA NA + 1997 123.33333 0.5 1.01 1 56 0 NA NA + 2005 122.00000 0.6 0.93 1 78 1 NA NA + 2032 126.66667 0.7 0.77 1 20 0 NA NA + 2034 116.00000 0.6 0.98 1 25 0 NA NA + 2036 122.00000 0.4 0.67 1 52 1 NA NA + 2054 111.33333 0.7 0.64 1 43 1 NA NA + 2086 124.66667 0.3 0.56 1 47 1 NA NA + 2122 141.33333 0.7 0.68 1 71 1 NA NA + 2124 115.33333 0.5 0.96 1 27 0 NA NA + 2133 134.66667 0.5 1.38 1 60 0 NA NA + 2163 128.66667 0.5 0.64 1 53 1 NA NA + 2174 148.66667 0.6 0.85 1 55 1 NA NA + 2175 125.33333 1.0 0.72 1 64 0 NA NA + 2195 109.33333 1.3 0.85 1 42 1 NA NA + 2197 94.00000 0.7 0.87 1 22 0 NA NA + 2202 118.66667 0.7 0.88 1 20 0 NA NA + 2222 140.66667 0.6 0.66 1 75 1 NA NA + 2231 104.00000 0.8 0.83 1 32 0 NA NA + 2248 107.33333 0.5 0.82 1 29 1 NA NA + 2260 142.00000 0.5 0.76 1 45 1 NA NA + 2265 93.33333 0.6 0.56 1 40 1 NA NA + 2268 110.00000 0.8 0.82 1 61 1 NA NA + 2306 106.66667 0.9 0.95 1 32 0 NA NA + 2313 138.00000 0.6 0.86 1 48 1 NA NA + 2333 126.00000 0.7 1.06 1 70 0 NA NA + 2337 124.00000 0.4 0.50 1 43 1 NA NA + 2351 136.00000 0.6 1.03 1 33 0 NA NA + 2375 98.66667 1.0 0.82 1 34 0 NA NA + 2378 134.66667 0.6 0.77 1 25 0 NA NA + 2385 101.33333 0.5 0.74 1 48 1 NA NA + 2401 114.66667 0.7 0.84 1 69 1 NA NA + 2417 122.66667 0.7 0.68 1 68 1 NA NA + 2428 140.66667 0.6 0.74 1 65 0 NA NA + 2431 115.33333 0.6 0.69 1 22 1 NA NA + 2440 116.66667 0.4 0.65 1 44 1 NA NA + 2446 132.00000 0.5 0.73 1 30 0 NA NA + 2453 127.33333 0.7 0.80 1 60 0 NA NA + 2460 94.66667 0.5 0.65 1 22 1 NA NA + 2475 116.00000 0.8 0.92 1 39 0 NA NA + 2491 102.66667 0.7 0.64 1 43 1 NA NA + 2493 114.00000 0.5 0.83 1 46 1 NA NA + 2519 116.00000 0.8 0.73 1 38 0 NA NA + 2549 115.33333 0.8 0.85 1 36 0 NA NA + 2551 111.33333 0.8 0.58 1 68 1 NA NA + 2552 86.00000 0.6 0.69 1 36 1 NA NA + 2554 112.66667 0.9 0.89 1 21 0 NA NA + 2562 93.33333 NA NA 1 62 0 NA NA + 2590 98.66667 1.1 0.84 1 23 1 NA NA + 2615 125.33333 1.2 0.91 1 22 0 NA NA + 2618 145.33333 1.1 0.82 1 37 0 NA NA + 2631 106.00000 1.1 0.65 1 37 1 NA NA + 2648 116.66667 0.8 1.12 1 43 0 NA NA + 2661 141.33333 0.5 0.94 1 35 0 NA NA + 2672 126.66667 0.9 0.84 1 29 0 NA NA + 2676 111.33333 NA NA 1 41 0 NA NA + 2681 102.66667 0.9 0.79 1 21 1 NA NA + 2718 111.33333 0.7 0.95 1 20 0 NA NA + 2733 142.66667 0.6 0.80 1 53 1 NA NA + 2752 98.66667 1.0 1.01 1 24 0 NA NA + 2763 124.00000 0.8 0.94 1 28 0 NA NA + 2764 129.33333 1.0 1.08 1 27 0 NA NA + + $m6f$spM_lvlone + center scale + SBP 119.2956989 15.3559299 + bili 0.7207865 0.2266570 + creat 0.8437640 0.1711968 + (Intercept) NA NA + age 43.5107527 15.0631963 + genderfemale NA NA + log(bili) -0.3758477 0.3135642 + exp(creat) 2.3601663 0.4232889 + + $m6f$mu_reg_norm + [1] 0 + + $m6f$tau_reg_norm + [1] 1e-04 + + $m6f$shape_tau_norm + [1] 0.01 + + $m6f$rate_tau_norm + [1] 0.01 + + $m6f$mu_reg_gamma + [1] 0 + + $m6f$tau_reg_gamma + [1] 1e-04 + + $m6f$shape_tau_gamma + [1] 0.01 + + $m6f$rate_tau_gamma + [1] 0.01 + + + $mod7a + $mod7a$M_lvlone + SBP bili (Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale + 10 108.00000 0.9 1 0.40123555 -0.219432742 0 + 14 105.33333 1.0 1 0.46083535 -0.235722523 0 + 41 110.00000 0.9 1 0.31290629 0.806930162 1 + 77 106.00000 0.7 1 0.08775523 -0.053921737 0 + 91 114.66667 0.6 1 0.49442764 -0.238374584 0 + 105 139.33333 1.2 1 0.57496216 -0.050288463 0 + 114 124.00000 0.3 1 0.30749447 -0.178633788 1 + 135 100.00000 0.5 1 0.20151795 -0.121481193 1 + 149 114.66667 0.4 1 0.44212741 -0.231841236 0 + 154 156.66667 NA 1 0.57740015 -0.138040409 1 + 155 127.33333 0.8 1 0.51397109 0.221341024 0 + 176 106.66667 0.6 1 0.17363342 -0.105334677 1 + 215 114.00000 0.9 1 0.40123555 -0.219432742 1 + 220 126.00000 NA 1 0.54452072 -0.220834542 0 + 224 86.00000 1.0 1 0.37919068 -0.211091372 1 + 226 117.33333 0.6 1 0.54156624 0.121087888 0 + 264 128.00000 0.6 1 0.11668254 -0.071462030 0 + 282 113.33333 NA 1 0.57165173 -0.172603893 0 + 286 117.33333 0.4 1 0.45580036 0.404707513 1 + 300 115.33333 0.7 1 0.44212741 -0.231841236 0 + 301 126.66667 0.6 1 0.40123555 -0.219432742 0 + 311 110.00000 0.6 1 0.54929184 0.089638669 0 + 317 124.66667 0.6 1 0.00000000 0.000000000 1 + 337 111.33333 0.9 1 0.41545995 0.521995691 1 + 383 153.33333 NA 1 0.57720730 -0.074412613 0 + 391 115.33333 0.8 1 0.08775523 -0.053921737 0 + 392 126.66667 1.0 1 0.33225062 -0.190598976 0 + 420 98.00000 0.8 1 0.42223764 -0.226380337 1 + 422 166.66667 0.6 1 0.57165173 -0.172603893 0 + 461 124.66667 1.0 1 0.56745550 0.001991464 0 + 475 112.66667 0.5 1 0.49442764 -0.238374584 1 + 483 106.66667 0.6 1 0.20151795 -0.121481193 0 + 501 112.66667 0.8 1 0.08775523 -0.053921737 0 + 533 110.66667 0.8 1 0.54452072 -0.220834542 1 + 538 127.33333 0.4 1 0.52386338 0.186995848 1 + 550 134.00000 1.2 1 0.54929184 0.089638669 1 + 557 135.33333 0.7 1 0.57496216 -0.050288463 1 + 589 128.66667 0.7 1 0.54452072 -0.220834542 0 + 598 118.66667 0.6 1 0.52386338 0.186995848 1 + 621 120.66667 0.5 1 0.30749447 -0.178633788 0 + 631 116.66667 0.7 1 0.53307593 0.153559208 1 + 637 118.66667 0.6 1 0.55336396 -0.211530878 1 + 650 111.33333 0.5 1 0.50917296 -0.236959521 1 + 673 135.33333 0.4 1 0.57514196 -0.156145511 1 + 696 140.66667 1.1 1 0.46083535 -0.235722523 0 + 703 106.00000 0.7 1 0.42223764 -0.226380337 0 + 704 124.66667 0.4 1 0.55621024 0.059268337 0 + 726 112.66667 0.6 1 0.02934443 -0.018097803 1 + 739 107.33333 0.4 1 0.57740015 -0.138040409 0 + 747 105.33333 0.8 1 0.40123555 -0.219432742 1 + 755 115.33333 0.5 1 0.44212741 -0.231841236 1 + 756 123.33333 1.4 1 0.44212741 -0.231841236 0 + 766 117.33333 0.8 1 0.56688698 -0.187358770 0 + 777 124.00000 0.7 1 0.30749447 -0.178633788 0 + 793 109.33333 0.6 1 0.52245837 -0.233593171 1 + 818 127.33333 1.0 1 0.46083535 -0.235722523 0 + 850 98.66667 1.0 1 0.30749447 -0.178633788 0 + 862 108.66667 1.1 1 0.53423302 -0.228207567 0 + 866 108.00000 0.5 1 0.40123555 -0.219432742 1 + 867 109.33333 0.7 1 0.08775523 -0.053921737 1 + 887 160.66667 0.7 1 0.57496216 -0.050288463 1 + 894 138.66667 0.7 1 0.55336396 -0.211530878 1 + 913 99.33333 0.9 1 0.20151795 -0.121481193 1 + 974 114.00000 0.6 1 0.08775523 -0.053921737 0 + 976 137.33333 0.8 1 0.56227896 0.030033674 0 + 980 117.33333 0.5 1 0.50917296 -0.236959521 1 + 1028 118.66667 0.8 1 0.28197362 -0.165646497 0 + 1039 124.66667 1.0 1 0.55621024 0.059268337 0 + 1040 112.00000 0.7 1 0.25575756 -0.151730022 1 + 1046 110.66667 1.0 1 0.53423302 -0.228207567 1 + 1055 112.00000 1.0 1 0.40123555 -0.219432742 0 + 1092 114.66667 0.5 1 0.44212741 -0.231841236 1 + 1108 108.66667 0.7 1 0.02934443 -0.018097803 0 + 1150 141.33333 1.1 1 0.41545995 0.521995691 0 + 1153 122.00000 0.4 1 0.17363342 -0.105334677 0 + 1165 98.00000 1.1 1 0.55336396 -0.211530878 0 + 1174 116.66667 0.7 1 0.51397109 0.221341024 1 + 1212 124.66667 0.5 1 0.53307593 0.153559208 0 + 1231 134.66667 0.9 1 0.56745550 0.001991464 0 + 1245 130.00000 NA 1 0.48064057 0.329259929 1 + 1247 108.66667 0.6 1 0.57839033 -0.097117177 1 + 1273 126.66667 0.4 1 0.52245837 -0.233593171 0 + 1278 103.33333 0.6 1 0.25575756 -0.151730022 0 + 1299 112.00000 0.7 1 0.47829193 -0.237931278 0 + 1346 99.33333 0.5 1 0.08775523 -0.053921737 1 + 1352 102.00000 0.4 1 0.56080521 -0.200353360 1 + 1360 103.00000 0.5 1 0.52245837 -0.233593171 0 + 1397 106.66667 0.7 1 0.30749447 -0.178633788 1 + 1399 106.66667 0.5 1 0.35617252 -0.201449144 1 + 1410 167.33333 0.6 1 0.42926079 0.482426436 1 + 1439 130.00000 1.1 1 0.54452072 -0.220834542 0 + 1481 93.33333 0.7 1 0.55621024 0.059268337 1 + 1494 120.66667 0.7 1 0.42926079 0.482426436 0 + 1499 130.00000 0.8 1 0.46083535 -0.235722523 0 + 1509 111.33333 1.1 1 0.38700859 0.602269871 0 + 1512 127.33333 0.4 1 0.56688698 -0.187358770 1 + 1520 120.00000 0.8 1 0.56745550 0.001991464 1 + 1560 144.00000 0.8 1 0.33225062 -0.190598976 0 + 1602 118.00000 0.5 1 0.22891583 -0.136977281 0 + 1608 140.66667 0.7 1 0.55621024 0.059268337 0 + 1619 122.00000 0.8 1 0.37919068 -0.211091372 0 + 1642 128.66667 0.8 1 0.28197362 -0.165646497 0 + 1648 100.00000 1.0 1 0.35617252 -0.201449144 1 + 1663 124.00000 0.4 1 0.57846877 -0.118345370 0 + 1671 140.66667 0.5 1 0.37244303 0.642861228 1 + 1691 122.00000 1.0 1 0.56745550 0.001991464 0 + 1701 119.33333 0.7 1 0.56745550 0.001991464 1 + 1726 154.66667 0.6 1 0.30749447 -0.178633788 0 + 1733 106.66667 0.5 1 0.46083535 -0.235722523 1 + 1743 114.66667 0.5 1 0.37244303 0.642861228 0 + 1753 118.66667 1.4 1 0.52245837 -0.233593171 1 + 1761 112.66667 0.6 1 0.56688698 -0.187358770 1 + 1765 125.33333 0.6 1 0.57514196 -0.156145511 0 + 1766 114.00000 1.2 1 0.53307593 0.153559208 0 + 1795 177.33333 0.8 1 0.49231720 0.292529847 1 + 1804 122.66667 0.8 1 0.53423302 -0.228207567 0 + 1809 116.00000 0.7 1 0.17363342 -0.105334677 0 + 1813 96.66667 NA 1 0.42223764 -0.226380337 0 + 1858 97.33333 1.1 1 0.53423302 -0.228207567 1 + 1878 122.00000 0.6 1 0.57846877 -0.118345370 0 + 1889 128.00000 0.7 1 0.37919068 -0.211091372 0 + 1933 104.66667 1.2 1 0.32789669 0.765770970 1 + 1940 110.66667 0.7 1 0.57165173 -0.172603893 1 + 1988 136.00000 0.7 1 0.52386338 0.186995848 1 + 1993 116.66667 0.7 1 0.55336396 -0.211530878 1 + 1997 123.33333 0.5 1 0.56745550 0.001991464 0 + 2005 122.00000 0.6 1 0.31290629 0.806930162 1 + 2032 126.66667 0.7 1 0.00000000 0.000000000 0 + 2034 116.00000 0.6 1 0.14533178 -0.088630649 0 + 2036 122.00000 0.4 1 0.57839033 -0.097117177 1 + 2054 111.33333 0.7 1 0.53423302 -0.228207567 1 + 2086 124.66667 0.3 1 0.56688698 -0.187358770 1 + 2122 141.33333 0.7 1 0.41545995 0.521995691 1 + 2124 115.33333 0.5 1 0.20151795 -0.121481193 0 + 2133 134.66667 0.5 1 0.54156624 0.121087888 0 + 2163 128.66667 0.5 1 0.57720730 -0.074412613 1 + 2174 148.66667 0.6 1 0.57169740 -0.024801509 1 + 2175 125.33333 1.0 1 0.50344153 0.256537951 0 + 2195 109.33333 1.3 1 0.52245837 -0.233593171 1 + 2197 94.00000 0.7 1 0.05861935 -0.036102689 0 + 2202 118.66667 0.7 1 0.00000000 0.000000000 0 + 2222 140.66667 0.6 1 0.35770754 0.683679720 1 + 2231 104.00000 0.8 1 0.33225062 -0.190598976 0 + 2248 107.33333 0.5 1 0.25575756 -0.151730022 1 + 2260 142.00000 0.5 1 0.55336396 -0.211530878 1 + 2265 93.33333 0.6 1 0.49442764 -0.238374584 1 + 2268 110.00000 0.8 1 0.53307593 0.153559208 1 + 2306 106.66667 0.9 1 0.33225062 -0.190598976 0 + 2313 138.00000 0.6 1 0.57165173 -0.172603893 1 + 2333 126.00000 0.7 1 0.42926079 0.482426436 0 + 2337 124.00000 0.4 1 0.53423302 -0.228207567 1 + 2351 136.00000 0.6 1 0.35617252 -0.201449144 0 + 2375 98.66667 1.0 1 0.37919068 -0.211091372 0 + 2378 134.66667 0.6 1 0.14533178 -0.088630649 0 + 2385 101.33333 0.5 1 0.57165173 -0.172603893 1 + 2401 114.66667 0.7 1 0.44272175 0.443311449 1 + 2417 122.66667 0.7 1 0.45580036 0.404707513 1 + 2428 140.66667 0.6 1 0.49231720 0.292529847 0 + 2431 115.33333 0.6 1 0.05861935 -0.036102689 1 + 2440 116.66667 0.4 1 0.54452072 -0.220834542 1 + 2446 132.00000 0.5 1 0.28197362 -0.165646497 0 + 2453 127.33333 0.7 1 0.54156624 0.121087888 0 + 2460 94.66667 0.5 1 0.05861935 -0.036102689 1 + 2475 116.00000 0.8 1 0.47829193 -0.237931278 0 + 2491 102.66667 0.7 1 0.53423302 -0.228207567 1 + 2493 114.00000 0.5 1 0.56080521 -0.200353360 1 + 2519 116.00000 0.8 1 0.46083535 -0.235722523 0 + 2549 115.33333 0.8 1 0.42223764 -0.226380337 0 + 2551 111.33333 0.8 1 0.45580036 0.404707513 1 + 2552 86.00000 0.6 1 0.42223764 -0.226380337 1 + 2554 112.66667 0.9 1 0.02934443 -0.018097803 0 + 2562 93.33333 NA 1 0.52386338 0.186995848 0 + 2590 98.66667 1.1 1 0.08775523 -0.053921737 1 + 2615 125.33333 1.2 1 0.05861935 -0.036102689 0 + 2618 145.33333 1.1 1 0.44212741 -0.231841236 0 + 2631 106.00000 1.1 1 0.44212741 -0.231841236 1 + 2648 116.66667 0.8 1 0.53423302 -0.228207567 0 + 2661 141.33333 0.5 1 0.40123555 -0.219432742 0 + 2672 126.66667 0.9 1 0.25575756 -0.151730022 0 + 2676 111.33333 NA 1 0.50917296 -0.236959521 0 + 2681 102.66667 0.9 1 0.02934443 -0.018097803 1 + 2718 111.33333 0.7 1 0.00000000 0.000000000 0 + 2733 142.66667 0.6 1 0.57720730 -0.074412613 1 + 2752 98.66667 1.0 1 0.11668254 -0.071462030 0 + 2763 124.00000 0.8 1 0.22891583 -0.136977281 0 + 2764 129.33333 1.0 1 0.20151795 -0.121481193 0 + I(bili^2) I(bili^3) age + 10 NA NA 35 + 14 NA NA 38 + 41 NA NA 78 + 77 NA NA 23 + 91 NA NA 40 + 105 NA NA 54 + 114 NA NA 31 + 135 NA NA 27 + 149 NA NA 37 + 154 NA NA 50 + 155 NA NA 63 + 176 NA NA 26 + 215 NA NA 35 + 220 NA NA 44 + 224 NA NA 34 + 226 NA NA 60 + 264 NA NA 24 + 282 NA NA 48 + 286 NA NA 68 + 300 NA NA 37 + 301 NA NA 35 + 311 NA NA 59 + 317 NA NA 20 + 337 NA NA 71 + 383 NA NA 53 + 391 NA NA 23 + 392 NA NA 32 + 420 NA NA 36 + 422 NA NA 48 + 461 NA NA 56 + 475 NA NA 40 + 483 NA NA 27 + 501 NA NA 23 + 533 NA NA 44 + 538 NA NA 62 + 550 NA NA 59 + 557 NA NA 54 + 589 NA NA 44 + 598 NA NA 62 + 621 NA NA 31 + 631 NA NA 61 + 637 NA NA 45 + 650 NA NA 41 + 673 NA NA 49 + 696 NA NA 38 + 703 NA NA 36 + 704 NA NA 58 + 726 NA NA 21 + 739 NA NA 50 + 747 NA NA 35 + 755 NA NA 37 + 756 NA NA 37 + 766 NA NA 47 + 777 NA NA 31 + 793 NA NA 42 + 818 NA NA 38 + 850 NA NA 31 + 862 NA NA 43 + 866 NA NA 35 + 867 NA NA 23 + 887 NA NA 54 + 894 NA NA 45 + 913 NA NA 27 + 974 NA NA 23 + 976 NA NA 57 + 980 NA NA 41 + 1028 NA NA 30 + 1039 NA NA 58 + 1040 NA NA 29 + 1046 NA NA 43 + 1055 NA NA 35 + 1092 NA NA 37 + 1108 NA NA 21 + 1150 NA NA 71 + 1153 NA NA 26 + 1165 NA NA 45 + 1174 NA NA 63 + 1212 NA NA 61 + 1231 NA NA 56 + 1245 NA NA 66 + 1247 NA NA 52 + 1273 NA NA 42 + 1278 NA NA 29 + 1299 NA NA 39 + 1346 NA NA 23 + 1352 NA NA 46 + 1360 NA NA 42 + 1397 NA NA 31 + 1399 NA NA 33 + 1410 NA NA 70 + 1439 NA NA 44 + 1481 NA NA 58 + 1494 NA NA 70 + 1499 NA NA 38 + 1509 NA NA 73 + 1512 NA NA 47 + 1520 NA NA 56 + 1560 NA NA 32 + 1602 NA NA 28 + 1608 NA NA 58 + 1619 NA NA 34 + 1642 NA NA 30 + 1648 NA NA 33 + 1663 NA NA 51 + 1671 NA NA 74 + 1691 NA NA 56 + 1701 NA NA 56 + 1726 NA NA 31 + 1733 NA NA 38 + 1743 NA NA 74 + 1753 NA NA 42 + 1761 NA NA 47 + 1765 NA NA 49 + 1766 NA NA 61 + 1795 NA NA 65 + 1804 NA NA 43 + 1809 NA NA 26 + 1813 NA NA 36 + 1858 NA NA 43 + 1878 NA NA 51 + 1889 NA NA 34 + 1933 NA NA 77 + 1940 NA NA 48 + 1988 NA NA 62 + 1993 NA NA 45 + 1997 NA NA 56 + 2005 NA NA 78 + 2032 NA NA 20 + 2034 NA NA 25 + 2036 NA NA 52 + 2054 NA NA 43 + 2086 NA NA 47 + 2122 NA NA 71 + 2124 NA NA 27 + 2133 NA NA 60 + 2163 NA NA 53 + 2174 NA NA 55 + 2175 NA NA 64 + 2195 NA NA 42 + 2197 NA NA 22 + 2202 NA NA 20 + 2222 NA NA 75 + 2231 NA NA 32 + 2248 NA NA 29 + 2260 NA NA 45 + 2265 NA NA 40 + 2268 NA NA 61 + 2306 NA NA 32 + 2313 NA NA 48 + 2333 NA NA 70 + 2337 NA NA 43 + 2351 NA NA 33 + 2375 NA NA 34 + 2378 NA NA 25 + 2385 NA NA 48 + 2401 NA NA 69 + 2417 NA NA 68 + 2428 NA NA 65 + 2431 NA NA 22 + 2440 NA NA 44 + 2446 NA NA 30 + 2453 NA NA 60 + 2460 NA NA 22 + 2475 NA NA 39 + 2491 NA NA 43 + 2493 NA NA 46 + 2519 NA NA 38 + 2549 NA NA 36 + 2551 NA NA 68 + 2552 NA NA 36 + 2554 NA NA 21 + 2562 NA NA 62 + 2590 NA NA 23 + 2615 NA NA 22 + 2618 NA NA 37 + 2631 NA NA 37 + 2648 NA NA 43 + 2661 NA NA 35 + 2672 NA NA 29 + 2676 NA NA 41 + 2681 NA NA 21 + 2718 NA NA 20 + 2733 NA NA 53 + 2752 NA NA 24 + 2763 NA NA 28 + 2764 NA NA 27 + + $mod7a$spM_lvlone + center scale + SBP 119.29569892 15.3559299 + bili 0.72078652 0.2266570 + (Intercept) NA NA + ns(age, df = 2)1 0.40886544 0.1673890 + ns(age, df = 2)2 -0.04985511 0.2381012 + genderfemale NA NA + I(bili^2) 0.57061798 0.3661097 + I(bili^3) 0.49253371 0.4876694 + age 43.51075269 15.0631963 + + $mod7a$mu_reg_norm + [1] 0 + + $mod7a$tau_reg_norm + [1] 1e-04 + + $mod7a$shape_tau_norm + [1] 0.01 + + $mod7a$rate_tau_norm + [1] 0.01 + + + +# jagsmodel remains the same + + Code + lapply(models, "[[", "jagsmodel") + Output + $m0a1 + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } + $m0a2 + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } + $m0a3 + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + log(mu_y[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } + $m0a4 + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) + inv_mu_y[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } + $m0b1 + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for B1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m0b2 + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + probit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for B1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m0b3 + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + log(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for B1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m0b4 + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + cloglog(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for B1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m0c1 + model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + } + $m0c2 + model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + log(mu_L1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + } + $m0d1 + model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for P1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + } + $m0d2 + model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + mu_P1[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for P1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + } + $m0e1 + model { + + # Log-normal model for L1 ------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + } + $m0f1 + model { + + # Beta model for Be1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for Be1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + } + $m1a + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for y + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } + $m1b + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for B1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m1c + model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for L1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + } + $m1d + model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for P1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + } + $m1e + model { + + # Log-normal model for L1 ------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for L1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + } + $m1f + model { + + # Beta model for Be1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for Be1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + } + $m2a + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for y + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2b + model { + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for B2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2c + model { + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for L1mis + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2d + model { + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P2[i])) + log(mu_P2[i]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for P2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2e + model { + + # Log-normal model for L1mis ---------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for L1mis + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2f + model { + + # Beta model for Be2 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for Be2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3a + model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 7] * beta[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + + M_lvlone[i, 8] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6] + } + + # Priors for the model for C1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5] + + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) + log(mu_P2[i]) <- M_lvlone[i, 7] * alpha[6] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] + } + + # Priors for the model for P2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- M_lvlone[i, 7] * alpha[10] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12] + } + + # Priors for the model for L1mis + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Beta model for Be2 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- M_lvlone[i, 7] * alpha[13] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14] + } + + # Priors for the model for Be2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 7] * alpha[15] + } + + # Priors for the model for C2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3b + model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 6] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 7] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + } + + # Priors for the model for C1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + probit(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) + mu_P2[i] <- M_lvlone[i, 6] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] + } + + # Priors for the model for P2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Log-normal model for L1mis ---------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- M_lvlone[i, 6] * alpha[8] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] + } + + # Priors for the model for L1mis + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- 1/max(1e-10, inv_mu_C2[i]) + inv_mu_C2[i] <- M_lvlone[i, 6] * alpha[10] + } + + # Priors for the model for C2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3c + model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 6] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 7] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + } + + # Priors for the model for C1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + log(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) + mu_P2[i] <- M_lvlone[i, 6] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] + } + + # Priors for the model for P2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + log(mu_L1mis[i]) <- M_lvlone[i, 6] * alpha[8] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] + } + + # Priors for the model for L1mis + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2) + log(mu_C2[i]) <- M_lvlone[i, 6] * alpha[10] + } + + # Priors for the model for C2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3d + model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 7] * beta[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + + M_lvlone[i, 8] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6] + } + + # Priors for the model for C1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + log(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5] + + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) + mu_P2[i] <- M_lvlone[i, 7] * alpha[6] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] + } + + # Priors for the model for P2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + log(mu_L1mis[i]) <- M_lvlone[i, 7] * alpha[10] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12] + } + + # Priors for the model for L1mis + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for Be2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 5] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1) + mu_Be2[i] <- M_lvlone[i, 7] * alpha[13] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14] + } + + # Priors for the model for Be2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_Be2 <- sqrt(1/tau_Be2) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2) + log(mu_C2[i]) <- M_lvlone[i, 7] * alpha[15] + } + + # Priors for the model for C2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m4a + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] + } + + # Priors for the model for y + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } + $m4b + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 5] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + } + + # Priors for the model for B1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[2] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] + } + + # Priors for the model for L1mis + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Beta model for Be2 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- M_lvlone[i, 5] * alpha[5] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[6] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] + + M_lvlone[i, 7] <- log(M_lvlone[i, 3]) + + + } + + # Priors for the model for Be2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dnorm(mu_C2[i], tau_C2) + log(mu_C2[i]) <- M_lvlone[i, 5] * alpha[8] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[9] + + M_lvlone[i, 6] <- abs(M_lvlone[i, 8] - M_lvlone[i, 4]) + + + } + + # Priors for the model for C2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5a1 + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5a2 + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + log(mu_y[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5a3 + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) + inv_mu_y[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5b1 + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for B1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + + M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5b2 + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + probit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for B1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + + M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5b3 + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + log(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for B1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + + M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5b4 + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + cloglog(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for B1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + + M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5c1 + model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for L1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5c2 + model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + log(mu_L1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for L1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5d1 + model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + log(mu_P1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for P1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5d2 + model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + mu_P1[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for P1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5e1 + model { + + # Log-normal model for L1 ------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for L1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m5f1 + model { + + # Beta model for Be1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for Be1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m6a + model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] + } + + # Priors for the model for y + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } + $m6b + model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] + } + + # Priors for the model for B1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } + $m6c + model { + + # Gamma model for C1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_C1[i], rate_C1[i]) + + shape_C1[i] <- pow(mu_C1[i], 2) / pow(sigma_C1, 2) + rate_C1[i] <- mu_C1[i] / pow(sigma_C1, 2) + + log(mu_C1[i]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + } + + # Priors for the model for C1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_C1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 16] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 14] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } + $m6d + model { + + # Normal model for SBP ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) + mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 6] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + } + + # Priors for the model for SBP + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_SBP <- sqrt(1/tau_SBP) + + + + # Normal model for bili --------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili)T(1e-05, 1e+10) + mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] + + M_lvlone[i, 7] <- log(M_lvlone[i, 2]) + + + } + + # Priors for the model for bili + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_bili <- sqrt(1/tau_bili) + + + + # Normal model for creat -------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) + mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + M_lvlone[i, 6] * alpha[7] + + M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) + + + } + + # Priors for the model for creat + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_creat <- sqrt(1/tau_creat) + + } + $m6e + model { + + # Normal model for SBP ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) + mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 6] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + } + + # Priors for the model for SBP + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_SBP <- sqrt(1/tau_SBP) + + + + # Log-normal model for bili ----------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 2] ~ dlnorm(mu_bili[i], tau_bili) + mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] + + M_lvlone[i, 7] <- log(M_lvlone[i, 2]) + + + } + + # Priors for the model for bili + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_bili <- sqrt(1/tau_bili) + + + + # Normal model for creat -------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) + mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + M_lvlone[i, 6] * alpha[7] + + M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) + + + } + + # Priors for the model for creat + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_creat <- sqrt(1/tau_creat) + + } + $m6f + model { + + # Normal model for SBP ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) + mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 6] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + } + + # Priors for the model for SBP + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_SBP <- sqrt(1/tau_SBP) + + + + # Gamma model for bili ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 2] ~ dgamma(shape_bili[i], rate_bili[i]) + + shape_bili[i] <- pow(mu_bili[i], 2) / pow(sigma_bili, 2) + rate_bili[i] <- mu_bili[i] / pow(sigma_bili, 2) + + mu_bili[i] <- 1/max(1e-10, inv_mu_bili[i]) + inv_mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] + + M_lvlone[i, 7] <- log(M_lvlone[i, 2]) + + + } + + # Priors for the model for bili + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_bili ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_bili <- sqrt(1/tau_bili) + + + + # Normal model for creat -------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) + mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + M_lvlone[i, 6] * alpha[7] + + M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) + + + } + + # Priors for the model for creat + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_creat <- sqrt(1/tau_creat) + + } + $mod7a + model { + + # Normal model for SBP ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) + mu_SBP[i] <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[3] + + M_lvlone[i, 6] * beta[4] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[5] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[6] + } + + # Priors for the model for SBP + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_SBP <- sqrt(1/tau_SBP) + + + + # Normal model for bili --------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili) + mu_bili[i] <- M_lvlone[i, 3] * alpha[1] + + (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + M_lvlone[i, 7] <- M_lvlone[i, 2]^2 + M_lvlone[i, 8] <- M_lvlone[i, 2]^3 + + + } + + # Priors for the model for bili + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_bili <- sqrt(1/tau_bili) + + } + +# GRcrit and MCerror give same result + + Code + lapply(models0, GR_crit, multivariate = FALSE) + Output + $m0a1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_y NaN NaN + + + $m0a2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_y NaN NaN + + + $m0a3 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_y NaN NaN + + + $m0a4 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_y NaN NaN + + + $m0b1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + + + $m0b2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + + + $m0b3 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + + + $m0b4 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + + + $m0c1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_L1 NaN NaN + + + $m0c2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_L1 NaN NaN + + + $m0d1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + + + $m0d2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + + + $m0e1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_L1 NaN NaN + + + $m0f1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + tau_Be1 NaN NaN + + + $m1a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + sigma_y NaN NaN + + + $m1b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + + + $m1c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + sigma_L1 NaN NaN + + + $m1d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + + + $m1e + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + sigma_L1 NaN NaN + + + $m1f + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + tau_Be1 NaN NaN + + + $m2a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + sigma_y NaN NaN + + + $m2b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + + + $m2c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + sigma_L1mis NaN NaN + + + $m2d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + + + $m2e + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + sigma_L1mis NaN NaN + + + $m2f + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + tau_Be2 NaN NaN + + + $m3a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + P2 NaN NaN + L1mis NaN NaN + Be2 NaN NaN + sigma_C1 NaN NaN + + + $m3b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + P2 NaN NaN + L1mis NaN NaN + sigma_C1 NaN NaN + + + $m3c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + P2 NaN NaN + L1mis NaN NaN + sigma_C1 NaN NaN + + + $m3d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + P2 NaN NaN + L1mis NaN NaN + Be2 NaN NaN + sigma_C1 NaN NaN + + + $m4a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + abs(C1 - C2) NaN NaN + log(C1) NaN NaN + O22:abs(C1 - C2) NaN NaN + O23:abs(C1 - C2) NaN NaN + O24:abs(C1 - C2) NaN NaN + sigma_y NaN NaN + + + $m4b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + L1mis NaN NaN + abs(C1 - C2) NaN NaN + log(Be2) NaN NaN + + + $m5a1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + sigma_y NaN NaN + + + $m5a2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + sigma_y NaN NaN + + + $m5a3 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + sigma_y NaN NaN + + + $m5b1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + C1 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + + + $m5b2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + C1 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + + + $m5b3 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + C1 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + + + $m5b4 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + C1 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + + + $m5c1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + sigma_L1 NaN NaN + + + $m5c2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + sigma_L1 NaN NaN + + + $m5d1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + + + $m5d2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + + + $m5e1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + sigma_L1 NaN NaN + + + $m5f1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B21 NaN NaN + B11 NaN NaN + O1.L NaN NaN + O1.Q NaN NaN + O1.C NaN NaN + tau_Be1 NaN NaN + + + $m6a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + abs(C1 - C2) NaN NaN + log(C1) NaN NaN + O22:abs(C1 - C2) NaN NaN + O23:abs(C1 - C2) NaN NaN + O24:abs(C1 - C2) NaN NaN + sigma_y NaN NaN + + + $m6b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + abs(C1 - C2) NaN NaN + log(C1) NaN NaN + O22:abs(C1 - C2) NaN NaN + O23:abs(C1 - C2) NaN NaN + O24:abs(C1 - C2) NaN NaN + + + $m6c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + O22 NaN NaN + O23 NaN NaN + O24 NaN NaN + abs(y - C2) NaN NaN + O22:abs(y - C2) NaN NaN + O23:abs(y - C2) NaN NaN + O24:abs(y - C2) NaN NaN + sigma_C1 NaN NaN + + + $m6d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + age NaN NaN + genderfemale NaN NaN + log(bili) NaN NaN + exp(creat) NaN NaN + sigma_SBP NaN NaN + + + $m6e + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + age NaN NaN + genderfemale NaN NaN + log(bili) NaN NaN + exp(creat) NaN NaN + sigma_SBP NaN NaN + + + $m6f + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + age NaN NaN + genderfemale NaN NaN + log(bili) NaN NaN + exp(creat) NaN NaN + sigma_SBP NaN NaN + + + $mod7a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + ns(age, df = 2)1 NaN NaN + ns(age, df = 2)2 NaN NaN + genderfemale NaN NaN + I(bili^2) NaN NaN + I(bili^3) NaN NaN + sigma_SBP NaN NaN + + + +--- + + Code + lapply(models0, MC_error) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m0a2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m0a3 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m0a4 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m0b1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + + $m0b2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + + $m0b3 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + + $m0b4 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + + $m0c1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_L1 0 0 0 NaN + + $m0c2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_L1 0 0 0 NaN + + $m0d1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + + $m0d2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + + $m0e1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_L1 0 0 0 NaN + + $m0f1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + tau_Be1 0 0 0 NaN + + $m1a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m1b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + + $m1c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + sigma_L1 0 0 0 NaN + + $m1d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + + $m1e + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + sigma_L1 0 0 0 NaN + + $m1f + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + tau_Be1 0 0 0 NaN + + $m2a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m2b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + + $m2c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + sigma_L1mis 0 0 0 NaN + + $m2d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + + $m2e + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + sigma_L1mis 0 0 0 NaN + + $m2f + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + tau_Be2 0 0 0 NaN + + $m3a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + P2 0 0 0 NaN + L1mis 0 0 0 NaN + Be2 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m3b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + P2 0 0 0 NaN + L1mis 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m3c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + P2 0 0 0 NaN + L1mis 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m3d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + P2 0 0 0 NaN + L1mis 0 0 0 NaN + Be2 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m4a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + abs(C1 - C2) 0 0 0 NaN + log(C1) 0 0 0 NaN + O22:abs(C1 - C2) 0 0 0 NaN + O23:abs(C1 - C2) 0 0 0 NaN + O24:abs(C1 - C2) 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m4b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + L1mis 0 0 0 NaN + abs(C1 - C2) 0 0 0 NaN + log(Be2) 0 0 0 NaN + + $m5a1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m5a2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m5a3 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m5b1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + C1 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + + $m5b2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + C1 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + + $m5b3 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + C1 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + + $m5b4 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + C1 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + + $m5c1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + sigma_L1 0 0 0 NaN + + $m5c2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + sigma_L1 0 0 0 NaN + + $m5d1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + + $m5d2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + + $m5e1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + sigma_L1 0 0 0 NaN + + $m5f1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B21 0 0 0 NaN + B11 0 0 0 NaN + O1.L 0 0 0 NaN + O1.Q 0 0 0 NaN + O1.C 0 0 0 NaN + tau_Be1 0 0 0 NaN + + $m6a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + abs(C1 - C2) 0 0 0 NaN + log(C1) 0 0 0 NaN + O22:abs(C1 - C2) 0 0 0 NaN + O23:abs(C1 - C2) 0 0 0 NaN + O24:abs(C1 - C2) 0 0 0 NaN + sigma_y 0 0 0 NaN + + $m6b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + abs(C1 - C2) 0 0 0 NaN + log(C1) 0 0 0 NaN + O22:abs(C1 - C2) 0 0 0 NaN + O23:abs(C1 - C2) 0 0 0 NaN + O24:abs(C1 - C2) 0 0 0 NaN + + $m6c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + O22 0 0 0 NaN + O23 0 0 0 NaN + O24 0 0 0 NaN + abs(y - C2) 0 0 0 NaN + O22:abs(y - C2) 0 0 0 NaN + O23:abs(y - C2) 0 0 0 NaN + O24:abs(y - C2) 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m6d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + age 0 0 0 NaN + genderfemale 0 0 0 NaN + log(bili) 0 0 0 NaN + exp(creat) 0 0 0 NaN + sigma_SBP 0 0 0 NaN + + $m6e + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + age 0 0 0 NaN + genderfemale 0 0 0 NaN + log(bili) 0 0 0 NaN + exp(creat) 0 0 0 NaN + sigma_SBP 0 0 0 NaN + + $m6f + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + age 0 0 0 NaN + genderfemale 0 0 0 NaN + log(bili) 0 0 0 NaN + exp(creat) 0 0 0 NaN + sigma_SBP 0 0 0 NaN + + $mod7a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + ns(age, df = 2)1 0 0 0 NaN + ns(age, df = 2)2 0 0 0 NaN + genderfemale 0 0 0 NaN + I(bili^2) 0 0 0 NaN + I(bili^3) 0 0 0 NaN + sigma_SBP 0 0 0 NaN + + +# summary output remained the same + + Code + lapply(models0, print) + Output + + Call: + lm_imp(formula = y ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) + 0 + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) + 0 + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF, + n.adapt = 150, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) + 0 + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"), + data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) + 0 + + Call: + glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) + 0 + + Call: + glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) + 0 + + Call: + lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal model for "L1" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta model for "Be1" + + + Coefficients: + (Intercept) + 0 + + Call: + lm_imp(formula = y ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C1 + 0 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C1 + 0 0 + + Call: + glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) C1 + 0 0 + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) C1 + 0 0 + + Call: + lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal model for "L1" + + + Coefficients: + (Intercept) C1 + 0 0 + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta model for "Be1" + + + Coefficients: + (Intercept) C1 + 0 0 + + Call: + lm_imp(formula = y ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C2 + 0 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial model for "B2" + + + Coefficients: + (Intercept) C2 + 0 0 + + Call: + glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma model for "L1mis" + + + Coefficients: + (Intercept) C2 + 0 0 + + + Residual standard deviation: + sigma_L1mis + 0 + + Call: + glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson model for "P2" + + + Coefficients: + (Intercept) C2 + 0 0 + + Call: + lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal model for "L1mis" + + + Coefficients: + (Intercept) C2 + 0 0 + + + Residual standard deviation: + sigma_L1mis + 0 + + Call: + betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta model for "Be2" + + + Coefficients: + (Intercept) C2 + 0 0 + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, + n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log", + L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) C2 B21 P2 L1mis Be2 + 0 0 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, + n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity", + B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) C2 B21 P2 L1mis + 0 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, + n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity", + L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) C2 B21 P2 L1mis + 0 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, + n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + P2 = "glm_poisson_identity", L1mis = "glm_gamma_log", + B2 = "glm_binomial_log"), seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(Be2 = c(0, 1))) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) C2 B21 P2 L1mis Be2 + 0 0 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(), + data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) L1mis abs(C1 - C2) log(Be2) + 0 0 0 0 + + Call: + lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + Call: + glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + Call: + glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + Call: + lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal model for "L1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta model for "Be1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + Call: + lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + + Residual standard deviation: + sigma_y + 0 + + Call: + glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial", + data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + Call: + glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian Gamma model for "C1" + + + Coefficients: + (Intercept) M22 M23 M24 O22 + 0 0 0 0 0 + O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2) + 0 0 0 0 0 + O24:abs(y - C2) + 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(bili = c(1e-05, 1e+10))) + + Bayesian linear model for "SBP" + + + Coefficients: + (Intercept) age genderfemale log(bili) exp(creat) + 0 0 0 0 0 + + + Residual standard deviation: + sigma_SBP + 0 + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm", + creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "SBP" + + + Coefficients: + (Intercept) age genderfemale log(bili) exp(creat) + 0 0 0 0 0 + + + Residual standard deviation: + sigma_SBP + 0 + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse", + creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "SBP" + + + Coefficients: + (Intercept) age genderfemale log(bili) exp(creat) + 0 0 0 0 0 + + + Residual standard deviation: + sigma_SBP + 0 + + Call: + lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) + + I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear model for "SBP" + + + Coefficients: + (Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale + 0 0 0 0 + I(bili^2) I(bili^3) + 0 0 + + + Residual standard deviation: + sigma_SBP + 0 + $m0a1 + + Call: + lm_imp(formula = y ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_y + 0 + + $m0a2 + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_y + 0 + + $m0a3 + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_y + 0 + + $m0a4 + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_y + 0 + + $m0b1 + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) + 0 + + $m0b2 + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) + 0 + + $m0b3 + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF, + n.adapt = 150, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) + 0 + + $m0b4 + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"), + data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) + 0 + + $m0c1 + + Call: + glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + $m0c2 + + Call: + glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + $m0d1 + + Call: + glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) + 0 + + $m0d2 + + Call: + glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) + 0 + + $m0e1 + + Call: + lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal model for "L1" + + + Coefficients: + (Intercept) + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + $m0f1 + + Call: + betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta model for "Be1" + + + Coefficients: + (Intercept) + 0 + + $m1a + + Call: + lm_imp(formula = y ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C1 + 0 0 + + + Residual standard deviation: + sigma_y + 0 + + $m1b + + Call: + glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C1 + 0 0 + + $m1c + + Call: + glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) C1 + 0 0 + + + Residual standard deviation: + sigma_L1 + 0 + + $m1d + + Call: + glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) C1 + 0 0 + + $m1e + + Call: + lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal model for "L1" + + + Coefficients: + (Intercept) C1 + 0 0 + + + Residual standard deviation: + sigma_L1 + 0 + + $m1f + + Call: + betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta model for "Be1" + + + Coefficients: + (Intercept) C1 + 0 0 + + $m2a + + Call: + lm_imp(formula = y ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C2 + 0 0 + + + Residual standard deviation: + sigma_y + 0 + + $m2b + + Call: + glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial model for "B2" + + + Coefficients: + (Intercept) C2 + 0 0 + + $m2c + + Call: + glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma model for "L1mis" + + + Coefficients: + (Intercept) C2 + 0 0 + + + Residual standard deviation: + sigma_L1mis + 0 + + $m2d + + Call: + glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson model for "P2" + + + Coefficients: + (Intercept) C2 + 0 0 + + $m2e + + Call: + lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal model for "L1mis" + + + Coefficients: + (Intercept) C2 + 0 0 + + + Residual standard deviation: + sigma_L1mis + 0 + + $m2f + + Call: + betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta model for "Be2" + + + Coefficients: + (Intercept) C2 + 0 0 + + $m3a + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, + n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log", + L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) C2 B21 P2 L1mis Be2 + 0 0 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m3b + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, + n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity", + B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) C2 B21 P2 L1mis + 0 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m3c + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, + n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity", + L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) C2 B21 P2 L1mis + 0 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m3d + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, + n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + P2 = "glm_poisson_identity", L1mis = "glm_gamma_log", + B2 = "glm_binomial_log"), seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(Be2 = c(0, 1))) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) C2 B21 P2 L1mis Be2 + 0 0 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m4a + + Call: + lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + + Residual standard deviation: + sigma_y + 0 + + $m4b + + Call: + glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(), + data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) L1mis abs(C1 - C2) log(Be2) + 0 0 0 0 + + $m5a1 + + Call: + lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_y + 0 + + $m5a2 + + Call: + glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_y + 0 + + $m5a3 + + Call: + glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_y + 0 + + $m5b1 + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + $m5b2 + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + $m5b3 + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + $m5b4 + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + $m5c1 + + Call: + glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + $m5c2 + + Call: + glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian Gamma model for "L1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + $m5d1 + + Call: + glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + $m5d2 + + Call: + glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian poisson model for "P1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + $m5e1 + + Call: + lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal model for "L1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + Residual standard deviation: + sigma_L1 + 0 + + $m5f1 + + Call: + betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta model for "Be1" + + + Coefficients: + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + $m6a + + Call: + lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "y" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + + Residual standard deviation: + sigma_y + 0 + + $m6b + + Call: + glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial", + data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian binomial model for "B1" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + $m6c + + Call: + glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian Gamma model for "C1" + + + Coefficients: + (Intercept) M22 M23 M24 O22 + 0 0 0 0 0 + O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2) + 0 0 0 0 0 + O24:abs(y - C2) + 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m6d + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(bili = c(1e-05, 1e+10))) + + Bayesian linear model for "SBP" + + + Coefficients: + (Intercept) age genderfemale log(bili) exp(creat) + 0 0 0 0 0 + + + Residual standard deviation: + sigma_SBP + 0 + + $m6e + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm", + creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "SBP" + + + Coefficients: + (Intercept) age genderfemale log(bili) exp(creat) + 0 0 0 0 0 + + + Residual standard deviation: + sigma_SBP + 0 + + $m6f + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse", + creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "SBP" + + + Coefficients: + (Intercept) age genderfemale log(bili) exp(creat) + 0 0 0 0 0 + + + Residual standard deviation: + sigma_SBP + 0 + + $mod7a + + Call: + lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) + + I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear model for "SBP" + + + Coefficients: + (Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale + 0 0 0 0 + I(bili^2) I(bili^3) + 0 0 + + + Residual standard deviation: + sigma_SBP + 0 + + +--- + + Code + lapply(models0, coef) + Output + $m0a1 + $m0a1$y + (Intercept) sigma_y + 0 0 + + + $m0a2 + $m0a2$y + (Intercept) sigma_y + 0 0 + + + $m0a3 + $m0a3$y + (Intercept) sigma_y + 0 0 + + + $m0a4 + $m0a4$y + (Intercept) sigma_y + 0 0 + + + $m0b1 + $m0b1$B1 + (Intercept) + 0 + + + $m0b2 + $m0b2$B1 + (Intercept) + 0 + + + $m0b3 + $m0b3$B1 + (Intercept) + 0 + + + $m0b4 + $m0b4$B1 + (Intercept) + 0 + + + $m0c1 + $m0c1$L1 + (Intercept) sigma_L1 + 0 0 + + + $m0c2 + $m0c2$L1 + (Intercept) sigma_L1 + 0 0 + + + $m0d1 + $m0d1$P1 + (Intercept) + 0 + + + $m0d2 + $m0d2$P1 + (Intercept) + 0 + + + $m0e1 + $m0e1$L1 + (Intercept) sigma_L1 + 0 0 + + + $m0f1 + $m0f1$Be1 + (Intercept) tau_Be1 + 0 0 + + + $m1a + $m1a$y + (Intercept) C1 sigma_y + 0 0 0 + + + $m1b + $m1b$B1 + (Intercept) C1 + 0 0 + + + $m1c + $m1c$L1 + (Intercept) C1 sigma_L1 + 0 0 0 + + + $m1d + $m1d$P1 + (Intercept) C1 + 0 0 + + + $m1e + $m1e$L1 + (Intercept) C1 sigma_L1 + 0 0 0 + + + $m1f + $m1f$Be1 + (Intercept) C1 tau_Be1 + 0 0 0 + + + $m2a + $m2a$y + (Intercept) C2 sigma_y + 0 0 0 + + + $m2b + $m2b$B2 + (Intercept) C2 + 0 0 + + + $m2c + $m2c$L1mis + (Intercept) C2 sigma_L1mis + 0 0 0 + + + $m2d + $m2d$P2 + (Intercept) C2 + 0 0 + + + $m2e + $m2e$L1mis + (Intercept) C2 sigma_L1mis + 0 0 0 + + + $m2f + $m2f$Be2 + (Intercept) C2 tau_Be2 + 0 0 0 + + + $m3a + $m3a$C1 + (Intercept) C2 B21 P2 L1mis Be2 + 0 0 0 0 0 0 + sigma_C1 + 0 + + + $m3b + $m3b$C1 + (Intercept) C2 B21 P2 L1mis sigma_C1 + 0 0 0 0 0 0 + + + $m3c + $m3c$C1 + (Intercept) C2 B21 P2 L1mis sigma_C1 + 0 0 0 0 0 0 + + + $m3d + $m3d$C1 + (Intercept) C2 B21 P2 L1mis Be2 + 0 0 0 0 0 0 + sigma_C1 + 0 + + + $m4a + $m4a$y + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + sigma_y + 0 + + + $m4b + $m4b$B1 + (Intercept) L1mis abs(C1 - C2) log(Be2) + 0 0 0 0 + + + $m5a1 + $m5a1$y + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C sigma_y + 0 0 + + + $m5a2 + $m5a2$y + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C sigma_y + 0 0 + + + $m5a3 + $m5a3$y + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C sigma_y + 0 0 + + + $m5b1 + $m5b1$B1 + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + $m5b2 + $m5b2$B1 + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + $m5b3 + $m5b3$B1 + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + $m5b4 + $m5b4$B1 + (Intercept) C2 B21 C1 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + $m5c1 + $m5c1$L1 + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C sigma_L1 + 0 0 + + + $m5c2 + $m5c2$L1 + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C sigma_L1 + 0 0 + + + $m5d1 + $m5d1$P1 + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + $m5d2 + $m5d2$P1 + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C + 0 + + + $m5e1 + $m5e1$L1 + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C sigma_L1 + 0 0 + + + $m5f1 + $m5f1$Be1 + (Intercept) C2 B21 B11 O1.L O1.Q + 0 0 0 0 0 0 + O1.C tau_Be1 + 0 0 + + + $m6a + $m6a$y + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + sigma_y + 0 + + + $m6b + $m6b$B1 + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + + $m6c + $m6c$C1 + (Intercept) M22 M23 M24 O22 + 0 0 0 0 0 + O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2) + 0 0 0 0 0 + O24:abs(y - C2) sigma_C1 + 0 0 + + + $m6d + $m6d$SBP + (Intercept) age genderfemale log(bili) exp(creat) sigma_SBP + 0 0 0 0 0 0 + + + $m6e + $m6e$SBP + (Intercept) age genderfemale log(bili) exp(creat) sigma_SBP + 0 0 0 0 0 0 + + + $m6f + $m6f$SBP + (Intercept) age genderfemale log(bili) exp(creat) sigma_SBP + 0 0 0 0 0 0 + + + $mod7a + $mod7a$SBP + (Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale + 0 0 0 0 + I(bili^2) I(bili^3) sigma_SBP + 0 0 0 + + + +--- + + Code + lapply(models0, confint) + Output + $m0a1 + $m0a1$y + 2.5% 97.5% + (Intercept) 0 0 + sigma_y 0 0 + + + $m0a2 + $m0a2$y + 2.5% 97.5% + (Intercept) 0 0 + sigma_y 0 0 + + + $m0a3 + $m0a3$y + 2.5% 97.5% + (Intercept) 0 0 + sigma_y 0 0 + + + $m0a4 + $m0a4$y + 2.5% 97.5% + (Intercept) 0 0 + sigma_y 0 0 + + + $m0b1 + $m0b1$B1 + 2.5% 97.5% + (Intercept) 0 0 + + + $m0b2 + $m0b2$B1 + 2.5% 97.5% + (Intercept) 0 0 + + + $m0b3 + $m0b3$B1 + 2.5% 97.5% + (Intercept) 0 0 + + + $m0b4 + $m0b4$B1 + 2.5% 97.5% + (Intercept) 0 0 + + + $m0c1 + $m0c1$L1 + 2.5% 97.5% + (Intercept) 0 0 + sigma_L1 0 0 + + + $m0c2 + $m0c2$L1 + 2.5% 97.5% + (Intercept) 0 0 + sigma_L1 0 0 + + + $m0d1 + $m0d1$P1 + 2.5% 97.5% + (Intercept) 0 0 + + + $m0d2 + $m0d2$P1 + 2.5% 97.5% + (Intercept) 0 0 + + + $m0e1 + $m0e1$L1 + 2.5% 97.5% + (Intercept) 0 0 + sigma_L1 0 0 + + + $m0f1 + $m0f1$Be1 + 2.5% 97.5% + (Intercept) 0 0 + tau_Be1 0 0 + + + $m1a + $m1a$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + sigma_y 0 0 + + + $m1b + $m1b$B1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + + + $m1c + $m1c$L1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + sigma_L1 0 0 + + + $m1d + $m1d$P1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + + + $m1e + $m1e$L1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + sigma_L1 0 0 + + + $m1f + $m1f$Be1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + tau_Be1 0 0 + + + $m2a + $m2a$y + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + sigma_y 0 0 + + + $m2b + $m2b$B2 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + + + $m2c + $m2c$L1mis + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + sigma_L1mis 0 0 + + + $m2d + $m2d$P2 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + + + $m2e + $m2e$L1mis + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + sigma_L1mis 0 0 + + + $m2f + $m2f$Be2 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + tau_Be2 0 0 + + + $m3a + $m3a$C1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + P2 0 0 + L1mis 0 0 + Be2 0 0 + sigma_C1 0 0 + + + $m3b + $m3b$C1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + P2 0 0 + L1mis 0 0 + sigma_C1 0 0 + + + $m3c + $m3c$C1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + P2 0 0 + L1mis 0 0 + sigma_C1 0 0 + + + $m3d + $m3d$C1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + P2 0 0 + L1mis 0 0 + Be2 0 0 + sigma_C1 0 0 + + + $m4a + $m4a$y + 2.5% 97.5% + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + O22:abs(C1 - C2) 0 0 + O23:abs(C1 - C2) 0 0 + O24:abs(C1 - C2) 0 0 + sigma_y 0 0 + + + $m4b + $m4b$B1 + 2.5% 97.5% + (Intercept) 0 0 + L1mis 0 0 + abs(C1 - C2) 0 0 + log(Be2) 0 0 + + + $m5a1 + $m5a1$y + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + sigma_y 0 0 + + + $m5a2 + $m5a2$y + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + sigma_y 0 0 + + + $m5a3 + $m5a3$y + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + sigma_y 0 0 + + + $m5b1 + $m5b1$B1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + C1 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + + + $m5b2 + $m5b2$B1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + C1 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + + + $m5b3 + $m5b3$B1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + C1 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + + + $m5b4 + $m5b4$B1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + C1 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + + + $m5c1 + $m5c1$L1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + sigma_L1 0 0 + + + $m5c2 + $m5c2$L1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + sigma_L1 0 0 + + + $m5d1 + $m5d1$P1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + + + $m5d2 + $m5d2$P1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + + + $m5e1 + $m5e1$L1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + sigma_L1 0 0 + + + $m5f1 + $m5f1$Be1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B21 0 0 + B11 0 0 + O1.L 0 0 + O1.Q 0 0 + O1.C 0 0 + tau_Be1 0 0 + + + $m6a + $m6a$y + 2.5% 97.5% + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + O22:abs(C1 - C2) 0 0 + O23:abs(C1 - C2) 0 0 + O24:abs(C1 - C2) 0 0 + sigma_y 0 0 + + + $m6b + $m6b$B1 + 2.5% 97.5% + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + O22:abs(C1 - C2) 0 0 + O23:abs(C1 - C2) 0 0 + O24:abs(C1 - C2) 0 0 + + + $m6c + $m6c$C1 + 2.5% 97.5% + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + abs(y - C2) 0 0 + O22:abs(y - C2) 0 0 + O23:abs(y - C2) 0 0 + O24:abs(y - C2) 0 0 + sigma_C1 0 0 + + + $m6d + $m6d$SBP + 2.5% 97.5% + (Intercept) 0 0 + age 0 0 + genderfemale 0 0 + log(bili) 0 0 + exp(creat) 0 0 + sigma_SBP 0 0 + + + $m6e + $m6e$SBP + 2.5% 97.5% + (Intercept) 0 0 + age 0 0 + genderfemale 0 0 + log(bili) 0 0 + exp(creat) 0 0 + sigma_SBP 0 0 + + + $m6f + $m6f$SBP + 2.5% 97.5% + (Intercept) 0 0 + age 0 0 + genderfemale 0 0 + log(bili) 0 0 + exp(creat) 0 0 + sigma_SBP 0 0 + + + $mod7a + $mod7a$SBP + 2.5% 97.5% + (Intercept) 0 0 + ns(age, df = 2)1 0 0 + ns(age, df = 2)2 0 0 + genderfemale 0 0 + I(bili^2) 0 0 + I(bili^3) 0 0 + sigma_SBP 0 0 + + + +--- + + Code + lapply(models0, summary, missinfo = TRUE) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a1 + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = y ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + y 0 0 + + + $m0a2 + + Bayesian linear model fitted with JointAI + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + y 0 0 + + + $m0a3 + + Bayesian linear model fitted with JointAI + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + y 0 0 + + + $m0a4 + + Bayesian linear model fitted with JointAI + + Call: + glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + y 0 0 + + + $m0b1 + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + + + $m0b2 + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + + + $m0b3 + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF, + n.adapt = 150, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 151:160 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + + + $m0b4 + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"), + data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 51:60 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + + + $m0c1 + + Bayesian Gamma model fitted with JointAI + + Call: + glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + L1 0 0 + + + $m0c2 + + Bayesian Gamma model fitted with JointAI + + Call: + glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + L1 0 0 + + + $m0d1 + + Bayesian poisson model fitted with JointAI + + Call: + glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + P1 0 0 + + + $m0d2 + + Bayesian poisson model fitted with JointAI + + Call: + glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + P1 0 0 + + + $m0e1 + + Bayesian log-normal model fitted with JointAI + + Call: + lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + L1 0 0 + + + $m0f1 + + Bayesian beta model fitted with JointAI + + Call: + betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + tau_Be1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + Be1 0 0 + + + $m1a + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = y ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + y 0 0 + C1 0 0 + + + $m1b + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + C1 0 0 + + + $m1c + + Bayesian Gamma model fitted with JointAI + + Call: + glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + L1 0 0 + C1 0 0 + + + $m1d + + Bayesian poisson model fitted with JointAI + + Call: + glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + P1 0 0 + C1 0 0 + + + $m1e + + Bayesian log-normal model fitted with JointAI + + Call: + lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + L1 0 0 + C1 0 0 + + + $m1f + + Bayesian beta model fitted with JointAI + + Call: + betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + tau_Be1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 100 100 + + Number and proportion of missing values: + # NA % NA + Be1 0 0 + C1 0 0 + + + $m2a + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = y ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 96 96 + + Number and proportion of missing values: + # NA % NA + y 0 0 + C2 4 4 + + + $m2b + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + C2 4 4 + B2 20 20 + + + $m2c + + Bayesian Gamma model fitted with JointAI + + Call: + glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1mis 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 76 76 + + Number and proportion of missing values: + # NA % NA + C2 4 4 + L1mis 20 20 + + + $m2d + + Bayesian poisson model fitted with JointAI + + Call: + glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + C2 4 4 + P2 20 20 + + + $m2e + + Bayesian log-normal model fitted with JointAI + + Call: + lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1mis 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 76 76 + + Number and proportion of missing values: + # NA % NA + C2 4 4 + L1mis 20 20 + + + $m2f + + Bayesian beta model fitted with JointAI + + Call: + betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + tau_Be2 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + C2 4 4 + Be2 20 20 + + + $m3a + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, + n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log", + L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + P2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + Be2 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 36 36 + + Number and proportion of missing values: + # NA % NA + C1 0 0 + C2 4 4 + B2 20 20 + P2 20 20 + L1mis 20 20 + Be2 20 20 + + + $m3b + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, + n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity", + B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + P2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 48 48 + + Number and proportion of missing values: + # NA % NA + C1 0 0 + C2 4 4 + B2 20 20 + P2 20 20 + L1mis 20 20 + + + $m3c + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, + n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity", + L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + P2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 48 48 + + Number and proportion of missing values: + # NA % NA + C1 0 0 + C2 4 4 + B2 20 20 + P2 20 20 + L1mis 20 20 + + + $m3d + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, + n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + P2 = "glm_poisson_identity", L1mis = "glm_gamma_log", + B2 = "glm_binomial_log"), seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(Be2 = c(0, 1))) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + P2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + Be2 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 36 36 + + Number and proportion of missing values: + # NA % NA + C1 0 0 + C2 4 4 + B2 20 20 + P2 20 20 + L1mis 20 20 + Be2 20 20 + + + $m4a + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 91 91 + + Number and proportion of missing values: + # NA % NA + y 0 0 + C1 0 0 + O2 2 2 + M2 3 3 + C2 4 4 + + + $m4b + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(), + data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(Be2) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 60 60 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + C1 0 0 + C2 4 4 + L1mis 20 20 + Be2 20 20 + + + $m5a1 + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + y 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5a2 + + Bayesian linear model fitted with JointAI + + Call: + glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + y 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5a3 + + Bayesian linear model fitted with JointAI + + Call: + glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + y 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5b1 + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + C1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5b2 + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + C1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5b3 + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + C1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5b4 + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + C1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5c1 + + Bayesian Gamma model fitted with JointAI + + Call: + glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + L1 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5c2 + + Bayesian Gamma model fitted with JointAI + + Call: + glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + L1 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5d1 + + Bayesian poisson model fitted with JointAI + + Call: + glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + P1 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5d2 + + Bayesian poisson model fitted with JointAI + + Call: + glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"), + data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + P1 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5e1 + + Bayesian log-normal model fitted with JointAI + + Call: + lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + L1 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m5f1 + + Bayesian beta model fitted with JointAI + + Call: + betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + tau_Be1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 77 77 + + Number and proportion of missing values: + # NA % NA + Be1 0 0 + B1 0 0 + O1 0 0 + C2 4 4 + B2 20 20 + + + $m6a + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:10 + Sample size per chain = 5 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 91 91 + + Number and proportion of missing values: + # NA % NA + y 0 0 + C1 0 0 + O2 2 2 + M2 3 3 + C2 4 4 + + + $m6b + + Bayesian binomial model fitted with JointAI + + Call: + glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial", + data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:10 + Sample size per chain = 5 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 91 91 + + Number and proportion of missing values: + # NA % NA + B1 0 0 + C1 0 0 + O2 2 2 + M2 3 3 + C2 4 4 + + + $m6c + + Bayesian Gamma model fitted with JointAI + + Call: + glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"), + data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(y - C2) 0 0 0 0 0 NaN NaN + O22:abs(y - C2) 0 0 0 0 0 NaN NaN + O23:abs(y - C2) 0 0 0 0 0 NaN NaN + O24:abs(y - C2) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:10 + Sample size per chain = 5 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + + Number and proportion of complete cases: + # % + lvlone 91 91 + + Number and proportion of missing values: + # NA % NA + C1 0 0 + y 0 0 + O2 2 2 + M2 3 3 + C2 4 4 + + + $m6d + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(bili = c(1e-05, 1e+10))) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + genderfemale 0 0 0 0 0 NaN NaN + log(bili) 0 0 0 0 0 NaN NaN + exp(creat) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_SBP 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:10 + Sample size per chain = 5 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 186 + + + Number and proportion of complete cases: + # % + lvlone 178 95.7 + + Number and proportion of missing values: + # NA % NA + SBP 0 0.0 + age 0 0.0 + gender 0 0.0 + bili 8 4.3 + creat 8 4.3 + + + $m6e + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm", + creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + genderfemale 0 0 0 0 0 NaN NaN + log(bili) 0 0 0 0 0 NaN NaN + exp(creat) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_SBP 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:10 + Sample size per chain = 5 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 186 + + + Number and proportion of complete cases: + # % + lvlone 178 95.7 + + Number and proportion of missing values: + # NA % NA + SBP 0 0.0 + age 0 0.0 + gender 0 0.0 + bili 8 4.3 + creat 8 4.3 + + + $m6f + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), + data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse", + creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + genderfemale 0 0 0 0 0 NaN NaN + log(bili) 0 0 0 0 0 NaN NaN + exp(creat) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_SBP 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:10 + Sample size per chain = 5 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 186 + + + Number and proportion of complete cases: + # % + lvlone 178 95.7 + + Number and proportion of missing values: + # NA % NA + SBP 0 0.0 + age 0 0.0 + gender 0 0.0 + bili 8 4.3 + creat 8 4.3 + + + $mod7a + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) + + I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + ns(age, df = 2)1 0 0 0 0 0 NaN NaN + ns(age, df = 2)2 0 0 0 0 0 NaN NaN + genderfemale 0 0 0 0 0 NaN NaN + I(bili^2) 0 0 0 0 0 NaN NaN + I(bili^3) 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_SBP 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 186 + + + Number and proportion of complete cases: + # % + lvlone 178 95.7 + + Number and proportion of missing values: + # NA % NA + SBP 0 0.0 + age 0 0.0 + gender 0 0.0 + bili 8 4.3 + + + +--- + + Code + lapply(models0, function(x) coef(summary(x))) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a1 + $m0a1$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0a2 + $m0a2$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0a3 + $m0a3$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0a4 + $m0a4$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b1 + $m0b1$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b2 + $m0b2$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b3 + $m0b3$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b4 + $m0b4$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0c1 + $m0c1$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0c2 + $m0c2$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0d1 + $m0d1$P1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0d2 + $m0d2$P1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0e1 + $m0e1$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0f1 + $m0f1$Be1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m1a + $m1a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1b + $m1b$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1c + $m1c$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1d + $m1d$P1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1e + $m1e$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1f + $m1f$Be1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m2a + $m2a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + + $m2b + $m2b$B2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + + $m2c + $m2c$L1mis + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + + $m2d + $m2d$P2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + + $m2e + $m2e$L1mis + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + + $m2f + $m2f$Be2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + + + $m3a + $m3a$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + P2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + Be2 0 0 0 0 0 NaN NaN + + + $m3b + $m3b$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + P2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + + + $m3c + $m3c$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + P2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + + + $m3d + $m3d$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + P2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + Be2 0 0 0 0 0 NaN NaN + + + $m4a + $m4a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + $m4b + $m4b$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(Be2) 0 0 0 0 0 NaN NaN + + + $m5a1 + $m5a1$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5a2 + $m5a2$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5a3 + $m5a3$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5b1 + $m5b1$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5b2 + $m5b2$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5b3 + $m5b3$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5b4 + $m5b4$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5c1 + $m5c1$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5c2 + $m5c2$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5d1 + $m5d1$P1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5d2 + $m5d2$P1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5e1 + $m5e1$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m5f1 + $m5f1$Be1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + O1.L 0 0 0 0 0 NaN NaN + O1.Q 0 0 0 0 0 NaN NaN + O1.C 0 0 0 0 0 NaN NaN + + + $m6a + $m6a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + $m6b + $m6b$B1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(C1 - C2) 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + $m6c + $m6c$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + O22 0 0 0 0 0 NaN NaN + O23 0 0 0 0 0 NaN NaN + O24 0 0 0 0 0 NaN NaN + abs(y - C2) 0 0 0 0 0 NaN NaN + O22:abs(y - C2) 0 0 0 0 0 NaN NaN + O23:abs(y - C2) 0 0 0 0 0 NaN NaN + O24:abs(y - C2) 0 0 0 0 0 NaN NaN + + + $m6d + $m6d$SBP + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + genderfemale 0 0 0 0 0 NaN NaN + log(bili) 0 0 0 0 0 NaN NaN + exp(creat) 0 0 0 0 0 NaN NaN + + + $m6e + $m6e$SBP + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + genderfemale 0 0 0 0 0 NaN NaN + log(bili) 0 0 0 0 0 NaN NaN + exp(creat) 0 0 0 0 0 NaN NaN + + + $m6f + $m6f$SBP + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + genderfemale 0 0 0 0 0 NaN NaN + log(bili) 0 0 0 0 0 NaN NaN + exp(creat) 0 0 0 0 0 NaN NaN + + + $mod7a + $mod7a$SBP + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + ns(age, df = 2)1 0 0 0 0 0 NaN NaN + ns(age, df = 2)2 0 0 0 0 0 NaN NaN + genderfemale 0 0 0 0 0 NaN NaN + I(bili^2) 0 0 0 0 0 NaN NaN + I(bili^3) 0 0 0 0 0 NaN NaN + + + diff --git a/tests/testthat/_snaps/glmm.md b/tests/testthat/_snaps/glmm.md new file mode 100644 index 00000000..bf6b7f07 --- /dev/null +++ b/tests/testthat/_snaps/glmm.md @@ -0,0 +1,49011 @@ +# data_list remains the same + + Code + lapply(models, "[[", "data_list") + Output + $m0a1 + $m0a1$M_id + (Intercept) + 1 1 + 2 1 + 3 1 + 4 1 + 5 1 + 6 1 + 7 1 + 8 1 + 9 1 + 10 1 + 11 1 + 12 1 + 13 1 + 14 1 + 15 1 + 16 1 + 17 1 + 18 1 + 19 1 + 20 1 + 21 1 + 22 1 + 23 1 + 24 1 + 25 1 + 26 1 + 27 1 + 28 1 + 29 1 + 30 1 + 31 1 + 32 1 + 33 1 + 34 1 + 35 1 + 36 1 + 37 1 + 38 1 + 39 1 + 40 1 + 41 1 + 42 1 + 43 1 + 44 1 + 45 1 + 46 1 + 47 1 + 48 1 + 49 1 + 50 1 + 51 1 + 52 1 + 53 1 + 54 1 + 55 1 + 56 1 + 57 1 + 58 1 + 59 1 + 60 1 + 61 1 + 62 1 + 63 1 + 64 1 + 65 1 + 66 1 + 67 1 + 68 1 + 69 1 + 70 1 + 71 1 + 72 1 + 73 1 + 74 1 + 75 1 + 76 1 + 77 1 + 78 1 + 79 1 + 80 1 + 81 1 + 82 1 + 83 1 + 84 1 + 85 1 + 86 1 + 87 1 + 88 1 + 89 1 + 90 1 + 91 1 + 92 1 + 93 1 + 94 1 + 95 1 + 96 1 + 97 1 + 98 1 + 99 1 + 100 1 + + $m0a1$M_lvlone + y + 1 -13.0493856 + 1.1 -9.3335901 + 1.2 -22.3469852 + 1.3 -15.0417337 + 2 -12.0655434 + 2.1 -15.8674476 + 2.2 -7.8800006 + 3 -11.4820604 + 3.1 -10.5983220 + 3.2 -22.4519157 + 4 -1.2697775 + 4.1 -11.1215184 + 4.2 -3.6134138 + 4.3 -14.5982385 + 5 -6.8457515 + 5.1 -7.0551214 + 5.2 -12.3418980 + 5.3 -9.2366906 + 6 -5.1648211 + 7 -10.0599502 + 7.1 -18.3267285 + 7.2 -12.5138426 + 8 -1.6305331 + 8.1 -9.6520453 + 8.2 -1.5278462 + 8.3 -7.4172211 + 8.4 -7.1238609 + 8.5 -8.8706950 + 9 -0.1634429 + 9.1 -2.6034300 + 9.2 -6.7272369 + 10 -6.4172202 + 10.1 -11.4834569 + 11 -8.7911356 + 11.1 -19.6645080 + 11.2 -20.2030932 + 11.3 -21.3082176 + 11.4 -14.5802901 + 12 -15.2006287 + 13 0.8058816 + 13.1 -13.6379208 + 14 -15.3422873 + 14.1 -10.0965208 + 14.2 -16.6452027 + 14.3 -15.8389733 + 15 -8.9424594 + 15.1 -22.0101983 + 15.2 -7.3975599 + 15.3 -10.3567334 + 16 -1.9691302 + 16.1 -9.9308357 + 16.2 -6.9626923 + 16.3 -3.2862557 + 16.4 -3.3972355 + 16.5 -11.5767835 + 17 -10.5474144 + 17.1 -7.6215009 + 17.2 -16.5386939 + 17.3 -20.0004774 + 17.4 -18.8505475 + 18 -19.7302351 + 19 -14.6177568 + 19.1 -17.8043866 + 19.2 -15.1641705 + 19.3 -16.6898418 + 20 -12.9059229 + 20.1 -16.8191201 + 20.2 -6.1010131 + 20.3 -7.9415371 + 20.4 -9.3904458 + 20.5 -13.3504189 + 21 -7.6974718 + 21.1 -11.9335526 + 21.2 -12.7064929 + 22 -21.5022909 + 22.1 -12.7745451 + 23 -3.5146508 + 23.1 -4.6724048 + 24 -2.5619821 + 25 -6.2944970 + 25.1 -3.8630505 + 25.2 -14.4205140 + 25.3 -19.6735037 + 25.4 -9.0288933 + 25.5 -9.0509738 + 26 -19.7340685 + 26.1 -14.1692728 + 26.2 -17.2819976 + 26.3 -24.6265576 + 27 -7.3354999 + 27.1 -11.1488468 + 28 -11.7996597 + 28.1 -8.2030122 + 28.2 -26.4317815 + 28.3 -18.5016071 + 29 -5.8551395 + 29.1 -2.0209442 + 29.2 -5.6368080 + 29.3 -3.8110961 + 30 -12.7217702 + 30.1 -17.0170140 + 30.2 -25.4236089 + 31 -17.0783921 + 32 -18.4338764 + 32.1 -19.4317212 + 32.2 -19.4738978 + 32.3 -21.4922645 + 33 2.0838099 + 33.1 -13.3172274 + 34 -10.0296691 + 34.1 -25.9426553 + 34.2 -18.5688138 + 34.3 -15.4173859 + 35 -14.3958113 + 35.1 -12.9457541 + 35.2 -16.1380691 + 36 -12.8166968 + 36.1 -14.3989481 + 36.2 -12.2436943 + 36.3 -15.0104638 + 36.4 -10.1775457 + 37 -15.2223495 + 37.1 -14.7526195 + 37.2 -19.8168430 + 38 -2.7065118 + 39 -8.7288138 + 39.1 -9.2746473 + 39.2 -18.2695344 + 39.3 -13.8219083 + 39.4 -16.2254704 + 39.5 -21.7283648 + 40 1.8291916 + 40.1 -6.6916432 + 40.2 -1.6278171 + 40.3 -10.5749790 + 41 -3.1556121 + 41.1 -11.5895327 + 41.2 -18.9352091 + 41.3 -15.9788960 + 41.4 -9.6070508 + 42 -5.2159485 + 42.1 -15.9878743 + 43 -16.6104361 + 43.1 -9.5549441 + 43.2 -14.2003491 + 44 -8.1969033 + 44.1 -19.9270197 + 44.2 -22.6521171 + 44.3 -21.1903736 + 45 -0.5686627 + 45.1 -7.5645740 + 46 -19.1624789 + 46.1 -18.4487574 + 46.2 -15.8222682 + 47 -5.4165074 + 47.1 -15.0975029 + 47.2 -12.9971413 + 47.3 -10.6844521 + 47.4 -18.2214784 + 48 -8.3101471 + 48.1 -18.3854275 + 49 -13.0130319 + 50 -10.4579977 + 51 -19.3157621 + 52 -4.4747188 + 52.1 -4.3163827 + 52.2 -6.9761408 + 52.3 -20.1764756 + 52.4 -8.9036692 + 52.5 -5.6949642 + 53 -10.3141887 + 53.1 -8.2642654 + 53.2 -9.1691554 + 54 -6.2198754 + 54.1 -15.7192609 + 54.2 -13.0978998 + 54.3 -5.1195299 + 54.4 -16.5771751 + 55 -5.7348534 + 55.1 -7.3217494 + 55.2 -12.2171938 + 55.3 -12.9821266 + 55.4 -14.8599983 + 56 -14.1764282 + 56.1 -12.5343602 + 56.2 -8.4573382 + 56.3 -12.4633969 + 56.4 -17.3841863 + 56.5 -14.8147645 + 57 -3.1403293 + 57.1 -11.1509248 + 57.2 -6.3940143 + 57.3 -9.3473241 + 58 -12.0245677 + 58.1 -9.2112246 + 58.2 -1.2071742 + 58.3 -11.0141711 + 58.4 -5.3721214 + 58.5 -7.8523047 + 59 -13.2946560 + 59.1 -10.0530648 + 60 -19.2209402 + 61 -4.6699914 + 61.1 -3.5981894 + 61.2 -1.4713611 + 61.3 -3.8819786 + 61.4 0.1041413 + 62 -2.8591600 + 62.1 -6.9461986 + 62.2 -16.7910593 + 62.3 -17.9844596 + 63 -24.0335535 + 63.1 -11.7765300 + 64 -20.5963897 + 65 -2.7969169 + 65.1 -11.1778694 + 65.2 -5.2830399 + 65.3 -7.9353390 + 66 -13.2318328 + 66.1 -1.9090560 + 66.2 -16.6643889 + 67 -25.6073277 + 68 -13.4806759 + 68.1 -18.4557183 + 68.2 -13.3982327 + 68.3 -12.4977127 + 68.4 -11.7073990 + 69 -14.5290675 + 70 -15.2122709 + 70.1 -7.8681167 + 71 -10.3352703 + 71.1 -7.5699888 + 71.2 -18.4680702 + 71.3 -21.4316644 + 71.4 -8.1137650 + 72 -9.1848162 + 72.1 -23.7538846 + 72.2 -26.3421306 + 72.3 -27.2843801 + 72.4 -20.8541617 + 72.5 -12.8948965 + 73 -2.6091307 + 74 -8.2790175 + 75 -12.5029612 + 76 -6.0061671 + 76.1 -8.8149114 + 76.2 -11.8359043 + 77 0.4772521 + 78 -9.4105229 + 79 -1.0217265 + 79.1 -11.8125257 + 79.2 -10.5465186 + 80 -12.7366807 + 80.1 -9.0584783 + 80.2 -16.6381566 + 81 0.5547913 + 81.1 -4.0892715 + 81.2 1.8283303 + 81.3 -5.2166381 + 82 -3.0749381 + 82.1 -10.5506696 + 82.2 -18.2226347 + 83 -12.5872635 + 83.1 -11.9756502 + 83.2 -10.6744217 + 83.3 -19.2714012 + 84 -2.6320312 + 84.1 -9.8140094 + 85 -12.3886736 + 85.1 -12.9196365 + 85.2 -9.6433248 + 85.3 -6.3296340 + 85.4 -7.0405525 + 85.5 -13.6714939 + 86 -10.8756412 + 86.1 -12.0055331 + 86.2 -13.3724699 + 86.3 -13.3252145 + 86.4 -14.9191290 + 86.5 -17.7515546 + 87 -10.7027963 + 87.1 -22.4941954 + 87.2 -14.9616716 + 88 -2.2264493 + 88.1 -8.9626474 + 88.2 -2.5095281 + 88.3 -16.3345673 + 89 -11.0459647 + 90 -4.5610239 + 90.1 -11.7036651 + 90.2 -5.3838521 + 90.3 -4.1636999 + 91 -7.1462503 + 91.1 -12.8374475 + 91.2 -18.2576707 + 92 -6.4119222 + 93 5.2122168 + 93.1 3.1211725 + 93.2 -3.6841177 + 93.3 2.6223542 + 93.4 -11.1877696 + 94 -6.9602492 + 94.1 -7.4318416 + 94.2 -4.3498045 + 94.3 -11.6340088 + 94.4 -12.9357964 + 94.5 -14.7648530 + 95 -12.8849309 + 95.1 -9.7451502 + 95.2 -0.8535063 + 96 -4.9139832 + 96.1 -3.9582653 + 96.2 -9.6555492 + 96.3 -11.8690793 + 96.4 -11.0224373 + 96.5 -10.9530403 + 97 -9.8540471 + 97.1 -19.2262840 + 98 -11.9651231 + 98.1 -2.6515128 + 98.2 -12.2606382 + 99 -11.4720500 + 99.1 -14.0596866 + 99.2 -17.3939469 + 100 1.1005874 + 100.1 -3.8226248 + 100.2 -0.9123182 + 100.3 -15.8389474 + 100.4 -12.8093826 + + $m0a1$mu_reg_norm + [1] 0 + + $m0a1$tau_reg_norm + [1] 1e-04 + + $m0a1$shape_tau_norm + [1] 0.01 + + $m0a1$rate_tau_norm + [1] 0.01 + + $m0a1$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m0a1$shape_diag_RinvD + [1] "0.01" + + $m0a1$rate_diag_RinvD + [1] "0.001" + + + $m0a2 + $m0a2$M_id + (Intercept) + 1 1 + 2 1 + 3 1 + 4 1 + 5 1 + 6 1 + 7 1 + 8 1 + 9 1 + 10 1 + 11 1 + 12 1 + 13 1 + 14 1 + 15 1 + 16 1 + 17 1 + 18 1 + 19 1 + 20 1 + 21 1 + 22 1 + 23 1 + 24 1 + 25 1 + 26 1 + 27 1 + 28 1 + 29 1 + 30 1 + 31 1 + 32 1 + 33 1 + 34 1 + 35 1 + 36 1 + 37 1 + 38 1 + 39 1 + 40 1 + 41 1 + 42 1 + 43 1 + 44 1 + 45 1 + 46 1 + 47 1 + 48 1 + 49 1 + 50 1 + 51 1 + 52 1 + 53 1 + 54 1 + 55 1 + 56 1 + 57 1 + 58 1 + 59 1 + 60 1 + 61 1 + 62 1 + 63 1 + 64 1 + 65 1 + 66 1 + 67 1 + 68 1 + 69 1 + 70 1 + 71 1 + 72 1 + 73 1 + 74 1 + 75 1 + 76 1 + 77 1 + 78 1 + 79 1 + 80 1 + 81 1 + 82 1 + 83 1 + 84 1 + 85 1 + 86 1 + 87 1 + 88 1 + 89 1 + 90 1 + 91 1 + 92 1 + 93 1 + 94 1 + 95 1 + 96 1 + 97 1 + 98 1 + 99 1 + 100 1 + + $m0a2$M_lvlone + y + 1 -13.0493856 + 1.1 -9.3335901 + 1.2 -22.3469852 + 1.3 -15.0417337 + 2 -12.0655434 + 2.1 -15.8674476 + 2.2 -7.8800006 + 3 -11.4820604 + 3.1 -10.5983220 + 3.2 -22.4519157 + 4 -1.2697775 + 4.1 -11.1215184 + 4.2 -3.6134138 + 4.3 -14.5982385 + 5 -6.8457515 + 5.1 -7.0551214 + 5.2 -12.3418980 + 5.3 -9.2366906 + 6 -5.1648211 + 7 -10.0599502 + 7.1 -18.3267285 + 7.2 -12.5138426 + 8 -1.6305331 + 8.1 -9.6520453 + 8.2 -1.5278462 + 8.3 -7.4172211 + 8.4 -7.1238609 + 8.5 -8.8706950 + 9 -0.1634429 + 9.1 -2.6034300 + 9.2 -6.7272369 + 10 -6.4172202 + 10.1 -11.4834569 + 11 -8.7911356 + 11.1 -19.6645080 + 11.2 -20.2030932 + 11.3 -21.3082176 + 11.4 -14.5802901 + 12 -15.2006287 + 13 0.8058816 + 13.1 -13.6379208 + 14 -15.3422873 + 14.1 -10.0965208 + 14.2 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[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m3e$shape_diag_RinvD + [1] "0.01" + + $m3e$rate_diag_RinvD + [1] "0.001" + + + $m3f + $m3f$M_id + C2 (Intercept) + 1 -1.381594459 1 + 2 0.344426024 1 + 3 NA 1 + 4 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4.587570e-09 + 26.1 2.394334e-06 + 26.2 4.510972e-08 + 26.3 3.657318e-11 + 27 NA + 27.1 8.874134e-06 + 28 3.673907e-06 + 28.1 4.541426e-04 + 28.2 2.697966e-12 + 28.3 NA + 29 3.282475e-03 + 29.1 2.270717e-01 + 29.2 9.981536e-03 + 29.3 2.343590e-02 + 30 NA + 30.1 1.591483e-07 + 30.2 1.896944e-11 + 31 5.546285e-08 + 32 9.411981e-09 + 32.1 1.270914e-08 + 32.2 3.910478e-09 + 32.3 9.124048e-10 + 33 9.056156e-01 + 33.1 3.047254e-06 + 34 1.040462e-04 + 34.1 5.714390e-12 + 34.2 7.883166e-09 + 34.3 3.055823e-07 + 35 1.287796e-07 + 35.1 1.762232e-06 + 35.2 5.355159e-08 + 36 7.250797e-06 + 36.1 2.370652e-06 + 36.2 1.537090e-05 + 36.3 6.993214e-07 + 36.4 4.950009e-05 + 37 2.755165e-07 + 37.1 3.400517e-07 + 37.2 2.489007e-09 + 38 1.302651e-01 + 39 4.343746e-04 + 39.1 6.653143e-05 + 39.2 1.940204e-09 + 39.3 8.300468e-07 + 39.4 7.464169e-08 + 39.5 5.765597e-10 + 40 9.140572e-01 + 40.1 1.883555e-03 + 40.2 2.303001e-01 + 40.3 2.799910e-05 + 41 3.700067e-02 + 41.1 5.798225e-06 + 41.2 1.086252e-08 + 41.3 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91.1 8.870147e-06 + 91.2 1.652965e-08 + 92 2.613551e-03 + 93 9.958480e-01 + 93.1 9.915375e-01 + 93.2 4.861680e-02 + 93.3 9.769008e-01 + 93.4 5.977439e-05 + 94 7.091952e-04 + 94.1 6.005522e-04 + 94.2 8.134430e-03 + 94.3 1.747604e-05 + 94.4 9.404259e-07 + 94.5 6.832077e-07 + 95 3.216011e-06 + 95.1 6.324477e-05 + 95.2 1.762187e-01 + 96 1.578796e-02 + 96.1 2.610661e-02 + 96.2 3.941700e-05 + 96.3 1.683671e-05 + 96.4 1.095127e-04 + 96.5 1.479105e-05 + 97 2.082560e-04 + 97.1 7.903013e-10 + 98 1.795949e-06 + 98.1 2.776600e-02 + 98.2 4.050457e-06 + 99 2.316802e-05 + 99.1 2.206426e-06 + 99.2 2.488411e-08 + 100 7.572193e-01 + 100.1 9.794641e-02 + 100.2 4.934595e-01 + 100.3 1.502083e-07 + 100.4 2.515993e-06 + + $m3f$spM_id + center scale + C2 -0.6240921 0.6857108 + (Intercept) NA NA + + $m3f$mu_reg_norm + [1] 0 + + $m3f$tau_reg_norm + [1] 1e-04 + + $m3f$shape_tau_norm + [1] 0.01 + + $m3f$rate_tau_norm + [1] 0.01 + + $m3f$mu_reg_beta + [1] 0 + + $m3f$tau_reg_beta + [1] 1e-04 + + $m3f$shape_tau_beta 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2.5339403 1.3818094 + I(time^2) 8.3244468 7.0900029 + + $m6b$mu_reg_norm + [1] 0 + + $m6b$tau_reg_norm + [1] 1e-04 + + $m6b$shape_tau_norm + [1] 0.01 + + $m6b$rate_tau_norm + [1] 0.01 + + $m6b$mu_reg_binom + [1] 0 + + $m6b$tau_reg_binom + [1] 1e-04 + + $m6b$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m6b$shape_diag_RinvD + [1] "0.01" + + $m6b$rate_diag_RinvD + [1] "0.001" + + $m6b$RinvD_b1_id + [,1] [,2] + [1,] NA 0 + [2,] 0 NA + + $m6b$KinvD_b1_id + id + 3 + + + $m7a + $m7a$M_id + (Intercept) + 1 1 + 2 1 + 3 1 + 4 1 + 5 1 + 6 1 + 7 1 + 8 1 + 9 1 + 10 1 + 11 1 + 12 1 + 13 1 + 14 1 + 15 1 + 16 1 + 17 1 + 18 1 + 19 1 + 20 1 + 21 1 + 22 1 + 23 1 + 24 1 + 25 1 + 26 1 + 27 1 + 28 1 + 29 1 + 30 1 + 31 1 + 32 1 + 33 1 + 34 1 + 35 1 + 36 1 + 37 1 + 38 1 + 39 1 + 40 1 + 41 1 + 42 1 + 43 1 + 44 1 + 45 1 + 46 1 + 47 1 + 48 1 + 49 1 + 50 1 + 51 1 + 52 1 + 53 1 + 54 1 + 55 1 + 56 1 + 57 1 + 58 1 + 59 1 + 60 1 + 61 1 + 62 1 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17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m9b$shape_diag_RinvD + [1] "0.01" + + 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+ 30 NA 1 0.7300717 1 + 31 NA 1 0.7550721 1 + 32 -1.466312154 1 0.7321898 1 + 33 -0.637352277 1 0.7306414 1 + 34 NA 1 0.7427216 1 + 35 NA 1 0.7193042 1 + 36 NA 1 0.7312888 0 + 37 NA 1 0.7100436 0 + 38 NA 1 0.7670184 1 + 39 0.006728205 1 0.7400449 1 + 40 NA 1 0.7397304 1 + 41 -1.663281353 1 0.7490966 1 + 42 0.161184794 1 0.7419274 1 + 43 0.457939180 1 0.7527810 1 + 44 -0.307070331 1 0.7408315 1 + 45 NA 1 0.7347550 0 + 46 -1.071668276 1 0.7332398 1 + 47 -0.814751321 1 0.7376481 0 + 48 -0.547630662 1 0.7346179 0 + 49 NA 1 0.7329402 1 + 50 -1.350213782 1 0.7260436 1 + 51 0.719054706 1 0.7242910 1 + 52 NA 1 0.7298067 0 + 53 -1.207130750 1 0.7254741 1 + 54 NA 1 0.7542067 1 + 55 -0.408600991 1 0.7389952 1 + 56 -0.271380529 1 0.7520638 1 + 57 -1.361925974 1 0.7219958 1 + 58 NA 1 0.7259632 1 + 59 NA 1 0.7458606 1 + 60 -0.323712205 1 0.7672421 1 + 61 NA 1 0.7257179 0 + 62 NA 1 0.7189892 1 + 63 -1.386906880 1 0.7333356 1 + 64 NA 1 0.7320243 1 + 65 NA 1 0.7477711 1 + 66 -0.565191691 1 0.7343974 0 + 67 -0.382899912 1 0.7491624 0 + 68 NA 1 0.7482736 1 + 69 -0.405642769 1 0.7338267 1 + 70 NA 1 0.7607742 1 + 71 -0.843748427 1 0.7777600 1 + 72 0.116003683 1 0.7408143 1 + 73 -0.778634325 1 0.7248271 1 + 74 NA 1 0.7364916 0 + 75 NA 1 0.7464926 1 + 76 NA 1 0.7355430 1 + 77 -0.632974758 1 0.7208449 1 + 78 NA 1 0.7373573 1 + 79 -0.778064615 1 0.7598079 1 + 80 NA 1 0.7360415 1 + 81 NA 1 0.7293932 1 + 82 -0.246123253 1 0.7279309 1 + 83 -1.239659782 1 0.7344643 0 + 84 -0.467772280 1 0.7384350 0 + 85 NA 1 0.7323716 1 + 86 -2.160485036 1 0.7576597 1 + 87 -0.657675572 1 0.7496139 1 + 88 NA 1 0.7275239 1 + 89 -0.696710744 1 0.7250648 1 + 90 NA 1 0.7335262 0 + 91 -0.179395847 1 0.7343980 1 + 92 -0.441545568 1 0.7380425 1 + 93 -0.685799334 1 0.7389460 0 + 94 NA 1 0.7259951 1 + 95 0.191929445 1 0.7282840 0 + 96 NA 1 0.7281676 0 + 97 -0.069760671 1 0.7245642 1 + 98 NA 1 0.7526938 1 + 99 NA 1 0.7272309 1 + 100 NA 1 0.7383460 1 + + $m9c$M_lvlone + y + 1 -13.0493856 + 1.1 -9.3335901 + 1.2 -22.3469852 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-19.7302351 + 19 -14.6177568 + 19.1 -17.8043866 + 19.2 -15.1641705 + 19.3 -16.6898418 + 20 -12.9059229 + 20.1 -16.8191201 + 20.2 -6.1010131 + 20.3 -7.9415371 + 20.4 -9.3904458 + 20.5 -13.3504189 + 21 -7.6974718 + 21.1 -11.9335526 + 21.2 -12.7064929 + 22 -21.5022909 + 22.1 -12.7745451 + 23 -3.5146508 + 23.1 -4.6724048 + 24 -2.5619821 + 25 -6.2944970 + 25.1 -3.8630505 + 25.2 -14.4205140 + 25.3 -19.6735037 + 25.4 -9.0288933 + 25.5 -9.0509738 + 26 -19.7340685 + 26.1 -14.1692728 + 26.2 -17.2819976 + 26.3 -24.6265576 + 27 -7.3354999 + 27.1 -11.1488468 + 28 -11.7996597 + 28.1 -8.2030122 + 28.2 -26.4317815 + 28.3 -18.5016071 + 29 -5.8551395 + 29.1 -2.0209442 + 29.2 -5.6368080 + 29.3 -3.8110961 + 30 -12.7217702 + 30.1 -17.0170140 + 30.2 -25.4236089 + 31 -17.0783921 + 32 -18.4338764 + 32.1 -19.4317212 + 32.2 -19.4738978 + 32.3 -21.4922645 + 33 2.0838099 + 33.1 -13.3172274 + 34 -10.0296691 + 34.1 -25.9426553 + 34.2 -18.5688138 + 34.3 -15.4173859 + 35 -14.3958113 + 35.1 -12.9457541 + 35.2 -16.1380691 + 36 -12.8166968 + 36.1 -14.3989481 + 36.2 -12.2436943 + 36.3 -15.0104638 + 36.4 -10.1775457 + 37 -15.2223495 + 37.1 -14.7526195 + 37.2 -19.8168430 + 38 -2.7065118 + 39 -8.7288138 + 39.1 -9.2746473 + 39.2 -18.2695344 + 39.3 -13.8219083 + 39.4 -16.2254704 + 39.5 -21.7283648 + 40 1.8291916 + 40.1 -6.6916432 + 40.2 -1.6278171 + 40.3 -10.5749790 + 41 -3.1556121 + 41.1 -11.5895327 + 41.2 -18.9352091 + 41.3 -15.9788960 + 41.4 -9.6070508 + 42 -5.2159485 + 42.1 -15.9878743 + 43 -16.6104361 + 43.1 -9.5549441 + 43.2 -14.2003491 + 44 -8.1969033 + 44.1 -19.9270197 + 44.2 -22.6521171 + 44.3 -21.1903736 + 45 -0.5686627 + 45.1 -7.5645740 + 46 -19.1624789 + 46.1 -18.4487574 + 46.2 -15.8222682 + 47 -5.4165074 + 47.1 -15.0975029 + 47.2 -12.9971413 + 47.3 -10.6844521 + 47.4 -18.2214784 + 48 -8.3101471 + 48.1 -18.3854275 + 49 -13.0130319 + 50 -10.4579977 + 51 -19.3157621 + 52 -4.4747188 + 52.1 -4.3163827 + 52.2 -6.9761408 + 52.3 -20.1764756 + 52.4 -8.9036692 + 52.5 -5.6949642 + 53 -10.3141887 + 53.1 -8.2642654 + 53.2 -9.1691554 + 54 -6.2198754 + 54.1 -15.7192609 + 54.2 -13.0978998 + 54.3 -5.1195299 + 54.4 -16.5771751 + 55 -5.7348534 + 55.1 -7.3217494 + 55.2 -12.2171938 + 55.3 -12.9821266 + 55.4 -14.8599983 + 56 -14.1764282 + 56.1 -12.5343602 + 56.2 -8.4573382 + 56.3 -12.4633969 + 56.4 -17.3841863 + 56.5 -14.8147645 + 57 -3.1403293 + 57.1 -11.1509248 + 57.2 -6.3940143 + 57.3 -9.3473241 + 58 -12.0245677 + 58.1 -9.2112246 + 58.2 -1.2071742 + 58.3 -11.0141711 + 58.4 -5.3721214 + 58.5 -7.8523047 + 59 -13.2946560 + 59.1 -10.0530648 + 60 -19.2209402 + 61 -4.6699914 + 61.1 -3.5981894 + 61.2 -1.4713611 + 61.3 -3.8819786 + 61.4 0.1041413 + 62 -2.8591600 + 62.1 -6.9461986 + 62.2 -16.7910593 + 62.3 -17.9844596 + 63 -24.0335535 + 63.1 -11.7765300 + 64 -20.5963897 + 65 -2.7969169 + 65.1 -11.1778694 + 65.2 -5.2830399 + 65.3 -7.9353390 + 66 -13.2318328 + 66.1 -1.9090560 + 66.2 -16.6643889 + 67 -25.6073277 + 68 -13.4806759 + 68.1 -18.4557183 + 68.2 -13.3982327 + 68.3 -12.4977127 + 68.4 -11.7073990 + 69 -14.5290675 + 70 -15.2122709 + 70.1 -7.8681167 + 71 -10.3352703 + 71.1 -7.5699888 + 71.2 -18.4680702 + 71.3 -21.4316644 + 71.4 -8.1137650 + 72 -9.1848162 + 72.1 -23.7538846 + 72.2 -26.3421306 + 72.3 -27.2843801 + 72.4 -20.8541617 + 72.5 -12.8948965 + 73 -2.6091307 + 74 -8.2790175 + 75 -12.5029612 + 76 -6.0061671 + 76.1 -8.8149114 + 76.2 -11.8359043 + 77 0.4772521 + 78 -9.4105229 + 79 -1.0217265 + 79.1 -11.8125257 + 79.2 -10.5465186 + 80 -12.7366807 + 80.1 -9.0584783 + 80.2 -16.6381566 + 81 0.5547913 + 81.1 -4.0892715 + 81.2 1.8283303 + 81.3 -5.2166381 + 82 -3.0749381 + 82.1 -10.5506696 + 82.2 -18.2226347 + 83 -12.5872635 + 83.1 -11.9756502 + 83.2 -10.6744217 + 83.3 -19.2714012 + 84 -2.6320312 + 84.1 -9.8140094 + 85 -12.3886736 + 85.1 -12.9196365 + 85.2 -9.6433248 + 85.3 -6.3296340 + 85.4 -7.0405525 + 85.5 -13.6714939 + 86 -10.8756412 + 86.1 -12.0055331 + 86.2 -13.3724699 + 86.3 -13.3252145 + 86.4 -14.9191290 + 86.5 -17.7515546 + 87 -10.7027963 + 87.1 -22.4941954 + 87.2 -14.9616716 + 88 -2.2264493 + 88.1 -8.9626474 + 88.2 -2.5095281 + 88.3 -16.3345673 + 89 -11.0459647 + 90 -4.5610239 + 90.1 -11.7036651 + 90.2 -5.3838521 + 90.3 -4.1636999 + 91 -7.1462503 + 91.1 -12.8374475 + 91.2 -18.2576707 + 92 -6.4119222 + 93 5.2122168 + 93.1 3.1211725 + 93.2 -3.6841177 + 93.3 2.6223542 + 93.4 -11.1877696 + 94 -6.9602492 + 94.1 -7.4318416 + 94.2 -4.3498045 + 94.3 -11.6340088 + 94.4 -12.9357964 + 94.5 -14.7648530 + 95 -12.8849309 + 95.1 -9.7451502 + 95.2 -0.8535063 + 96 -4.9139832 + 96.1 -3.9582653 + 96.2 -9.6555492 + 96.3 -11.8690793 + 96.4 -11.0224373 + 96.5 -10.9530403 + 97 -9.8540471 + 97.1 -19.2262840 + 98 -11.9651231 + 98.1 -2.6515128 + 98.2 -12.2606382 + 99 -11.4720500 + 99.1 -14.0596866 + 99.2 -17.3939469 + 100 1.1005874 + 100.1 -3.8226248 + 100.2 -0.9123182 + 100.3 -15.8389474 + 100.4 -12.8093826 + + $m9c$spM_id + center scale + C2 -0.6240921 0.68571078 + (Intercept) NA NA + C1 0.7372814 0.01472882 + B11 NA NA + + $m9c$mu_reg_norm + [1] 0 + + $m9c$tau_reg_norm + [1] 1e-04 + + $m9c$shape_tau_norm + [1] 0.01 + + $m9c$rate_tau_norm + [1] 0.01 + + $m9c$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m9c$shape_diag_RinvD + [1] "0.01" + + $m9c$rate_diag_RinvD + [1] "0.001" + + + +# jagsmodel remains the same + + Code + lapply(models, "[[", "jagsmodel") + Output + $m0a1 + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } + $m0a2 + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } + $m0a3 + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + log(mu_y[i]) <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } + $m0a4 + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) + inv_mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } + $m0b1 + model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } + $m0b2 + model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + probit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } + $m0b3 + model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + log(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } + $m0b4 + model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + log(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } + $m0c1 + model { + + # Gamma mixed effects model for L1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } + $m0c2 + model { + + # Gamma mixed effects model for L1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + log(mu_L1[i]) <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } + $m0d1 + model { + + # Poisson mixed effects model for p1 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) + log(mu_p1[i]) <- b_p1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) + mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for p1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) + } + $m0d2 + model { + + # Poisson mixed effects model for p1 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) + mu_p1[i] <- b_p1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) + mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for p1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) + } + $m0e1 + model { + + # Log-normal mixed effects model for L1 ----------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } + $m0f1 + model { + + # Beta mixed effects model for Be1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ]) + mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for Be1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1]) + } + $m1a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for y + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } + $m1b + model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for b1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } + $m1c + model { + + # Gamma mixed effects model for L1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for L1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } + $m1d + model { + + # Poisson mixed effects model for p1 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) + log(mu_p1[i]) <- b_p1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) + mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for p1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) + } + $m1e + model { + + # Log-normal mixed effects model for L1 ----------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for L1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } + $m1f + model { + + # Beta mixed effects model for Be1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ]) + mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for Be1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1]) + } + $m2a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m2b + model { + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m2c + model { + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1mis + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m2d + model { + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i])) + log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for p2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m2e + model { + + # Log-normal mixed effects model for L1mis -------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1mis + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m2f + model { + + # Beta mixed effects model for Be2 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for Be2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m3a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3b + model { + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for b2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3c + model { + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for L1mis + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3d + model { + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i])) + log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for p2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3e + model { + + # Log-normal mixed effects model for L1mis -------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for L1mis + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3f + model { + + # Beta mixed effects model for Be2 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for Be2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m4a + model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + } + + # Priors for the model for c1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) + log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + } + + # Priors for the model for p2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + M_id[ii, 3] * alpha[7] + } + + # Priors for the model for c2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + + alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[10] + M_id[ii, 3] * alpha[11] + } + + # Priors for the model for L1mis + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Beta mixed effects model for Be2 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[13] + M_id[ii, 3] * alpha[14] + } + + # Priors for the model for Be2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[15] + + M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m4b + model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[3] * M_lvlone[i, 6] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) + mu_p2[i] <- b_p2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[3] * M_lvlone[i, 6] + + alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for p2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + probit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5] + } + + # Priors for the model for b2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- 1/max(1e-10, inv_mu_c2[i]) + inv_mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] + } + + # Priors for the model for c2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Log-normal mixed effects model for L1mis -------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10] + } + + # Priors for the model for L1mis + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + } + $m4c + model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[3] * M_lvlone[i, 6] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) + mu_p2[i] <- b_p2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[3] * M_lvlone[i, 6] + + alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for p2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + log(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5] + } + + # Priors for the model for b2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) + log(mu_c2[i]) <- b_c2_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] + } + + # Priors for the model for c2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10] + } + + # Priors for the model for L1mis + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + } + $m4d + model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[3] * M_lvlone[i, 7] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_beta[k]) + tau_reg_norm_ridge_beta[k] ~ dgamma(0.01, 0.01) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) + mu_p2[i] <- b_p2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[3] * M_lvlone[i, 7] + + alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + alpha[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for p2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson_ridge_alpha[k]) + tau_reg_poisson_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + log(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[8] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + alpha[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[6] + } + + # Priors for the model for b2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_alpha[k]) + tau_reg_binom_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) + log(mu_c2[i]) <- b_c2_id[group_id[i], 1] + + alpha[11] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + alpha[12] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[10] + } + + # Priors for the model for c2 + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] + + alpha[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[13] + } + + # Priors for the model for L1mis + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k]) + tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal mixed effects model for Be2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 6] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1) + mu_Be2[i] <- b_Be2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * alpha[15] + } + + # Priors for the model for Be2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_Be2 <- sqrt(1/tau_Be2) + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + } + $m5a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[6] * M_lvlone[i, 5] + beta[7] * M_lvlone[i, 6] + + beta[8] * M_lvlone[i, 7] + + beta[9] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + beta[11] * (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] + + beta[12] * (M_lvlone[i, 10] - spM_lvlone[10, 1])/spM_lvlone[10, 2] + + beta[13] * (M_lvlone[i, 11] - spM_lvlone[11, 1])/spM_lvlone[11, 2] + + beta[14] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + + M_id[ii, 4] * beta[3] + M_id[ii, 5] * beta[4] + + (M_id[ii, 6] - spM_id[6, 1])/spM_id[6, 2] * beta[5] + mu_b_y_id[ii, 2] <- beta[10] + } + + # Priors for the model for y + for (k in 1:14) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[6] * M_lvlone[i, 5] + + alpha[7] * M_lvlone[i, 6] + alpha[8] * M_lvlone[i, 7] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + + M_lvlone[i, 8] <- abs(M_id[group_id[i], 7] - M_lvlone[i, 2]) + + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + + M_id[ii, 4] * alpha[3] + M_id[ii, 5] * alpha[4] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[5] + } + + # Priors for the model for c2 + for (k in 1:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + alpha[14] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- M_id[ii, 3] * alpha[10] + M_id[ii, 4] * alpha[11] + + M_id[ii, 5] * alpha[12] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[13] + } + + + + # Priors for the model for o2 + for (k in 10:14) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[15] + M_id[ii, 3] * alpha[16] + + M_id[ii, 4] * alpha[17] + M_id[ii, 5] * alpha[18] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[19] + } + + # Priors for the model for time + for (k in 15:19) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 2] * alpha[20] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[21] + log(phi_M2[ii, 3]) <- M_id[ii, 2] * alpha[22] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[23] + log(phi_M2[ii, 4]) <- M_id[ii, 2] * alpha[24] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[25] + + M_id[ii, 3] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 20:25) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 10] <- M_lvlone[i, 5] * M_lvlone[i, 8] + M_lvlone[i, 11] <- M_lvlone[i, 6] * M_lvlone[i, 8] + M_lvlone[i, 12] <- M_lvlone[i, 7] * M_lvlone[i, 8] + } + + } + $m5b + model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + + b_b1_id[group_id[i], 2] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + b_b1_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:3] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 2] * beta[1] + mu_b_b1_id[ii, 2] <- beta[5] + mu_b_b1_id[ii, 3] <- 0 + } + + # Priors for the model for b1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k]) + tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01) + } + + for (k in 1:3) { + RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_b1_id[1:3, 1:3] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id) + D_b1_id[1:3, 1:3] <- inverse(invD_b1_id[ , ]) + + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] + } + + # Priors for the model for L1mis + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k]) + tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Beta mixed effects model for Be2 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + + alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + + M_lvlone[i, 7] <- log(M_lvlone[i, 3]) + + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] + } + + # Priors for the model for Be2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta_ridge_alpha[k]) + tau_reg_beta_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + + M_lvlone[i, 6] <- abs(M_lvlone[i, 4] - M_id[group_id[i], 1]) + + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[11] + } + + # Priors for the model for c1 + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[14] + } + + # Priors for the model for time + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + log(mu_C2[ii]) <- M_id[ii, 2] * alpha[15] + + + + } + + # Priors for the model for C2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m6a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[3] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[4] * M_lvlone[i, 3] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- beta[5] + } + + # Priors for the model for y + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + } + + # Priors for the model for b2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + } + + # Priors for the model for C2 + for (k in 5:6) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m6b + model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_b1_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[3] * M_id[group_id[i], 3] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:2] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- beta[5] + mu_b_b1_id[ii, 2] <- 0 + } + + # Priors for the model for b1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k]) + tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01) + } + + for (k in 1:2) { + RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_b1_id[1:2, 1:2] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id) + D_b1_id[1:2, 1:2] <- inverse(invD_b1_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] + + M_id[ii, 3] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[6] + + M_id[ii, 3] * alpha[7] + } + + # Priors for the model for time + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9] + } + + # Priors for the model for C2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m7a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[2] + mu_b_y_id[ii, 3] <- beta[3] + } + + # Priors for the model for y + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + } + $m7b + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[2] + mu_b_y_id[ii, 3] <- beta[3] + mu_b_y_id[ii, 4] <- beta[4] + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:4) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) + } + $m7c + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + mu_b_y_id[ii, 2] <- beta[4] + mu_b_y_id[ii, 3] <- beta[5] + mu_b_y_id[ii, 4] <- beta[6] + } + + # Priors for the model for y + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:4) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) + } + $m7d + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + mu_b_y_id[ii, 2] <- 0 + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] + } + + # Priors for the model for time + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + } + + # Priors for the model for C2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m7e + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + mu_b_y_id[ii, 2] <- beta[5] + mu_b_y_id[ii, 3] <- beta[6] + mu_b_y_id[ii, 4] <- beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:4) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + } + + # Priors for the model for C2 + for (k in 5:6) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m7f + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + mu_b_y_id[ii, 2] <- 0 + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] + } + + # Priors for the model for time + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + } + + # Priors for the model for C2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m8a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[4] + mu_b_y_id[ii, 3] <- beta[3] + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m8b + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[4] + mu_b_y_id[ii, 3] <- beta[3] + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m8c + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + mu_b_y_id[ii, 2] <- beta[5] + mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6] + } + + # Priors for the model for y + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + } + + # Priors for the model for c2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6] + } + + # Priors for the model for c1 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + + M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 8:8) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3] + } + + } + $m8d + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + mu_b_y_id[ii, 2] <- beta[5] + mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6] + } + + # Priors for the model for y + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + } + + # Priors for the model for c2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6] + } + + # Priors for the model for c1 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9] + } + + # Priors for the model for time + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + + M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3] + } + + } + $m8e + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for time + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] + } + + } + $m8f + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 10:11) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] + } + + } + $m8g + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 6:7) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] + } + + } + $m8h + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for time + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + } + + } + $m8i + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 10:11) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + } + + } + $m8j + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for time + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + } + + } + $m8k + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for time + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + } + + } + $m8l + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[5] + M_id[ii, 4] * beta[7] + mu_b_y_id[ii, 3] <- 0 + } + + # Priors for the model for y + for (k in 1:9) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + M_id[ii, 4] * alpha[7] + } + + # Priors for the model for time + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 4] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] + M_lvlone[i, 7] <- M_id[group_id[i], 4] * M_lvlone[i, 2] * M_lvlone[i, 3] + } + + } + $m8m + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + b_y_id[group_id[i], 2] * M_lvlone[i, 3] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[4] * M_lvlone[i, 4] + beta[5] * M_lvlone[i, 5] + + beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[3] + } + + # Priors for the model for y + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + } + $m8n + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[6] * M_lvlone[i, 5] + } + + for (ii in 1:100) { + b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[2] + mu_b_y_id[ii, 3] <- beta[5] + mu_b_y_id[ii, 4] <- beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:4) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * M_lvlone[i, 5] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for time + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for b1 + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m9a + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + b_y_o1[group_o1[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[3] * M_lvlone[i, 3] + + beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + for (iii in 1:3) { + b_y_o1[iii, 1:1] ~ dnorm(mu_b_y_o1[iii, ], invD_y_o1[ , ]) + mu_b_y_o1[iii, 1] <- 0 + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + invD_y_o1[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_o1[1, 1] <- 1 / (invD_y_o1[1, 1]) + } + $m9b + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + + M_id[ii, 4] * beta[4] + mu_b_y_id[ii, 2] <- beta[5] + } + + # Priors for the model for y + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + + M_id[ii, 4] * alpha[4] + } + + # Priors for the model for time + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + M_id[ii, 4] * alpha[7] + } + + # Priors for the model for C2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m9c + model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + + M_id[ii, 4] * beta[4] + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for C2 + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + +# GRcrit and MCerror give same result + + Code + lapply(models0, GR_crit, multivariate = FALSE) + Output + $m0a1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m0a2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m0a3 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m0a4 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m0b1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + D_b1_id[1,1] NaN NaN + + + $m0b2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + D_b1_id[1,1] NaN NaN + + + $m0b3 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + D_b1_id[1,1] NaN NaN + + + $m0b4 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + D_b1_id[1,1] NaN NaN + + + $m0c1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_L1 NaN NaN + D_L1_id[1,1] NaN NaN + + + $m0c2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_L1 NaN NaN + D_L1_id[1,1] NaN NaN + + + $m0d1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + D_p1_id[1,1] NaN NaN + + + $m0d2 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + D_p1_id[1,1] NaN NaN + + + $m0e1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + sigma_L1 NaN NaN + D_L1_id[1,1] NaN NaN + + + $m0f1 + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + tau_Be1 NaN NaN + D_Be1_id[1,1] NaN NaN + + + $m1a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m1b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + D_b1_id[1,1] NaN NaN + + + $m1c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + sigma_L1 NaN NaN + D_L1_id[1,1] NaN NaN + + + $m1d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + D_p1_id[1,1] NaN NaN + + + $m1e + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + sigma_L1 NaN NaN + D_L1_id[1,1] NaN NaN + + + $m1f + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + tau_Be1 NaN NaN + D_Be1_id[1,1] NaN NaN + + + $m2a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m2b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + D_b2_id[1,1] NaN NaN + + + $m2c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + sigma_L1mis NaN NaN + D_L1mis_id[1,1] NaN NaN + + + $m2d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + D_p2_id[1,1] NaN NaN + + + $m2e + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + sigma_L1mis NaN NaN + D_L1mis_id[1,1] NaN NaN + + + $m2f + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + tau_Be2 NaN NaN + D_Be2_id[1,1] NaN NaN + + + $m3a + Potential scale reduction factors: + + Point est. Upper C.I. + C2 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m3b + Potential scale reduction factors: + + Point est. Upper C.I. + C2 NaN NaN + D_b2_id[1,1] NaN NaN + + + $m3c + Potential scale reduction factors: + + Point est. Upper C.I. + C2 NaN NaN + sigma_L1mis NaN NaN + D_L1mis_id[1,1] NaN NaN + + + $m3d + Potential scale reduction factors: + + Point est. Upper C.I. + C2 NaN NaN + D_p2_id[1,1] NaN NaN + + + $m3e + Potential scale reduction factors: + + Point est. Upper C.I. + C2 NaN NaN + sigma_L1mis NaN NaN + D_L1mis_id[1,1] NaN NaN + + + $m3f + Potential scale reduction factors: + + Point est. Upper C.I. + C2 NaN NaN + tau_Be2 NaN NaN + D_Be2_id[1,1] NaN NaN + + + $m4a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + B21 NaN NaN + c2 NaN NaN + p2 NaN NaN + L1mis NaN NaN + Be2 NaN NaN + sigma_c1 NaN NaN + D_c1_id[1,1] NaN NaN + + + $m4b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + b21 NaN NaN + p2 NaN NaN + L1mis NaN NaN + sigma_c1 NaN NaN + D_c1_id[1,1] NaN NaN + + + $m4c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + b21 NaN NaN + p2 NaN NaN + L1mis NaN NaN + sigma_c1 NaN NaN + D_c1_id[1,1] NaN NaN + + + $m4d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c2 NaN NaN + b21 NaN NaN + p2 NaN NaN + L1mis NaN NaN + Be2 NaN NaN + sigma_c1 NaN NaN + D_c1_id[1,1] NaN NaN + + + $m5a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + log(C1) NaN NaN + o22 NaN NaN + o23 NaN NaN + o24 NaN NaN + abs(C1 - c2) NaN NaN + time NaN NaN + I(time^2) NaN NaN + o22:abs(C1 - c2) NaN NaN + o23:abs(C1 - c2) NaN NaN + o24:abs(C1 - c2) NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + + + $m5b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + L1mis NaN NaN + abs(c1 - C2) NaN NaN + log(Be2) NaN NaN + time NaN NaN + D_b1_id[1,1] NaN NaN + D_b1_id[1,2] NaN NaN + D_b1_id[2,2] NaN NaN + D_b1_id[1,3] NaN NaN + D_b1_id[2,3] NaN NaN + D_b1_id[3,3] NaN NaN + + + $m6a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + C2 NaN NaN + b21 NaN NaN + time NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + $m6b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C2 NaN NaN + B11 NaN NaN + c1 NaN NaN + time NaN NaN + D_b1_id[1,1] NaN NaN + D_b1_id[1,2] NaN NaN + D_b1_id[2,2] NaN NaN + + + $m7a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + ns(time, df = 2)1 NaN NaN + ns(time, df = 2)2 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m7b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + bs(time, df = 3)1 NaN NaN + bs(time, df = 3)2 NaN NaN + bs(time, df = 3)3 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + D_y_id[1,4] NaN NaN + D_y_id[2,4] NaN NaN + D_y_id[3,4] NaN NaN + D_y_id[4,4] NaN NaN + + + $m7c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + c1 NaN NaN + ns(time, df = 3)1 NaN NaN + ns(time, df = 3)2 NaN NaN + ns(time, df = 3)3 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + D_y_id[1,4] NaN NaN + D_y_id[2,4] NaN NaN + D_y_id[3,4] NaN NaN + D_y_id[4,4] NaN NaN + + + $m7d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + C2 NaN NaN + c1 NaN NaN + ns(time, df = 3)1 NaN NaN + ns(time, df = 3)2 NaN NaN + ns(time, df = 3)3 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + + + $m7e + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + C2 NaN NaN + c1 NaN NaN + ns(time, df = 3)1 NaN NaN + ns(time, df = 3)2 NaN NaN + ns(time, df = 3)3 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + D_y_id[1,4] NaN NaN + D_y_id[2,4] NaN NaN + D_y_id[3,4] NaN NaN + D_y_id[4,4] NaN NaN + + + $m7f + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + C2 NaN NaN + c1 NaN NaN + ns(time, df = 3)1 NaN NaN + ns(time, df = 3)2 NaN NaN + ns(time, df = 3)3 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + + + $m8a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c1 NaN NaN + c2 NaN NaN + time NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c1 NaN NaN + c2 NaN NaN + time NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + B21 NaN NaN + c1 NaN NaN + c2 NaN NaN + time NaN NaN + B21:c1 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8d + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + B21 NaN NaN + c1 NaN NaN + c2 NaN NaN + time NaN NaN + B21:c1 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8e + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c1 NaN NaN + c2 NaN NaN + time NaN NaN + B21:c1 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8f + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c1 NaN NaN + c2 NaN NaN + time NaN NaN + B21:c1 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8g + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c1 NaN NaN + c2 NaN NaN + time NaN NaN + B21:c1 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8h + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c2 NaN NaN + c1 NaN NaN + time NaN NaN + B21:c2 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8i + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c2 NaN NaN + c1 NaN NaN + time NaN NaN + B21:c2 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8j + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c2 NaN NaN + c1 NaN NaN + time NaN NaN + B21:c2 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8k + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c2 NaN NaN + c1 NaN NaN + time NaN NaN + B21:c2 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8l + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c1 NaN NaN + time NaN NaN + B21:c1 NaN NaN + B21:time NaN NaN + c1:time NaN NaN + B21:c1:time NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + + + $m8m + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c1 NaN NaN + b11 NaN NaN + o1.L NaN NaN + o1.Q NaN NaN + c1:b11 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + + + $m8n + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + B21 NaN NaN + c1 NaN NaN + time NaN NaN + b11 NaN NaN + C1:time NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + D_y_id[1,3] NaN NaN + D_y_id[2,3] NaN NaN + D_y_id[3,3] NaN NaN + D_y_id[1,4] NaN NaN + D_y_id[2,4] NaN NaN + D_y_id[3,4] NaN NaN + D_y_id[4,4] NaN NaN + + + $m9a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + c1 NaN NaN + b11 NaN NaN + time NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_o1[1,1] NaN NaN + + + $m9b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + C2 NaN NaN + B11 NaN NaN + time NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + D_y_id[1,2] NaN NaN + D_y_id[2,2] NaN NaN + + + $m9c + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + C1 NaN NaN + C2 NaN NaN + B11 NaN NaN + sigma_y NaN NaN + D_y_id[1,1] NaN NaN + + + +--- + + Code + lapply(models0, MC_error) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m0a2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m0a3 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m0a4 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m0b1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + D_b1_id[1,1] 0 0 0 NaN + + $m0b2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + D_b1_id[1,1] 0 0 0 NaN + + $m0b3 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + D_b1_id[1,1] 0 0 0 NaN + + $m0b4 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + D_b1_id[1,1] 0 0 0 NaN + + $m0c1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_L1 0 0 0 NaN + D_L1_id[1,1] 0 0 0 NaN + + $m0c2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_L1 0 0 0 NaN + D_L1_id[1,1] 0 0 0 NaN + + $m0d1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + D_p1_id[1,1] 0 0 0 NaN + + $m0d2 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + D_p1_id[1,1] 0 0 0 NaN + + $m0e1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + sigma_L1 0 0 0 NaN + D_L1_id[1,1] 0 0 0 NaN + + $m0f1 + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + tau_Be1 0 0 0 NaN + D_Be1_id[1,1] 0 0 0 NaN + + $m1a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m1b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + D_b1_id[1,1] 0 0 0 NaN + + $m1c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + sigma_L1 0 0 0 NaN + D_L1_id[1,1] 0 0 0 NaN + + $m1d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + D_p1_id[1,1] 0 0 0 NaN + + $m1e + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + sigma_L1 0 0 0 NaN + D_L1_id[1,1] 0 0 0 NaN + + $m1f + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + tau_Be1 0 0 0 NaN + D_Be1_id[1,1] 0 0 0 NaN + + $m2a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m2b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + D_b2_id[1,1] 0 0 0 NaN + + $m2c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + sigma_L1mis 0 0 0 NaN + D_L1mis_id[1,1] 0 0 0 NaN + + $m2d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + D_p2_id[1,1] 0 0 0 NaN + + $m2e + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + sigma_L1mis 0 0 0 NaN + D_L1mis_id[1,1] 0 0 0 NaN + + $m2f + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + tau_Be2 0 0 0 NaN + D_Be2_id[1,1] 0 0 0 NaN + + $m3a + est MCSE SD MCSE/SD + C2 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m3b + est MCSE SD MCSE/SD + C2 0 0 0 NaN + D_b2_id[1,1] 0 0 0 NaN + + $m3c + est MCSE SD MCSE/SD + C2 0 0 0 NaN + sigma_L1mis 0 0 0 NaN + D_L1mis_id[1,1] 0 0 0 NaN + + $m3d + est MCSE SD MCSE/SD + C2 0 0 0 NaN + D_p2_id[1,1] 0 0 0 NaN + + $m3e + est MCSE SD MCSE/SD + C2 0 0 0 NaN + sigma_L1mis 0 0 0 NaN + D_L1mis_id[1,1] 0 0 0 NaN + + $m3f + est MCSE SD MCSE/SD + C2 0 0 0 NaN + tau_Be2 0 0 0 NaN + D_Be2_id[1,1] 0 0 0 NaN + + $m4a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + B21 0 0 0 NaN + c2 0 0 0 NaN + p2 0 0 0 NaN + L1mis 0 0 0 NaN + Be2 0 0 0 NaN + sigma_c1 0 0 0 NaN + D_c1_id[1,1] 0 0 0 NaN + + $m4b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + b21 0 0 0 NaN + p2 0 0 0 NaN + L1mis 0 0 0 NaN + sigma_c1 0 0 0 NaN + D_c1_id[1,1] 0 0 0 NaN + + $m4c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + b21 0 0 0 NaN + p2 0 0 0 NaN + L1mis 0 0 0 NaN + sigma_c1 0 0 0 NaN + D_c1_id[1,1] 0 0 0 NaN + + $m4d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c2 0 0 0 NaN + b21 0 0 0 NaN + p2 0 0 0 NaN + L1mis 0 0 0 NaN + Be2 0 0 0 NaN + sigma_c1 0 0 0 NaN + D_c1_id[1,1] 0 0 0 NaN + + $m5a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + log(C1) 0 0 0 NaN + o22 0 0 0 NaN + o23 0 0 0 NaN + o24 0 0 0 NaN + abs(C1 - c2) 0 0 0 NaN + time 0 0 0 NaN + I(time^2) 0 0 0 NaN + o22:abs(C1 - c2) 0 0 0 NaN + o23:abs(C1 - c2) 0 0 0 NaN + o24:abs(C1 - c2) 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + + $m5b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + L1mis 0 0 0 NaN + abs(c1 - C2) 0 0 0 NaN + log(Be2) 0 0 0 NaN + time 0 0 0 NaN + D_b1_id[1,1] 0 0 0 NaN + D_b1_id[1,2] 0 0 0 NaN + D_b1_id[2,2] 0 0 0 NaN + D_b1_id[1,3] 0 0 0 NaN + D_b1_id[2,3] 0 0 0 NaN + D_b1_id[3,3] 0 0 0 NaN + + $m6a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + C2 0 0 0 NaN + b21 0 0 0 NaN + time 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + $m6b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C2 0 0 0 NaN + B11 0 0 0 NaN + c1 0 0 0 NaN + time 0 0 0 NaN + D_b1_id[1,1] 0 0 0 NaN + D_b1_id[1,2] 0 0 0 NaN + D_b1_id[2,2] 0 0 0 NaN + + $m7a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + ns(time, df = 2)1 0 0 0 NaN + ns(time, df = 2)2 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m7b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + bs(time, df = 3)1 0 0 0 NaN + bs(time, df = 3)2 0 0 0 NaN + bs(time, df = 3)3 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + D_y_id[1,4] 0 0 0 NaN + D_y_id[2,4] 0 0 0 NaN + D_y_id[3,4] 0 0 0 NaN + D_y_id[4,4] 0 0 0 NaN + + $m7c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + c1 0 0 0 NaN + ns(time, df = 3)1 0 0 0 NaN + ns(time, df = 3)2 0 0 0 NaN + ns(time, df = 3)3 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + D_y_id[1,4] 0 0 0 NaN + D_y_id[2,4] 0 0 0 NaN + D_y_id[3,4] 0 0 0 NaN + D_y_id[4,4] 0 0 0 NaN + + $m7d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + C2 0 0 0 NaN + c1 0 0 0 NaN + ns(time, df = 3)1 0 0 0 NaN + ns(time, df = 3)2 0 0 0 NaN + ns(time, df = 3)3 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + + $m7e + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + C2 0 0 0 NaN + c1 0 0 0 NaN + ns(time, df = 3)1 0 0 0 NaN + ns(time, df = 3)2 0 0 0 NaN + ns(time, df = 3)3 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + D_y_id[1,4] 0 0 0 NaN + D_y_id[2,4] 0 0 0 NaN + D_y_id[3,4] 0 0 0 NaN + D_y_id[4,4] 0 0 0 NaN + + $m7f + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + C2 0 0 0 NaN + c1 0 0 0 NaN + ns(time, df = 3)1 0 0 0 NaN + ns(time, df = 3)2 0 0 0 NaN + ns(time, df = 3)3 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + + $m8a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c1 0 0 0 NaN + c2 0 0 0 NaN + time 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c1 0 0 0 NaN + c2 0 0 0 NaN + time 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + B21 0 0 0 NaN + c1 0 0 0 NaN + c2 0 0 0 NaN + time 0 0 0 NaN + B21:c1 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8d + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + B21 0 0 0 NaN + c1 0 0 0 NaN + c2 0 0 0 NaN + time 0 0 0 NaN + B21:c1 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8e + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c1 0 0 0 NaN + c2 0 0 0 NaN + time 0 0 0 NaN + B21:c1 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8f + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c1 0 0 0 NaN + c2 0 0 0 NaN + time 0 0 0 NaN + B21:c1 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8g + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c1 0 0 0 NaN + c2 0 0 0 NaN + time 0 0 0 NaN + B21:c1 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8h + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c2 0 0 0 NaN + c1 0 0 0 NaN + time 0 0 0 NaN + B21:c2 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8i + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c2 0 0 0 NaN + c1 0 0 0 NaN + time 0 0 0 NaN + B21:c2 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8j + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c2 0 0 0 NaN + c1 0 0 0 NaN + time 0 0 0 NaN + B21:c2 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8k + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c2 0 0 0 NaN + c1 0 0 0 NaN + time 0 0 0 NaN + B21:c2 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8l + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c1 0 0 0 NaN + time 0 0 0 NaN + B21:c1 0 0 0 NaN + B21:time 0 0 0 NaN + c1:time 0 0 0 NaN + B21:c1:time 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + + $m8m + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c1 0 0 0 NaN + b11 0 0 0 NaN + o1.L 0 0 0 NaN + o1.Q 0 0 0 NaN + c1:b11 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + + $m8n + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + B21 0 0 0 NaN + c1 0 0 0 NaN + time 0 0 0 NaN + b11 0 0 0 NaN + C1:time 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + D_y_id[1,3] 0 0 0 NaN + D_y_id[2,3] 0 0 0 NaN + D_y_id[3,3] 0 0 0 NaN + D_y_id[1,4] 0 0 0 NaN + D_y_id[2,4] 0 0 0 NaN + D_y_id[3,4] 0 0 0 NaN + D_y_id[4,4] 0 0 0 NaN + + $m9a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + c1 0 0 0 NaN + b11 0 0 0 NaN + time 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_o1[1,1] 0 0 0 NaN + + $m9b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + C2 0 0 0 NaN + B11 0 0 0 NaN + time 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + D_y_id[1,2] 0 0 0 NaN + D_y_id[2,2] 0 0 0 NaN + + $m9c + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + C1 0 0 0 NaN + C2 0 0 0 NaN + B11 0 0 0 NaN + sigma_y 0 0 0 NaN + D_y_id[1,1] 0 0 0 NaN + + +# summary output remained the same + + Code + lapply(models0, print) + Output + + Call: + lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), + n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), + n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + Call: + glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + p1 + (Intercept) + p1 (Intercept) 0 + + + Call: + glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + p1 + (Intercept) + p1 (Intercept) 0 + + + Call: + lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal mixed model for "L1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta mixed model for "Be1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + Be1 + (Intercept) + Be1 (Intercept) 0 + + + Call: + lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + Call: + glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + p1 + (Intercept) + p1 (Intercept) 0 + + + Call: + lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal mixed model for "L1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + Call: + betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta mixed model for "Be1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + Be1 + (Intercept) + Be1 (Intercept) 0 + + + Call: + lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b2" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + b2 + (Intercept) + b2 (Intercept) 0 + + + Call: + glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1mis" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + L1mis + (Intercept) + L1mis (Intercept) 0 + + + + Residual standard deviation: + sigma_L1mis + 0 + + Call: + glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p2" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + p2 + (Intercept) + p2 (Intercept) 0 + + + Call: + lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal mixed model for "L1mis" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + L1mis + (Intercept) + L1mis (Intercept) 0 + + + + Residual standard deviation: + sigma_L1mis + 0 + + Call: + betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta mixed model for "Be2" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + Be2 + (Intercept) + Be2 (Intercept) 0 + + + Call: + lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b2" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + b2 + (Intercept) + b2 (Intercept) 0 + + + Call: + glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1mis" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + L1mis + (Intercept) + L1mis (Intercept) 0 + + + + Residual standard deviation: + sigma_L1mis + 0 + + Call: + glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p2" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + p2 + (Intercept) + p2 (Intercept) 0 + + + Call: + lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal mixed model for "L1mis" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + L1mis + (Intercept) + L1mis (Intercept) 0 + + + + Residual standard deviation: + sigma_L1mis + 0 + + Call: + betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta mixed model for "Be2" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + Be2 + (Intercept) + Be2 (Intercept) 0 + + + Call: + lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", + L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) B21 c2 p2 L1mis Be2 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", + p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", + L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) c2 b21 p2 L1mis + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", + p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", + b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) c2 b21 p2 L1mis + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", + p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", + b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, + warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1))) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) c2 b21 p2 L1mis Be2 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + Call: + lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + + I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) M22 M23 M24 + 0 0 0 0 + log(C1) o22 o23 o24 + 0 0 0 0 + abs(C1 - c2) time I(time^2) o22:abs(C1 - c2) + 0 0 0 0 + o23:abs(C1 - c2) o24:abs(C1 - c2) + 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) time + y (Intercept) 0 0 + y time 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + + (time + I(time^2) | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) L1mis abs(c1 - C2) log(Be2) time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + b1 b1 b1 + (Intercept) time I(time^2) + b1 (Intercept) 0 0 0 + b1 time 0 0 0 + b1 I(time^2) 0 0 0 + + + Call: + lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, + n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 b21 time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y + time + y time 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | + id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, + shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) C2 B11 c1 time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + b1 b1 + time I(time^2) + b1 time 0 0 + b1 I(time^2) 0 0 + + + Call: + lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, + df = 2) | id, n.iter = 10, seed = 2020, adapt = 5) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 + 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 + y (Intercept) 0 0 0 + y ns(time, df = 2)1 0 0 0 + y ns(time, df = 2)2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, + df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 + 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y y + (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 + y (Intercept) 0 0 0 0 + y bs(time, df = 3)1 0 0 0 0 + y bs(time, df = 3)2 0 0 0 0 + y bs(time, df = 3)3 0 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, + random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, + nadapt = 5) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 c1 ns(time, df = 3)1 + 0 0 0 0 + ns(time, df = 3)2 ns(time, df = 3)3 + 0 0 + + + Random effects covariance matrix: + $id + y y y y + (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + y (Intercept) 0 0 0 0 + y ns(time, df = 3)1 0 0 0 0 + y ns(time, df = 3)2 0 0 0 0 + y ns(time, df = 3)3 0 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + 0 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) time + y (Intercept) 0 0 + y time 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, + no_model = "time", seed = 2020) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + 0 0 0 + + + Random effects covariance matrix: + $id + y y y y + (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + y (Intercept) 0 0 0 0 + y ns(time, df = 3)1 0 0 0 0 + y ns(time, df = 3)2 0 0 0 0 + y ns(time, df = 3)3 0 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + 0 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) time + y (Intercept) 0 0 + y time 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c1 c2 time + 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c1 c2 time + 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) B21 c1 c2 time B21:c1 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c1 + y (Intercept) 0 0 0 + y time 0 0 0 + y c1 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) B21 c1 c2 time B21:c1 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c1 + y (Intercept) 0 0 0 + y time 0 0 0 + y c1 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", + "c1"), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c1 + y (Intercept) 0 0 0 + y time 0 0 0 + y c1 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c1 + y (Intercept) 0 0 0 + y time 0 0 0 + y c1 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + + I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 time B21:c1 + 0 0 0 0 0 0 + B21:time c1:time B21:c1:time + 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time I(time^2) + y (Intercept) 0 0 0 + y time 0 0 0 + y I(time^2) 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | + id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c1 b11 o1.L o1.Q c1:b11 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) b11 + y (Intercept) 0 0 + y b11 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, + random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 time b11 + 0 0 0 0 0 0 + C1:time + 0 + + + Random effects covariance matrix: + $id + y y y y + (Intercept) C1 time C1:time + y (Intercept) 0 0 0 0 + y C1 0 0 0 0 + y time 0 0 0 0 + y C1:time 0 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c1 b11 time + 0 0 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + $o1 + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 B11 time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) time + y (Intercept) 0 0 + y time 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + Call: + lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, monitor_params = c(analysis_random = TRUE), + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 B11 + 0 0 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + $m0a1 + + Call: + lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m0a2 + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m0a3 + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m0a4 + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m0b1 + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + $m0b2 + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + $m0b3 + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), + n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + $m0b4 + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), + n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + $m0c1 + + Call: + glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + $m0c2 + + Call: + glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + $m0d1 + + Call: + glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + p1 + (Intercept) + p1 (Intercept) 0 + + + $m0d2 + + Call: + glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + p1 + (Intercept) + p1 (Intercept) 0 + + + $m0e1 + + Call: + lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal mixed model for "L1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + $m0f1 + + Call: + betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta mixed model for "Be1" + + Fixed effects: + (Intercept) + 0 + + + Random effects covariance matrix: + $id + Be1 + (Intercept) + Be1 (Intercept) 0 + + + $m1a + + Call: + lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m1b + + Call: + glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + b1 + (Intercept) + b1 (Intercept) 0 + + + $m1c + + Call: + glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + $m1d + + Call: + glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + p1 + (Intercept) + p1 (Intercept) 0 + + + $m1e + + Call: + lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal mixed model for "L1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + L1 + (Intercept) + L1 (Intercept) 0 + + + + Residual standard deviation: + sigma_L1 + 0 + + $m1f + + Call: + betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta mixed model for "Be1" + + Fixed effects: + (Intercept) C1 + 0 0 + + + Random effects covariance matrix: + $id + Be1 + (Intercept) + Be1 (Intercept) 0 + + + $m2a + + Call: + lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m2b + + Call: + glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b2" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + b2 + (Intercept) + b2 (Intercept) 0 + + + $m2c + + Call: + glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1mis" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + L1mis + (Intercept) + L1mis (Intercept) 0 + + + + Residual standard deviation: + sigma_L1mis + 0 + + $m2d + + Call: + glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p2" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + p2 + (Intercept) + p2 (Intercept) 0 + + + $m2e + + Call: + lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal mixed model for "L1mis" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + L1mis + (Intercept) + L1mis (Intercept) 0 + + + + Residual standard deviation: + sigma_L1mis + 0 + + $m2f + + Call: + betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta mixed model for "Be2" + + Fixed effects: + (Intercept) c2 + 0 0 + + + Random effects covariance matrix: + $id + Be2 + (Intercept) + Be2 (Intercept) 0 + + + $m3a + + Call: + lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m3b + + Call: + glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b2" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + b2 + (Intercept) + b2 (Intercept) 0 + + + $m3c + + Call: + glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian Gamma mixed model for "L1mis" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + L1mis + (Intercept) + L1mis (Intercept) 0 + + + + Residual standard deviation: + sigma_L1mis + 0 + + $m3d + + Call: + glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian poisson mixed model for "p2" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + p2 + (Intercept) + p2 (Intercept) 0 + + + $m3e + + Call: + lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian log-normal mixed model for "L1mis" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + L1mis + (Intercept) + L1mis (Intercept) 0 + + + + Residual standard deviation: + sigma_L1mis + 0 + + $m3f + + Call: + betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian beta mixed model for "Be2" + + Fixed effects: + C2 + 0 + + + Random effects covariance matrix: + $id + Be2 + (Intercept) + Be2 (Intercept) 0 + + + $m4a + + Call: + lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", + L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) B21 c2 p2 L1mis Be2 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + $m4b + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", + p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", + L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) c2 b21 p2 L1mis + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + $m4c + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", + p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", + b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) c2 b21 p2 L1mis + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + $m4d + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", + p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", + b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, + warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1))) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) c2 b21 p2 L1mis Be2 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + $m5a + + Call: + lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + + I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) M22 M23 M24 + 0 0 0 0 + log(C1) o22 o23 o24 + 0 0 0 0 + abs(C1 - c2) time I(time^2) o22:abs(C1 - c2) + 0 0 0 0 + o23:abs(C1 - c2) o24:abs(C1 - c2) + 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) time + y (Intercept) 0 0 + y time 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m5b + + Call: + glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + + (time + I(time^2) | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) L1mis abs(c1 - C2) log(Be2) time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + b1 b1 b1 + (Intercept) time I(time^2) + b1 (Intercept) 0 0 0 + b1 time 0 0 0 + b1 I(time^2) 0 0 0 + + + $m6a + + Call: + lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, + n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 b21 time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y + time + y time 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m6b + + Call: + glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | + id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, + shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian binomial mixed model for "b1" + + Fixed effects: + (Intercept) C2 B11 c1 time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + b1 b1 + time I(time^2) + b1 time 0 0 + b1 I(time^2) 0 0 + + + $m7a + + Call: + lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, + df = 2) | id, n.iter = 10, seed = 2020, adapt = 5) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 + 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 + y (Intercept) 0 0 0 + y ns(time, df = 2)1 0 0 0 + y ns(time, df = 2)2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m7b + + Call: + lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, + df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 + 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y y + (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 + y (Intercept) 0 0 0 0 + y bs(time, df = 3)1 0 0 0 0 + y bs(time, df = 3)2 0 0 0 0 + y bs(time, df = 3)3 0 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m7c + + Call: + lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, + random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, + nadapt = 5) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 c1 ns(time, df = 3)1 + 0 0 0 0 + ns(time, df = 3)2 ns(time, df = 3)3 + 0 0 + + + Random effects covariance matrix: + $id + y y y y + (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + y (Intercept) 0 0 0 0 + y ns(time, df = 3)1 0 0 0 0 + y ns(time, df = 3)2 0 0 0 0 + y ns(time, df = 3)3 0 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m7d + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + 0 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) time + y (Intercept) 0 0 + y time 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m7e + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, + no_model = "time", seed = 2020) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + 0 0 0 + + + Random effects covariance matrix: + $id + y y y y + (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + y (Intercept) 0 0 0 0 + y ns(time, df = 3)1 0 0 0 0 + y ns(time, df = 3)2 0 0 0 0 + y ns(time, df = 3)3 0 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m7f + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 + 0 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) time + y (Intercept) 0 0 + y time 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8a + + Call: + lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c1 c2 time + 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8b + + Call: + lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c1 c2 time + 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8c + + Call: + lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) B21 c1 c2 time B21:c1 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c1 + y (Intercept) 0 0 0 + y time 0 0 0 + y c1 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8d + + Call: + lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) B21 c1 c2 time B21:c1 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c1 + y (Intercept) 0 0 0 + y time 0 0 0 + y c1 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8e + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8f + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8g + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", + "c1"), seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8h + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c1 + y (Intercept) 0 0 0 + y time 0 0 0 + y c1 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8i + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c1 + y (Intercept) 0 0 0 + y time 0 0 0 + y c1 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8j + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8k + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 + 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time c2 + y (Intercept) 0 0 0 + y time 0 0 0 + y c2 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8l + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + + I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 time B21:c1 + 0 0 0 0 0 0 + B21:time c1:time B21:c1:time + 0 0 0 + + + Random effects covariance matrix: + $id + y y y + (Intercept) time I(time^2) + y (Intercept) 0 0 0 + y time 0 0 0 + y I(time^2) 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8m + + Call: + lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | + id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c1 b11 o1.L o1.Q c1:b11 + 0 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) b11 + y (Intercept) 0 0 + y b11 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m8n + + Call: + lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, + random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 B21 c1 time b11 + 0 0 0 0 0 0 + C1:time + 0 + + + Random effects covariance matrix: + $id + y y y y + (Intercept) C1 time C1:time + y (Intercept) 0 0 0 0 + y C1 0 0 0 0 + y time 0 0 0 0 + y C1:time 0 0 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m9a + + Call: + lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) c1 b11 time + 0 0 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + $o1 + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m9b + + Call: + lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 B11 time + 0 0 0 0 0 + + + Random effects covariance matrix: + $id + y y + (Intercept) time + y (Intercept) 0 0 + y time 0 0 + + + + Residual standard deviation: + sigma_y + 0 + + $m9c + + Call: + lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, monitor_params = c(analysis_random = TRUE), + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "y" + + Fixed effects: + (Intercept) C1 C2 B11 + 0 0 0 0 + + + Random effects covariance matrix: + $id + y + (Intercept) + y (Intercept) 0 + + + + Residual standard deviation: + sigma_y + 0 + + +--- + + Code + lapply(models0, coef) + Output + $m0a1 + $m0a1$y + (Intercept) sigma_y D_y_id[1,1] + 0 0 0 + + + $m0a2 + $m0a2$y + (Intercept) sigma_y D_y_id[1,1] + 0 0 0 + + + $m0a3 + $m0a3$y + (Intercept) sigma_y D_y_id[1,1] + 0 0 0 + + + $m0a4 + $m0a4$y + (Intercept) sigma_y D_y_id[1,1] + 0 0 0 + + + $m0b1 + $m0b1$b1 + (Intercept) D_b1_id[1,1] + 0 0 + + + $m0b2 + $m0b2$b1 + (Intercept) D_b1_id[1,1] + 0 0 + + + $m0b3 + $m0b3$b1 + (Intercept) D_b1_id[1,1] + 0 0 + + + $m0b4 + $m0b4$b1 + (Intercept) D_b1_id[1,1] + 0 0 + + + $m0c1 + $m0c1$L1 + (Intercept) sigma_L1 D_L1_id[1,1] + 0 0 0 + + + $m0c2 + $m0c2$L1 + (Intercept) sigma_L1 D_L1_id[1,1] + 0 0 0 + + + $m0d1 + $m0d1$p1 + (Intercept) D_p1_id[1,1] + 0 0 + + + $m0d2 + $m0d2$p1 + (Intercept) D_p1_id[1,1] + 0 0 + + + $m0e1 + $m0e1$L1 + (Intercept) sigma_L1 D_L1_id[1,1] + 0 0 0 + + + $m0f1 + $m0f1$Be1 + (Intercept) tau_Be1 D_Be1_id[1,1] + 0 0 0 + + + $m1a + $m1a$y + (Intercept) C1 sigma_y D_y_id[1,1] + 0 0 0 0 + + + $m1b + $m1b$b1 + (Intercept) C1 D_b1_id[1,1] + 0 0 0 + + + $m1c + $m1c$L1 + (Intercept) C1 sigma_L1 D_L1_id[1,1] + 0 0 0 0 + + + $m1d + $m1d$p1 + (Intercept) C1 D_p1_id[1,1] + 0 0 0 + + + $m1e + $m1e$L1 + (Intercept) C1 sigma_L1 D_L1_id[1,1] + 0 0 0 0 + + + $m1f + $m1f$Be1 + (Intercept) C1 tau_Be1 D_Be1_id[1,1] + 0 0 0 0 + + + $m2a + $m2a$y + (Intercept) c2 sigma_y D_y_id[1,1] + 0 0 0 0 + + + $m2b + $m2b$b2 + (Intercept) c2 D_b2_id[1,1] + 0 0 0 + + + $m2c + $m2c$L1mis + (Intercept) c2 sigma_L1mis D_L1mis_id[1,1] + 0 0 0 0 + + + $m2d + $m2d$p2 + (Intercept) c2 D_p2_id[1,1] + 0 0 0 + + + $m2e + $m2e$L1mis + (Intercept) c2 sigma_L1mis D_L1mis_id[1,1] + 0 0 0 0 + + + $m2f + $m2f$Be2 + (Intercept) c2 tau_Be2 D_Be2_id[1,1] + 0 0 0 0 + + + $m3a + $m3a$y + C2 sigma_y D_y_id[1,1] + 0 0 0 + + + $m3b + $m3b$b2 + C2 D_b2_id[1,1] + 0 0 + + + $m3c + $m3c$L1mis + C2 sigma_L1mis D_L1mis_id[1,1] + 0 0 0 + + + $m3d + $m3d$p2 + C2 D_p2_id[1,1] + 0 0 + + + $m3e + $m3e$L1mis + C2 sigma_L1mis D_L1mis_id[1,1] + 0 0 0 + + + $m3f + $m3f$Be2 + C2 tau_Be2 D_Be2_id[1,1] + 0 0 0 + + + $m4a + $m4a$c1 + (Intercept) B21 c2 p2 L1mis Be2 + 0 0 0 0 0 0 + sigma_c1 D_c1_id[1,1] + 0 0 + + + $m4b + $m4b$c1 + (Intercept) c2 b21 p2 L1mis sigma_c1 + 0 0 0 0 0 0 + D_c1_id[1,1] + 0 + + + $m4c + $m4c$c1 + (Intercept) c2 b21 p2 L1mis sigma_c1 + 0 0 0 0 0 0 + D_c1_id[1,1] + 0 + + + $m4d + $m4d$c1 + (Intercept) c2 b21 p2 L1mis Be2 + 0 0 0 0 0 0 + sigma_c1 D_c1_id[1,1] + 0 0 + + + $m5a + $m5a$y + (Intercept) M22 M23 M24 + 0 0 0 0 + log(C1) o22 o23 o24 + 0 0 0 0 + abs(C1 - c2) time I(time^2) o22:abs(C1 - c2) + 0 0 0 0 + o23:abs(C1 - c2) o24:abs(C1 - c2) sigma_y D_y_id[1,1] + 0 0 0 0 + D_y_id[1,2] D_y_id[2,2] + 0 0 + + + $m5b + $m5b$b1 + (Intercept) L1mis abs(c1 - C2) log(Be2) time D_b1_id[1,1] + 0 0 0 0 0 0 + D_b1_id[1,2] D_b1_id[2,2] D_b1_id[1,3] D_b1_id[2,3] D_b1_id[3,3] + 0 0 0 0 0 + + + $m6a + $m6a$y + (Intercept) C1 C2 b21 time sigma_y + 0 0 0 0 0 0 + D_y_id[1,1] + 0 + + + $m6b + $m6b$b1 + (Intercept) C2 B11 c1 time D_b1_id[1,1] + 0 0 0 0 0 0 + D_b1_id[1,2] D_b1_id[2,2] + 0 0 + + + $m7a + $m7a$y + (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 sigma_y + 0 0 0 0 + D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] + 0 0 + + + $m7b + $m7b$y + (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 + 0 0 0 0 + sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] + 0 0 0 0 + D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] + 0 0 0 0 + D_y_id[2,4] D_y_id[3,4] D_y_id[4,4] + 0 0 0 + + + $m7c + $m7c$y + (Intercept) C1 c1 ns(time, df = 3)1 + 0 0 0 0 + ns(time, df = 3)2 ns(time, df = 3)3 sigma_y D_y_id[1,1] + 0 0 0 0 + D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] + 0 0 0 0 + D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] D_y_id[3,4] + 0 0 0 0 + D_y_id[4,4] + 0 + + + $m7d + $m7d$y + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y + 0 0 0 0 + D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] + 0 0 0 + + + $m7e + $m7e$y + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y + 0 0 0 0 + D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] + 0 0 0 0 + D_y_id[3,4] D_y_id[4,4] + 0 0 + + + $m7f + $m7f$y + (Intercept) C1 C2 c1 + 0 0 0 0 + ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y + 0 0 0 0 + D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] + 0 0 0 + + + $m8a + $m8a$y + (Intercept) c1 c2 time sigma_y D_y_id[1,1] + 0 0 0 0 0 0 + D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] + 0 0 0 0 0 + + + $m8b + $m8b$y + (Intercept) c1 c2 time sigma_y D_y_id[1,1] + 0 0 0 0 0 0 + D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] + 0 0 0 0 0 + + + $m8c + $m8c$y + (Intercept) B21 c1 c2 time B21:c1 + 0 0 0 0 0 0 + sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] + 0 0 0 0 0 0 + D_y_id[3,3] + 0 + + + $m8d + $m8d$y + (Intercept) B21 c1 c2 time B21:c1 + 0 0 0 0 0 0 + sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] + 0 0 0 0 0 0 + D_y_id[3,3] + 0 + + + $m8e + $m8e$y + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] + 0 0 + + + $m8f + $m8f$y + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] + 0 0 + + + $m8g + $m8g$y + (Intercept) C1 B21 c1 c2 time + 0 0 0 0 0 0 + B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] + 0 0 + + + $m8h + $m8h$y + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] + 0 0 + + + $m8i + $m8i$y + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] + 0 0 + + + $m8j + $m8j$y + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] + 0 0 + + + $m8k + $m8k$y + (Intercept) C1 B21 c2 c1 time + 0 0 0 0 0 0 + B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] + 0 0 + + + $m8l + $m8l$y + (Intercept) C1 B21 c1 time B21:c1 + 0 0 0 0 0 0 + B21:time c1:time B21:c1:time sigma_y D_y_id[1,1] D_y_id[1,2] + 0 0 0 0 0 0 + D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] + 0 0 0 0 + + + $m8m + $m8m$y + (Intercept) c1 b11 o1.L o1.Q c1:b11 + 0 0 0 0 0 0 + sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] + 0 0 0 0 + + + $m8n + $m8n$y + (Intercept) C1 B21 c1 time b11 + 0 0 0 0 0 0 + C1:time sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] + 0 0 0 0 0 0 + D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] D_y_id[3,4] D_y_id[4,4] + 0 0 0 0 0 0 + + + $m9a + $m9a$y + (Intercept) c1 b11 time sigma_y D_y_id[1,1] + 0 0 0 0 0 0 + D_y_o1[1,1] + 0 + + + $m9b + $m9b$y + (Intercept) C1 C2 B11 time sigma_y + 0 0 0 0 0 0 + D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] + 0 0 0 + + + $m9c + $m9c$y + (Intercept) C1 C2 B11 sigma_y D_y_id[1,1] + 0 0 0 0 0 0 + + + +--- + + Code + lapply(models0, confint) + Output + $m0a1 + $m0a1$y + 2.5% 97.5% + (Intercept) 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m0a2 + $m0a2$y + 2.5% 97.5% + (Intercept) 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m0a3 + $m0a3$y + 2.5% 97.5% + (Intercept) 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m0a4 + $m0a4$y + 2.5% 97.5% + (Intercept) 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m0b1 + $m0b1$b1 + 2.5% 97.5% + (Intercept) 0 0 + D_b1_id[1,1] 0 0 + + + $m0b2 + $m0b2$b1 + 2.5% 97.5% + (Intercept) 0 0 + D_b1_id[1,1] 0 0 + + + $m0b3 + $m0b3$b1 + 2.5% 97.5% + (Intercept) 0 0 + D_b1_id[1,1] 0 0 + + + $m0b4 + $m0b4$b1 + 2.5% 97.5% + (Intercept) 0 0 + D_b1_id[1,1] 0 0 + + + $m0c1 + $m0c1$L1 + 2.5% 97.5% + (Intercept) 0 0 + sigma_L1 0 0 + D_L1_id[1,1] 0 0 + + + $m0c2 + $m0c2$L1 + 2.5% 97.5% + (Intercept) 0 0 + sigma_L1 0 0 + D_L1_id[1,1] 0 0 + + + $m0d1 + $m0d1$p1 + 2.5% 97.5% + (Intercept) 0 0 + D_p1_id[1,1] 0 0 + + + $m0d2 + $m0d2$p1 + 2.5% 97.5% + (Intercept) 0 0 + D_p1_id[1,1] 0 0 + + + $m0e1 + $m0e1$L1 + 2.5% 97.5% + (Intercept) 0 0 + sigma_L1 0 0 + D_L1_id[1,1] 0 0 + + + $m0f1 + $m0f1$Be1 + 2.5% 97.5% + (Intercept) 0 0 + tau_Be1 0 0 + D_Be1_id[1,1] 0 0 + + + $m1a + $m1a$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m1b + $m1b$b1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + D_b1_id[1,1] 0 0 + + + $m1c + $m1c$L1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + sigma_L1 0 0 + D_L1_id[1,1] 0 0 + + + $m1d + $m1d$p1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + D_p1_id[1,1] 0 0 + + + $m1e + $m1e$L1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + sigma_L1 0 0 + D_L1_id[1,1] 0 0 + + + $m1f + $m1f$Be1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + tau_Be1 0 0 + D_Be1_id[1,1] 0 0 + + + $m2a + $m2a$y + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m2b + $m2b$b2 + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + D_b2_id[1,1] 0 0 + + + $m2c + $m2c$L1mis + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + sigma_L1mis 0 0 + D_L1mis_id[1,1] 0 0 + + + $m2d + $m2d$p2 + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + D_p2_id[1,1] 0 0 + + + $m2e + $m2e$L1mis + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + sigma_L1mis 0 0 + D_L1mis_id[1,1] 0 0 + + + $m2f + $m2f$Be2 + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + tau_Be2 0 0 + D_Be2_id[1,1] 0 0 + + + $m3a + $m3a$y + 2.5% 97.5% + C2 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m3b + $m3b$b2 + 2.5% 97.5% + C2 0 0 + D_b2_id[1,1] 0 0 + + + $m3c + $m3c$L1mis + 2.5% 97.5% + C2 0 0 + sigma_L1mis 0 0 + D_L1mis_id[1,1] 0 0 + + + $m3d + $m3d$p2 + 2.5% 97.5% + C2 0 0 + D_p2_id[1,1] 0 0 + + + $m3e + $m3e$L1mis + 2.5% 97.5% + C2 0 0 + sigma_L1mis 0 0 + D_L1mis_id[1,1] 0 0 + + + $m3f + $m3f$Be2 + 2.5% 97.5% + C2 0 0 + tau_Be2 0 0 + D_Be2_id[1,1] 0 0 + + + $m4a + $m4a$c1 + 2.5% 97.5% + (Intercept) 0 0 + B21 0 0 + c2 0 0 + p2 0 0 + L1mis 0 0 + Be2 0 0 + sigma_c1 0 0 + D_c1_id[1,1] 0 0 + + + $m4b + $m4b$c1 + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + b21 0 0 + p2 0 0 + L1mis 0 0 + sigma_c1 0 0 + D_c1_id[1,1] 0 0 + + + $m4c + $m4c$c1 + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + b21 0 0 + p2 0 0 + L1mis 0 0 + sigma_c1 0 0 + D_c1_id[1,1] 0 0 + + + $m4d + $m4d$c1 + 2.5% 97.5% + (Intercept) 0 0 + c2 0 0 + b21 0 0 + p2 0 0 + L1mis 0 0 + Be2 0 0 + sigma_c1 0 0 + D_c1_id[1,1] 0 0 + + + $m5a + $m5a$y + 2.5% 97.5% + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + log(C1) 0 0 + o22 0 0 + o23 0 0 + o24 0 0 + abs(C1 - c2) 0 0 + time 0 0 + I(time^2) 0 0 + o22:abs(C1 - c2) 0 0 + o23:abs(C1 - c2) 0 0 + o24:abs(C1 - c2) 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + + + $m5b + $m5b$b1 + 2.5% 97.5% + (Intercept) 0 0 + L1mis 0 0 + abs(c1 - C2) 0 0 + log(Be2) 0 0 + time 0 0 + D_b1_id[1,1] 0 0 + D_b1_id[1,2] 0 0 + D_b1_id[2,2] 0 0 + D_b1_id[1,3] 0 0 + D_b1_id[2,3] 0 0 + D_b1_id[3,3] 0 0 + + + $m6a + $m6a$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + C2 0 0 + b21 0 0 + time 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + $m6b + $m6b$b1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + B11 0 0 + c1 0 0 + time 0 0 + D_b1_id[1,1] 0 0 + D_b1_id[1,2] 0 0 + D_b1_id[2,2] 0 0 + + + $m7a + $m7a$y + 2.5% 97.5% + (Intercept) 0 0 + ns(time, df = 2)1 0 0 + ns(time, df = 2)2 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m7b + $m7b$y + 2.5% 97.5% + (Intercept) 0 0 + bs(time, df = 3)1 0 0 + bs(time, df = 3)2 0 0 + bs(time, df = 3)3 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + D_y_id[1,4] 0 0 + D_y_id[2,4] 0 0 + D_y_id[3,4] 0 0 + D_y_id[4,4] 0 0 + + + $m7c + $m7c$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + c1 0 0 + ns(time, df = 3)1 0 0 + ns(time, df = 3)2 0 0 + ns(time, df = 3)3 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + D_y_id[1,4] 0 0 + D_y_id[2,4] 0 0 + D_y_id[3,4] 0 0 + D_y_id[4,4] 0 0 + + + $m7d + $m7d$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + C2 0 0 + c1 0 0 + ns(time, df = 3)1 0 0 + ns(time, df = 3)2 0 0 + ns(time, df = 3)3 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + + + $m7e + $m7e$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + C2 0 0 + c1 0 0 + ns(time, df = 3)1 0 0 + ns(time, df = 3)2 0 0 + ns(time, df = 3)3 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + D_y_id[1,4] 0 0 + D_y_id[2,4] 0 0 + D_y_id[3,4] 0 0 + D_y_id[4,4] 0 0 + + + $m7f + $m7f$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + C2 0 0 + c1 0 0 + ns(time, df = 3)1 0 0 + ns(time, df = 3)2 0 0 + ns(time, df = 3)3 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + + + $m8a + $m8a$y + 2.5% 97.5% + (Intercept) 0 0 + c1 0 0 + c2 0 0 + time 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8b + $m8b$y + 2.5% 97.5% + (Intercept) 0 0 + c1 0 0 + c2 0 0 + time 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8c + $m8c$y + 2.5% 97.5% + (Intercept) 0 0 + B21 0 0 + c1 0 0 + c2 0 0 + time 0 0 + B21:c1 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8d + $m8d$y + 2.5% 97.5% + (Intercept) 0 0 + B21 0 0 + c1 0 0 + c2 0 0 + time 0 0 + B21:c1 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8e + $m8e$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c1 0 0 + c2 0 0 + time 0 0 + B21:c1 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8f + $m8f$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c1 0 0 + c2 0 0 + time 0 0 + B21:c1 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8g + $m8g$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c1 0 0 + c2 0 0 + time 0 0 + B21:c1 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8h + $m8h$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c2 0 0 + c1 0 0 + time 0 0 + B21:c2 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8i + $m8i$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c2 0 0 + c1 0 0 + time 0 0 + B21:c2 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8j + $m8j$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c2 0 0 + c1 0 0 + time 0 0 + B21:c2 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8k + $m8k$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c2 0 0 + c1 0 0 + time 0 0 + B21:c2 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8l + $m8l$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c1 0 0 + time 0 0 + B21:c1 0 0 + B21:time 0 0 + c1:time 0 0 + B21:c1:time 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + + + $m8m + $m8m$y + 2.5% 97.5% + (Intercept) 0 0 + c1 0 0 + b11 0 0 + o1.L 0 0 + o1.Q 0 0 + c1:b11 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + + + $m8n + $m8n$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + c1 0 0 + time 0 0 + b11 0 0 + C1:time 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + D_y_id[1,3] 0 0 + D_y_id[2,3] 0 0 + D_y_id[3,3] 0 0 + D_y_id[1,4] 0 0 + D_y_id[2,4] 0 0 + D_y_id[3,4] 0 0 + D_y_id[4,4] 0 0 + + + $m9a + $m9a$y + 2.5% 97.5% + (Intercept) 0 0 + c1 0 0 + b11 0 0 + time 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_o1[1,1] 0 0 + + + $m9b + $m9b$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + C2 0 0 + B11 0 0 + time 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + D_y_id[1,2] 0 0 + D_y_id[2,2] 0 0 + + + $m9c + $m9c$y + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + C2 0 0 + B11 0 0 + sigma_y 0 0 + D_y_id[1,1] 0 0 + + + +--- + + Code + lapply(models0, summary, missinfo = TRUE) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a1 + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0a2 + + Bayesian linear mixed model fitted with JointAI + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0a3 + + Bayesian linear mixed model fitted with JointAI + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0a4 + + Bayesian linear mixed model fitted with JointAI + + Call: + glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0b1 + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + b1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0b2 + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + b1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0b3 + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), + n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 51:60 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + b1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0b4 + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), + n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 51:60 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + b1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0c1 + + Bayesian Gamma mixed model fitted with JointAI + + Call: + glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + L1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0c2 + + Bayesian Gamma mixed model fitted with JointAI + + Call: + glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + L1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0d1 + + Bayesian poisson mixed model fitted with JointAI + + Call: + glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_p1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + p1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0d2 + + Bayesian poisson mixed model fitted with JointAI + + Call: + glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_p1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + p1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0e1 + + Bayesian log-normal mixed model fitted with JointAI + + Call: + lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + L1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m0f1 + + Bayesian beta mixed model fitted with JointAI + + Call: + betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_Be1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + tau_Be1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + Be1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m1a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + + + $m1b + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + b1 lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + + + $m1c + + Bayesian Gamma mixed model fitted with JointAI + + Call: + glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + L1 lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + + + $m1d + + Bayesian poisson mixed model fitted with JointAI + + Call: + glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_p1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + p1 lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + + + $m1e + + Bayesian log-normal mixed model fitted with JointAI + + Call: + lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + L1 lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + + + $m1f + + Bayesian beta mixed model fitted with JointAI + + Call: + betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_Be1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + tau_Be1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + Be1 lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + + + $m2a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + id id 0 0 + + + $m2b + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 189 57.4 + + Number and proportion of missing values: + level # NA % NA + c2 lvlone 66 20.1 + b2 lvlone 99 30.1 + + level # NA % NA + id id 0 0 + + + $m2c + + Bayesian Gamma mixed model fitted with JointAI + + Call: + glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1mis_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1mis 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 246 74.8 + + Number and proportion of missing values: + level # NA % NA + L1mis lvlone 20 6.08 + c2 lvlone 66 20.06 + + level # NA % NA + id id 0 0 + + + $m2d + + Bayesian poisson mixed model fitted with JointAI + + Call: + glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_p2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 142 43.2 + + Number and proportion of missing values: + level # NA % NA + c2 lvlone 66 20.1 + p2 lvlone 162 49.2 + + level # NA % NA + id id 0 0 + + + $m2e + + Bayesian log-normal mixed model fitted with JointAI + + Call: + lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1mis_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1mis 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 246 74.8 + + Number and proportion of missing values: + level # NA % NA + L1mis lvlone 20 6.08 + c2 lvlone 66 20.06 + + level # NA % NA + id id 0 0 + + + $m2f + + Bayesian beta mixed model fitted with JointAI + + Call: + betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_Be2_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + tau_Be2 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 246 74.8 + + Number and proportion of missing values: + level # NA % NA + Be2 lvlone 20 6.08 + c2 lvlone 66 20.06 + + level # NA % NA + id id 0 0 + + + $m3a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + + level # NA % NA + id id 0 0 + C2 id 42 42 + + + $m3b + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58.0 + lvlone lvlone 230 69.9 + + Number and proportion of missing values: + level # NA % NA + b2 lvlone 99 30.1 + + level # NA % NA + id id 0 0 + C2 id 42 42 + + + $m3c + + Bayesian Gamma mixed model fitted with JointAI + + Call: + glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1mis_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1mis 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58.0 + lvlone lvlone 309 93.9 + + Number and proportion of missing values: + level # NA % NA + L1mis lvlone 20 6.08 + + level # NA % NA + id id 0 0 + C2 id 42 42 + + + $m3d + + Bayesian poisson mixed model fitted with JointAI + + Call: + glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_p2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58.0 + lvlone lvlone 167 50.8 + + Number and proportion of missing values: + level # NA % NA + p2 lvlone 162 49.2 + + level # NA % NA + id id 0 0 + C2 id 42 42 + + + $m3e + + Bayesian log-normal mixed model fitted with JointAI + + Call: + lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_L1mis_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_L1mis 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58.0 + lvlone lvlone 309 93.9 + + Number and proportion of missing values: + level # NA % NA + L1mis lvlone 20 6.08 + + level # NA % NA + id id 0 0 + C2 id 42 42 + + + $m3f + + Bayesian beta mixed model fitted with JointAI + + Call: + betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_Be2_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of other parameters: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + tau_Be2 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58.0 + lvlone lvlone 309 93.9 + + Number and proportion of missing values: + level # NA % NA + Be2 lvlone 20 6.08 + + level # NA % NA + id id 0 0 + C2 id 42 42 + + + $m4a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", + L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + p2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + Be2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_c1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_c1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90 + lvlone lvlone 125 38 + + Number and proportion of missing values: + level # NA % NA + c1 lvlone 0 0.00 + L1mis lvlone 20 6.08 + Be2 lvlone 20 6.08 + c2 lvlone 66 20.06 + p2 lvlone 162 49.24 + + level # NA % NA + id id 0 0 + B2 id 10 10 + + + $m4b + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", + p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", + L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + p2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_c1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_c1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 96 29.2 + + Number and proportion of missing values: + level # NA % NA + c1 lvlone 0 0.00 + L1mis lvlone 20 6.08 + c2 lvlone 66 20.06 + b2 lvlone 99 30.09 + p2 lvlone 162 49.24 + + level # NA % NA + id id 0 0 + + + $m4c + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", + p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", + b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + p2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_c1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_c1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 96 29.2 + + Number and proportion of missing values: + level # NA % NA + c1 lvlone 0 0.00 + L1mis lvlone 20 6.08 + c2 lvlone 66 20.06 + b2 lvlone 99 30.09 + p2 lvlone 162 49.24 + + level # NA % NA + id id 0 0 + + + $m4d + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", + p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", + b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, + warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1))) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + p2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + Be2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_c1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_c1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 92 28 + + Number and proportion of missing values: + level # NA % NA + c1 lvlone 0 0.00 + L1mis lvlone 20 6.08 + Be2 lvlone 20 6.08 + c2 lvlone 66 20.06 + b2 lvlone 99 30.09 + p2 lvlone 162 49.24 + + level # NA % NA + id id 0 0 + + + $m5a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + + I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + abs(C1 - c2) 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + I(time^2) 0 0 0 0 0 NaN NaN + o22:abs(C1 - c2) 0 0 0 0 0 NaN NaN + o23:abs(C1 - c2) 0 0 0 0 0 NaN NaN + o24:abs(C1 - c2) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 56 56 + lvlone lvlone 217 66 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + time lvlone 0 0.0 + o2 lvlone 59 17.9 + c2 lvlone 66 20.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + M2 id 44 44 + + + $m5b + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + + (time + I(time^2) | id), data = longDF, family = binomial(), + n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", + L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + abs(c1 - C2) 0 0 0 0 0 NaN NaN + log(Be2) 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b1_id[1,1] 0 0 0 0 NaN NaN + D_b1_id[1,2] 0 0 0 0 0 NaN NaN + D_b1_id[2,2] 0 0 0 0 NaN NaN + D_b1_id[1,3] 0 0 0 0 0 NaN NaN + D_b1_id[2,3] 0 0 0 0 0 NaN NaN + D_b1_id[3,3] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58.0 + lvlone lvlone 291 88.4 + + Number and proportion of missing values: + level # NA % NA + b1 lvlone 0 0.00 + c1 lvlone 0 0.00 + time lvlone 0 0.00 + L1mis lvlone 20 6.08 + Be2 lvlone 20 6.08 + + level # NA % NA + id id 0 0 + C2 id 42 42 + + + $m6a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, + n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58.0 + lvlone lvlone 230 69.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + time lvlone 0 0.0 + b2 lvlone 99 30.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + C2 id 42 42 + + + $m6b + + Bayesian binomial mixed model fitted with JointAI + + Call: + glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | + id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, + shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_b1_id[1,1] 0 0 0 0 NaN NaN + D_b1_id[1,2] 0 0 0 0 0 NaN NaN + D_b1_id[2,2] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + b1 lvlone 0 0 + c1 lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + B1 id 0 0 + id id 0 0 + C2 id 42 42 + + + $m7a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, + df = 2) | id, n.iter = 10, seed = 2020, adapt = 5) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + ns(time, df = 2)1 0 0 0 0 0 NaN NaN + ns(time, df = 2)2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 101:110 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m7b + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, + df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + bs(time, df = 3)1 0 0 0 0 0 NaN NaN + bs(time, df = 3)2 0 0 0 0 0 NaN NaN + bs(time, df = 3)3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + D_y_id[1,4] 0 0 0 0 0 NaN NaN + D_y_id[2,4] 0 0 0 0 0 NaN NaN + D_y_id[3,4] 0 0 0 0 0 NaN NaN + D_y_id[4,4] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m7c + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, + random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, + nadapt = 5) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + ns(time, df = 3)1 0 0 0 0 0 NaN NaN + ns(time, df = 3)2 0 0 0 0 0 NaN NaN + ns(time, df = 3)3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + D_y_id[1,4] 0 0 0 0 0 NaN NaN + D_y_id[2,4] 0 0 0 0 0 NaN NaN + D_y_id[3,4] 0 0 0 0 0 NaN NaN + D_y_id[4,4] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 101:110 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + c1 lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + + + $m7d + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + ns(time, df = 3)1 0 0 0 0 0 NaN NaN + ns(time, df = 3)2 0 0 0 0 0 NaN NaN + ns(time, df = 3)3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + c1 lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + C2 id 42 42 + + + $m7e + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, + no_model = "time", seed = 2020) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + ns(time, df = 3)1 0 0 0 0 0 NaN NaN + ns(time, df = 3)2 0 0 0 0 0 NaN NaN + ns(time, df = 3)3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + D_y_id[1,4] 0 0 0 0 0 NaN NaN + D_y_id[2,4] 0 0 0 0 0 NaN NaN + D_y_id[3,4] 0 0 0 0 0 NaN NaN + D_y_id[4,4] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + c1 lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + C2 id 42 42 + + + $m7f + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + ns(time, df = 3)1 0 0 0 0 0 NaN NaN + ns(time, df = 3)2 0 0 0 0 0 NaN NaN + ns(time, df = 3)3 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + c1 lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + C2 id 42 42 + + + $m8a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + Warning + + There are missing values in a variable for which a random effect is + specified ("c2"). It will not be possible to re-scale the random + effects "b_y_id" and their variance covariance matrix "D_y_id" back to + the original scale of the data. If you are not interested in the + estimated random effects or their (co)variances this is not a problem. + The fixed effects estimates are not affected by this. If you are + interested in the random effects or the (co)variances you need to + specify that "time" and "c2" are not scaled (using the argument + "scale_params"). + Output + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + id id 0 0 + + + $m8b + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + Warning + + There are missing values in a variable for which a random effect is + specified ("c2"). It will not be possible to re-scale the random + effects "b_y_id" and their variance covariance matrix "D_y_id" back to + the original scale of the data. If you are not interested in the + estimated random effects or their (co)variances this is not a problem. + The fixed effects estimates are not affected by this. If you are + interested in the random effects or the (co)variances you need to + specify that "time" and "c2" are not scaled (using the argument + "scale_params"). + Output + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + id id 0 0 + + + $m8c + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + id id 0 0 + B2 id 10 10 + + + $m8d + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + id id 0 0 + B2 id 10 10 + + + $m8e + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + Warning + + There are missing values in a variable for which a random effect is + specified ("c2"). It will not be possible to re-scale the random + effects "b_y_id" and their variance covariance matrix "D_y_id" back to + the original scale of the data. If you are not interested in the + estimated random effects or their (co)variances this is not a problem. + The fixed effects estimates are not affected by this. If you are + interested in the random effects or the (co)variances you need to + specify that "time" and "c2" are not scaled (using the argument + "scale_params"). + Output + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m8f + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + Warning + + There are missing values in a variable for which a random effect is + specified ("c2"). It will not be possible to re-scale the random + effects "b_y_id" and their variance covariance matrix "D_y_id" back to + the original scale of the data. If you are not interested in the + estimated random effects or their (co)variances this is not a problem. + The fixed effects estimates are not affected by this. If you are + interested in the random effects or the (co)variances you need to + specify that "time" and "c2" are not scaled (using the argument + "scale_params"). + Output + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m8g + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", + "c1"), seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + Warning + + There are missing values in a variable for which a random effect is + specified ("c2"). It will not be possible to re-scale the random + effects "b_y_id" and their variance covariance matrix "D_y_id" back to + the original scale of the data. If you are not interested in the + estimated random effects or their (co)variances this is not a problem. + The fixed effects estimates are not affected by this. If you are + interested in the random effects or the (co)variances you need to + specify that "time" and "c2" are not scaled (using the argument + "scale_params"). + Output + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m8h + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m8i + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m8j + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + Warning + + There are missing values in a variable for which a random effect is + specified ("c2"). It will not be possible to re-scale the random + effects "b_y_id" and their variance covariance matrix "D_y_id" back to + the original scale of the data. If you are not interested in the + estimated random effects or their (co)variances this is not a problem. + The fixed effects estimates are not affected by this. If you are + interested in the random effects or the (co)variances you need to + specify that "time" and "c2" are not scaled (using the argument + "scale_params"). + Output + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m8k + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, + random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + Warning + + There are missing values in a variable for which a random effect is + specified ("c2"). It will not be possible to re-scale the random + effects "b_y_id" and their variance covariance matrix "D_y_id" back to + the original scale of the data. If you are not interested in the + estimated random effects or their (co)variances this is not a problem. + The fixed effects estimates are not affected by this. If you are + interested in the random effects or the (co)variances you need to + specify that "time" and "c2" are not scaled (using the argument + "scale_params"). + Output + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90.0 + lvlone lvlone 263 79.9 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0.0 + c1 lvlone 0 0.0 + time lvlone 0 0.0 + c2 lvlone 66 20.1 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m8l + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + + I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + B21:time 0 0 0 0 0 NaN NaN + c1:time 0 0 0 0 0 NaN NaN + B21:c1:time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + c1 lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m8m + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | + id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + b11 0 0 0 0 0 NaN NaN + o1.L 0 0 0 0 0 NaN NaN + o1.Q 0 0 0 0 0 NaN NaN + c1:b11 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 100 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + c1 lvlone 0 0 + b1 lvlone 0 0 + o1 lvlone 0 0 + + level # NA % NA + id id 0 0 + + + $m8n + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, + random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + b11 0 0 0 0 0 NaN NaN + C1:time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + D_y_id[1,3] 0 0 0 0 0 NaN NaN + D_y_id[2,3] 0 0 0 0 0 NaN NaN + D_y_id[3,3] 0 0 0 0 NaN NaN + D_y_id[1,4] 0 0 0 0 0 NaN NaN + D_y_id[2,4] 0 0 0 0 0 NaN NaN + D_y_id[3,4] 0 0 0 0 0 NaN NaN + D_y_id[4,4] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 90 90 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + c1 lvlone 0 0 + time lvlone 0 0 + b1 lvlone 0 0 + + level # NA % NA + C1 id 0 0 + id id 0 0 + B2 id 10 10 + + + $m9a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + b11 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + + * For level "id": + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + * For level "o1": + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_o1[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + - o1: 3 + + + Number and proportion of complete cases: + level # % + id id 100 100 + o1 o1 3 100 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + c1 lvlone 0 0 + b1 lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + id id 0 0 + + level # NA % NA + o1 o1 0 0 + + + $m9b + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, + n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + D_y_id[1,2] 0 0 0 0 0 NaN NaN + D_y_id[2,2] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + time lvlone 0 0 + + level # NA % NA + C1 id 0 0 + B1 id 0 0 + id id 0 0 + C2 id 42 42 + + + $m9c + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, monitor_params = c(analysis_random = TRUE), + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_y_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_y 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + + Number and proportion of complete cases: + level # % + id id 58 58 + lvlone lvlone 329 100 + + Number and proportion of missing values: + level # NA % NA + y lvlone 0 0 + + level # NA % NA + C1 id 0 0 + B1 id 0 0 + id id 0 0 + C2 id 42 42 + + + +--- + + Code + lapply(models0, function(x) coef(summary(x))) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. 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Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a1 + $m0a1$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0a2 + $m0a2$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0a3 + $m0a3$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0a4 + $m0a4$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b1 + $m0b1$b1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b2 + $m0b2$b1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b3 + $m0b3$b1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b4 + $m0b4$b1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0c1 + $m0c1$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0c2 + $m0c2$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0d1 + $m0d1$p1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0d2 + $m0d2$p1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0e1 + $m0e1$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m0f1 + $m0f1$Be1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m1a + $m1a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1b + $m1b$b1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1c + $m1c$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1d + $m1d$p1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1e + $m1e$L1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m1f + $m1f$Be1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + + + $m2a + $m2a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + $m2b + $m2b$b2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + $m2c + $m2c$L1mis + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + $m2d + $m2d$p2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + $m2e + $m2e$L1mis + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + $m2f + $m2f$Be2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + + + $m3a + $m3a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m3b + $m3b$b2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m3c + $m3c$L1mis + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m3d + $m3d$p2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m3e + $m3e$L1mis + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m3f + $m3f$Be2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + C2 0 0 0 0 0 NaN NaN + + + $m4a + $m4a$c1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + p2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + Be2 0 0 0 0 0 NaN NaN + + + $m4b + $m4b$c1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + p2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + + + $m4c + $m4c$c1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + p2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + + + $m4d + $m4d$c1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + p2 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + Be2 0 0 0 0 0 NaN NaN + + + $m5a + $m5a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + log(C1) 0 0 0 0 0 NaN NaN + o22 0 0 0 0 0 NaN NaN + o23 0 0 0 0 0 NaN NaN + o24 0 0 0 0 0 NaN NaN + abs(C1 - c2) 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + I(time^2) 0 0 0 0 0 NaN NaN + o22:abs(C1 - c2) 0 0 0 0 0 NaN NaN + o23:abs(C1 - c2) 0 0 0 0 0 NaN NaN + o24:abs(C1 - c2) 0 0 0 0 0 NaN NaN + + + $m5b + $m5b$b1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + L1mis 0 0 0 0 0 NaN NaN + abs(c1 - C2) 0 0 0 0 0 NaN NaN + log(Be2) 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + $m6a + $m6a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + b21 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + $m6b + $m6b$b1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + $m7a + $m7a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + ns(time, df = 2)1 0 0 0 0 0 NaN NaN + ns(time, df = 2)2 0 0 0 0 0 NaN NaN + + + $m7b + $m7b$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + bs(time, df = 3)1 0 0 0 0 0 NaN NaN + bs(time, df = 3)2 0 0 0 0 0 NaN NaN + bs(time, df = 3)3 0 0 0 0 0 NaN NaN + + + $m7c + $m7c$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + ns(time, df = 3)1 0 0 0 0 0 NaN NaN + ns(time, df = 3)2 0 0 0 0 0 NaN NaN + ns(time, df = 3)3 0 0 0 0 0 NaN NaN + + + $m7d + $m7d$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + ns(time, df = 3)1 0 0 0 0 0 NaN NaN + ns(time, df = 3)2 0 0 0 0 0 NaN NaN + ns(time, df = 3)3 0 0 0 0 0 NaN NaN + + + $m7e + $m7e$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + ns(time, df = 3)1 0 0 0 0 0 NaN NaN + ns(time, df = 3)2 0 0 0 0 0 NaN NaN + ns(time, df = 3)3 0 0 0 0 0 NaN NaN + + + $m7f + $m7f$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + ns(time, df = 3)1 0 0 0 0 0 NaN NaN + ns(time, df = 3)2 0 0 0 0 0 NaN NaN + ns(time, df = 3)3 0 0 0 0 0 NaN NaN + + + $m8a + $m8a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + $m8b + $m8b$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + $m8c + $m8c$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + $m8d + $m8d$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + $m8e + $m8e$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + $m8f + $m8f$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + $m8g + $m8g$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + + + $m8h + $m8h$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c2 0 0 0 0 0 NaN NaN + + + $m8i + $m8i$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c2 0 0 0 0 0 NaN NaN + + + $m8j + $m8j$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c2 0 0 0 0 0 NaN NaN + + + $m8k + $m8k$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c2 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c2 0 0 0 0 0 NaN NaN + + + $m8l + $m8l$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + B21:c1 0 0 0 0 0 NaN NaN + B21:time 0 0 0 0 0 NaN NaN + c1:time 0 0 0 0 0 NaN NaN + B21:c1:time 0 0 0 0 0 NaN NaN + + + $m8m + $m8m$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + b11 0 0 0 0 0 NaN NaN + o1.L 0 0 0 0 0 NaN NaN + o1.Q 0 0 0 0 0 NaN NaN + c1:b11 0 0 0 0 0 NaN NaN + + + $m8n + $m8n$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + B21 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + b11 0 0 0 0 0 NaN NaN + C1:time 0 0 0 0 0 NaN NaN + + + $m9a + $m9a$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + c1 0 0 0 0 0 NaN NaN + b11 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + $m9b + $m9b$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + time 0 0 0 0 0 NaN NaN + + + $m9c + $m9c$y + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + C1 0 0 0 0 0 NaN NaN + C2 0 0 0 0 0 NaN NaN + B11 0 0 0 0 0 NaN NaN + + + diff --git a/tests/testthat/_snaps/mlogit.md b/tests/testthat/_snaps/mlogit.md new file mode 100644 index 00000000..86958b33 --- /dev/null +++ b/tests/testthat/_snaps/mlogit.md @@ -0,0 +1,3942 @@ +# data_list remains the same + + Code + lapply(models, "[[", "data_list") + Output + $m0a + $m0a$M_lvlone + M1 (Intercept) + 1 1 1 + 2 2 1 + 3 2 1 + 4 1 1 + 5 3 1 + 6 3 1 + 7 3 1 + 8 3 1 + 9 1 1 + 10 2 1 + 11 1 1 + 12 2 1 + 13 1 1 + 14 4 1 + 15 2 1 + 16 3 1 + 17 3 1 + 18 1 1 + 19 2 1 + 20 2 1 + 21 1 1 + 22 4 1 + 23 1 1 + 24 2 1 + 25 1 1 + 26 1 1 + 27 2 1 + 28 1 1 + 29 3 1 + 30 1 1 + 31 4 1 + 32 1 1 + 33 4 1 + 34 2 1 + 35 1 1 + 36 1 1 + 37 1 1 + 38 2 1 + 39 2 1 + 40 2 1 + 41 4 1 + 42 4 1 + 43 4 1 + 44 4 1 + 45 2 1 + 46 1 1 + 47 3 1 + 48 3 1 + 49 2 1 + 50 2 1 + 51 3 1 + 52 1 1 + 53 2 1 + 54 3 1 + 55 2 1 + 56 1 1 + 57 4 1 + 58 1 1 + 59 4 1 + 60 1 1 + 61 1 1 + 62 1 1 + 63 4 1 + 64 1 1 + 65 2 1 + 66 4 1 + 67 4 1 + 68 3 1 + 69 3 1 + 70 2 1 + 71 3 1 + 72 2 1 + 73 4 1 + 74 2 1 + 75 1 1 + 76 3 1 + 77 2 1 + 78 4 1 + 79 4 1 + 80 1 1 + 81 1 1 + 82 4 1 + 83 3 1 + 84 4 1 + 85 2 1 + 86 4 1 + 87 3 1 + 88 3 1 + 89 3 1 + 90 1 1 + 91 1 1 + 92 1 1 + 93 1 1 + 94 3 1 + 95 2 1 + 96 1 1 + 97 3 1 + 98 4 1 + 99 2 1 + 100 1 1 + + $m0a$mu_reg_multinomial + [1] 0 + + $m0a$tau_reg_multinomial + [1] 1e-04 + + + $m0b + $m0b$M_lvlone + M2 (Intercept) + 1 4 1 + 2 1 1 + 3 3 1 + 4 3 1 + 5 4 1 + 6 4 1 + 7 1 1 + 8 1 1 + 9 2 1 + 10 2 1 + 11 3 1 + 12 3 1 + 13 2 1 + 14 3 1 + 15 2 1 + 16 1 1 + 17 4 1 + 18 2 1 + 19 3 1 + 20 3 1 + 21 2 1 + 22 2 1 + 23 3 1 + 24 3 1 + 25 2 1 + 26 2 1 + 27 1 1 + 28 3 1 + 29 4 1 + 30 2 1 + 31 NA 1 + 32 4 1 + 33 4 1 + 34 4 1 + 35 2 1 + 36 1 1 + 37 3 1 + 38 4 1 + 39 3 1 + 40 3 1 + 41 1 1 + 42 4 1 + 43 1 1 + 44 2 1 + 45 2 1 + 46 1 1 + 47 4 1 + 48 2 1 + 49 4 1 + 50 1 1 + 51 4 1 + 52 4 1 + 53 4 1 + 54 3 1 + 55 3 1 + 56 3 1 + 57 2 1 + 58 3 1 + 59 3 1 + 60 4 1 + 61 2 1 + 62 2 1 + 63 1 1 + 64 2 1 + 65 2 1 + 66 3 1 + 67 2 1 + 68 4 1 + 69 NA 1 + 70 1 1 + 71 2 1 + 72 3 1 + 73 4 1 + 74 4 1 + 75 1 1 + 76 4 1 + 77 NA 1 + 78 2 1 + 79 2 1 + 80 2 1 + 81 4 1 + 82 3 1 + 83 3 1 + 84 1 1 + 85 2 1 + 86 1 1 + 87 3 1 + 88 1 1 + 89 2 1 + 90 1 1 + 91 3 1 + 92 1 1 + 93 4 1 + 94 4 1 + 95 1 1 + 96 4 1 + 97 4 1 + 98 3 1 + 99 3 1 + 100 4 1 + + $m0b$mu_reg_multinomial + [1] 0 + + $m0b$tau_reg_multinomial + [1] 1e-04 + + + $m1a + $m1a$M_lvlone + M1 (Intercept) C1 + 1 1 1 1.410531 + 2 2 1 1.434183 + 3 2 1 1.430994 + 4 1 1 1.453096 + 5 3 1 1.438344 + 6 3 1 1.453207 + 7 3 1 1.425176 + 8 3 1 1.437908 + 9 1 1 1.416911 + 10 2 1 1.448638 + 11 1 1 1.428375 + 12 2 1 1.450130 + 13 1 1 1.420545 + 14 4 1 1.423005 + 15 2 1 1.435902 + 16 3 1 1.423901 + 17 3 1 1.457208 + 18 1 1 1.414280 + 19 2 1 1.443383 + 20 2 1 1.434954 + 21 1 1 1.429499 + 22 4 1 1.441897 + 23 1 1 1.423713 + 24 2 1 1.435395 + 25 1 1 1.425944 + 26 1 1 1.437115 + 27 2 1 1.441326 + 28 1 1 1.422953 + 29 3 1 1.437797 + 30 1 1 1.472121 + 31 4 1 1.421782 + 32 1 1 1.457672 + 33 4 1 1.430842 + 34 2 1 1.431523 + 35 1 1 1.421395 + 36 1 1 1.434496 + 37 1 1 1.425383 + 38 2 1 1.421802 + 39 2 1 1.430094 + 40 2 1 1.447621 + 41 4 1 1.434797 + 42 4 1 1.446091 + 43 4 1 1.445306 + 44 4 1 1.448783 + 45 2 1 1.450617 + 46 1 1 1.415055 + 47 3 1 1.436590 + 48 3 1 1.433938 + 49 2 1 1.414941 + 50 2 1 1.421807 + 51 3 1 1.453203 + 52 1 1 1.452129 + 53 2 1 1.431510 + 54 3 1 1.430082 + 55 2 1 1.443492 + 56 1 1 1.436460 + 57 4 1 1.418119 + 58 1 1 1.434971 + 59 4 1 1.445599 + 60 1 1 1.437097 + 61 1 1 1.428360 + 62 1 1 1.440550 + 63 4 1 1.443014 + 64 1 1 1.424298 + 65 2 1 1.448823 + 66 4 1 1.425834 + 67 4 1 1.427102 + 68 3 1 1.414240 + 69 3 1 1.456218 + 70 2 1 1.470594 + 71 3 1 1.425058 + 72 2 1 1.432371 + 73 4 1 1.441656 + 74 2 1 1.434952 + 75 1 1 1.402860 + 76 3 1 1.453363 + 77 2 1 1.432909 + 78 4 1 1.435103 + 79 4 1 1.434462 + 80 1 1 1.434661 + 81 1 1 1.445881 + 82 4 1 1.442548 + 83 3 1 1.430097 + 84 4 1 1.430119 + 85 2 1 1.430315 + 86 4 1 1.437584 + 87 3 1 1.409738 + 88 3 1 1.422388 + 89 3 1 1.422509 + 90 1 1 1.439432 + 91 1 1 1.430175 + 92 1 1 1.418002 + 93 1 1 1.423812 + 94 3 1 1.423473 + 95 2 1 1.434412 + 96 1 1 1.450844 + 97 3 1 1.433371 + 98 4 1 1.444378 + 99 2 1 1.422523 + 100 1 1 1.410394 + + $m1a$spM_lvlone + center scale + M1 NA NA + (Intercept) NA NA + C1 1.434101 0.01299651 + + $m1a$mu_reg_multinomial + [1] 0 + + $m1a$tau_reg_multinomial + [1] 1e-04 + + + $m1b + $m1b$M_lvlone + M2 (Intercept) C1 + 1 4 1 1.410531 + 2 1 1 1.434183 + 3 3 1 1.430994 + 4 3 1 1.453096 + 5 4 1 1.438344 + 6 4 1 1.453207 + 7 1 1 1.425176 + 8 1 1 1.437908 + 9 2 1 1.416911 + 10 2 1 1.448638 + 11 3 1 1.428375 + 12 3 1 1.450130 + 13 2 1 1.420545 + 14 3 1 1.423005 + 15 2 1 1.435902 + 16 1 1 1.423901 + 17 4 1 1.457208 + 18 2 1 1.414280 + 19 3 1 1.443383 + 20 3 1 1.434954 + 21 2 1 1.429499 + 22 2 1 1.441897 + 23 3 1 1.423713 + 24 3 1 1.435395 + 25 2 1 1.425944 + 26 2 1 1.437115 + 27 1 1 1.441326 + 28 3 1 1.422953 + 29 4 1 1.437797 + 30 2 1 1.472121 + 31 NA 1 1.421782 + 32 4 1 1.457672 + 33 4 1 1.430842 + 34 4 1 1.431523 + 35 2 1 1.421395 + 36 1 1 1.434496 + 37 3 1 1.425383 + 38 4 1 1.421802 + 39 3 1 1.430094 + 40 3 1 1.447621 + 41 1 1 1.434797 + 42 4 1 1.446091 + 43 1 1 1.445306 + 44 2 1 1.448783 + 45 2 1 1.450617 + 46 1 1 1.415055 + 47 4 1 1.436590 + 48 2 1 1.433938 + 49 4 1 1.414941 + 50 1 1 1.421807 + 51 4 1 1.453203 + 52 4 1 1.452129 + 53 4 1 1.431510 + 54 3 1 1.430082 + 55 3 1 1.443492 + 56 3 1 1.436460 + 57 2 1 1.418119 + 58 3 1 1.434971 + 59 3 1 1.445599 + 60 4 1 1.437097 + 61 2 1 1.428360 + 62 2 1 1.440550 + 63 1 1 1.443014 + 64 2 1 1.424298 + 65 2 1 1.448823 + 66 3 1 1.425834 + 67 2 1 1.427102 + 68 4 1 1.414240 + 69 NA 1 1.456218 + 70 1 1 1.470594 + 71 2 1 1.425058 + 72 3 1 1.432371 + 73 4 1 1.441656 + 74 4 1 1.434952 + 75 1 1 1.402860 + 76 4 1 1.453363 + 77 NA 1 1.432909 + 78 2 1 1.435103 + 79 2 1 1.434462 + 80 2 1 1.434661 + 81 4 1 1.445881 + 82 3 1 1.442548 + 83 3 1 1.430097 + 84 1 1 1.430119 + 85 2 1 1.430315 + 86 1 1 1.437584 + 87 3 1 1.409738 + 88 1 1 1.422388 + 89 2 1 1.422509 + 90 1 1 1.439432 + 91 3 1 1.430175 + 92 1 1 1.418002 + 93 4 1 1.423812 + 94 4 1 1.423473 + 95 1 1 1.434412 + 96 4 1 1.450844 + 97 4 1 1.433371 + 98 3 1 1.444378 + 99 3 1 1.422523 + 100 4 1 1.410394 + + $m1b$spM_lvlone + center scale + M2 NA NA + (Intercept) NA NA + C1 1.434101 0.01299651 + + $m1b$mu_reg_multinomial + [1] 0 + + $m1b$tau_reg_multinomial + [1] 1e-04 + + + $m2a + $m2a$M_lvlone + M1 C2 (Intercept) + 1 1 0.144065882 1 + 2 2 0.032778478 1 + 3 2 0.343008492 1 + 4 1 -0.361887858 1 + 5 3 -0.389600647 1 + 6 3 -0.205306841 1 + 7 3 0.079434830 1 + 8 3 -0.331246757 1 + 9 1 -0.329638800 1 + 10 2 0.167597533 1 + 11 1 0.860207989 1 + 12 2 0.022730640 1 + 13 1 0.217171172 1 + 14 4 -0.403002412 1 + 15 2 0.087369742 1 + 16 3 -0.183870429 1 + 17 3 -0.194577002 1 + 18 1 -0.349718516 1 + 19 2 -0.508781244 1 + 20 2 0.494883111 1 + 21 1 0.258041067 1 + 22 4 -0.922621989 1 + 23 1 0.431254949 1 + 24 2 -0.294218881 1 + 25 1 -0.425548895 1 + 26 1 0.057176054 1 + 27 2 0.289090158 1 + 28 1 -0.473079489 1 + 29 3 -0.385664863 1 + 30 1 -0.154780107 1 + 31 4 0.100536296 1 + 32 1 0.634791958 1 + 33 4 -0.387252617 1 + 34 2 -0.181741088 1 + 35 1 -0.311562695 1 + 36 1 -0.044115907 1 + 37 1 -0.657409991 1 + 38 2 0.159577214 1 + 39 2 -0.460416933 1 + 40 2 NA 1 + 41 4 -0.248909867 1 + 42 4 -0.609021545 1 + 43 4 0.025471883 1 + 44 4 0.066648592 1 + 45 2 -0.276108719 1 + 46 1 -0.179737577 1 + 47 3 0.181190937 1 + 48 3 -0.453871693 1 + 49 2 0.448629602 1 + 50 2 -0.529811821 1 + 51 3 -0.028304571 1 + 52 1 -0.520318482 1 + 53 2 0.171317619 1 + 54 3 0.432732046 1 + 55 2 -0.346286005 1 + 56 1 -0.469375653 1 + 57 4 0.031021711 1 + 58 1 -0.118837515 1 + 59 4 0.507769984 1 + 60 1 0.271797031 1 + 61 1 -0.124442204 1 + 62 1 0.277677389 1 + 63 4 -0.102893730 1 + 64 1 NA 1 + 65 2 -0.678303052 1 + 66 4 0.478880037 1 + 67 4 -0.428028760 1 + 68 3 0.048119185 1 + 69 3 0.216932805 1 + 70 2 -0.234575269 1 + 71 3 0.006827078 1 + 72 2 -0.456055171 1 + 73 4 0.346486708 1 + 74 2 0.205092215 1 + 75 1 -0.136596858 1 + 76 3 -0.500179043 1 + 77 2 0.527352086 1 + 78 4 0.022742250 1 + 79 4 NA 1 + 80 1 -0.002032440 1 + 81 1 -0.154246160 1 + 82 4 0.140201825 1 + 83 3 -0.141417121 1 + 84 4 NA 1 + 85 2 -0.021285339 1 + 86 4 -0.010196306 1 + 87 3 -0.089747520 1 + 88 3 -0.083699898 1 + 89 3 -0.044061996 1 + 90 1 -0.209291697 1 + 91 1 0.639036426 1 + 92 1 0.094698299 1 + 93 1 -0.055510622 1 + 94 3 -0.421318463 1 + 95 2 0.125295503 1 + 96 1 0.213084904 1 + 97 3 -0.161914659 1 + 98 4 -0.034767685 1 + 99 2 -0.320681689 1 + 100 1 0.058192962 1 + + $m2a$spM_lvlone + center scale + M1 NA NA + C2 -0.06490582 0.3331735 + (Intercept) NA NA + + $m2a$mu_reg_norm + [1] 0 + + $m2a$tau_reg_norm + [1] 1e-04 + + $m2a$shape_tau_norm + [1] 0.01 + + $m2a$rate_tau_norm + [1] 0.01 + + $m2a$mu_reg_multinomial + [1] 0 + + $m2a$tau_reg_multinomial + [1] 1e-04 + + + $m2b + $m2b$M_lvlone + M2 C2 (Intercept) + 1 4 0.144065882 1 + 2 1 0.032778478 1 + 3 3 0.343008492 1 + 4 3 -0.361887858 1 + 5 4 -0.389600647 1 + 6 4 -0.205306841 1 + 7 1 0.079434830 1 + 8 1 -0.331246757 1 + 9 2 -0.329638800 1 + 10 2 0.167597533 1 + 11 3 0.860207989 1 + 12 3 0.022730640 1 + 13 2 0.217171172 1 + 14 3 -0.403002412 1 + 15 2 0.087369742 1 + 16 1 -0.183870429 1 + 17 4 -0.194577002 1 + 18 2 -0.349718516 1 + 19 3 -0.508781244 1 + 20 3 0.494883111 1 + 21 2 0.258041067 1 + 22 2 -0.922621989 1 + 23 3 0.431254949 1 + 24 3 -0.294218881 1 + 25 2 -0.425548895 1 + 26 2 0.057176054 1 + 27 1 0.289090158 1 + 28 3 -0.473079489 1 + 29 4 -0.385664863 1 + 30 2 -0.154780107 1 + 31 NA 0.100536296 1 + 32 4 0.634791958 1 + 33 4 -0.387252617 1 + 34 4 -0.181741088 1 + 35 2 -0.311562695 1 + 36 1 -0.044115907 1 + 37 3 -0.657409991 1 + 38 4 0.159577214 1 + 39 3 -0.460416933 1 + 40 3 NA 1 + 41 1 -0.248909867 1 + 42 4 -0.609021545 1 + 43 1 0.025471883 1 + 44 2 0.066648592 1 + 45 2 -0.276108719 1 + 46 1 -0.179737577 1 + 47 4 0.181190937 1 + 48 2 -0.453871693 1 + 49 4 0.448629602 1 + 50 1 -0.529811821 1 + 51 4 -0.028304571 1 + 52 4 -0.520318482 1 + 53 4 0.171317619 1 + 54 3 0.432732046 1 + 55 3 -0.346286005 1 + 56 3 -0.469375653 1 + 57 2 0.031021711 1 + 58 3 -0.118837515 1 + 59 3 0.507769984 1 + 60 4 0.271797031 1 + 61 2 -0.124442204 1 + 62 2 0.277677389 1 + 63 1 -0.102893730 1 + 64 2 NA 1 + 65 2 -0.678303052 1 + 66 3 0.478880037 1 + 67 2 -0.428028760 1 + 68 4 0.048119185 1 + 69 NA 0.216932805 1 + 70 1 -0.234575269 1 + 71 2 0.006827078 1 + 72 3 -0.456055171 1 + 73 4 0.346486708 1 + 74 4 0.205092215 1 + 75 1 -0.136596858 1 + 76 4 -0.500179043 1 + 77 NA 0.527352086 1 + 78 2 0.022742250 1 + 79 2 NA 1 + 80 2 -0.002032440 1 + 81 4 -0.154246160 1 + 82 3 0.140201825 1 + 83 3 -0.141417121 1 + 84 1 NA 1 + 85 2 -0.021285339 1 + 86 1 -0.010196306 1 + 87 3 -0.089747520 1 + 88 1 -0.083699898 1 + 89 2 -0.044061996 1 + 90 1 -0.209291697 1 + 91 3 0.639036426 1 + 92 1 0.094698299 1 + 93 4 -0.055510622 1 + 94 4 -0.421318463 1 + 95 1 0.125295503 1 + 96 4 0.213084904 1 + 97 4 -0.161914659 1 + 98 3 -0.034767685 1 + 99 3 -0.320681689 1 + 100 4 0.058192962 1 + + $m2b$spM_lvlone + center scale + M2 NA NA + C2 -0.06490582 0.3331735 + (Intercept) NA NA + + $m2b$mu_reg_norm + [1] 0 + + $m2b$tau_reg_norm + [1] 1e-04 + + $m2b$shape_tau_norm + [1] 0.01 + + $m2b$rate_tau_norm + [1] 0.01 + + $m2b$mu_reg_multinomial + [1] 0 + + $m2b$tau_reg_multinomial + [1] 1e-04 + + + $m3a + $m3a$M_lvlone + C1 (Intercept) M12 M13 M14 + 1 1.410531 1 0 0 0 + 2 1.434183 1 1 0 0 + 3 1.430994 1 1 0 0 + 4 1.453096 1 0 0 0 + 5 1.438344 1 0 1 0 + 6 1.453207 1 0 1 0 + 7 1.425176 1 0 1 0 + 8 1.437908 1 0 1 0 + 9 1.416911 1 0 0 0 + 10 1.448638 1 1 0 0 + 11 1.428375 1 0 0 0 + 12 1.450130 1 1 0 0 + 13 1.420545 1 0 0 0 + 14 1.423005 1 0 0 1 + 15 1.435902 1 1 0 0 + 16 1.423901 1 0 1 0 + 17 1.457208 1 0 1 0 + 18 1.414280 1 0 0 0 + 19 1.443383 1 1 0 0 + 20 1.434954 1 1 0 0 + 21 1.429499 1 0 0 0 + 22 1.441897 1 0 0 1 + 23 1.423713 1 0 0 0 + 24 1.435395 1 1 0 0 + 25 1.425944 1 0 0 0 + 26 1.437115 1 0 0 0 + 27 1.441326 1 1 0 0 + 28 1.422953 1 0 0 0 + 29 1.437797 1 0 1 0 + 30 1.472121 1 0 0 0 + 31 1.421782 1 0 0 1 + 32 1.457672 1 0 0 0 + 33 1.430842 1 0 0 1 + 34 1.431523 1 1 0 0 + 35 1.421395 1 0 0 0 + 36 1.434496 1 0 0 0 + 37 1.425383 1 0 0 0 + 38 1.421802 1 1 0 0 + 39 1.430094 1 1 0 0 + 40 1.447621 1 1 0 0 + 41 1.434797 1 0 0 1 + 42 1.446091 1 0 0 1 + 43 1.445306 1 0 0 1 + 44 1.448783 1 0 0 1 + 45 1.450617 1 1 0 0 + 46 1.415055 1 0 0 0 + 47 1.436590 1 0 1 0 + 48 1.433938 1 0 1 0 + 49 1.414941 1 1 0 0 + 50 1.421807 1 1 0 0 + 51 1.453203 1 0 1 0 + 52 1.452129 1 0 0 0 + 53 1.431510 1 1 0 0 + 54 1.430082 1 0 1 0 + 55 1.443492 1 1 0 0 + 56 1.436460 1 0 0 0 + 57 1.418119 1 0 0 1 + 58 1.434971 1 0 0 0 + 59 1.445599 1 0 0 1 + 60 1.437097 1 0 0 0 + 61 1.428360 1 0 0 0 + 62 1.440550 1 0 0 0 + 63 1.443014 1 0 0 1 + 64 1.424298 1 0 0 0 + 65 1.448823 1 1 0 0 + 66 1.425834 1 0 0 1 + 67 1.427102 1 0 0 1 + 68 1.414240 1 0 1 0 + 69 1.456218 1 0 1 0 + 70 1.470594 1 1 0 0 + 71 1.425058 1 0 1 0 + 72 1.432371 1 1 0 0 + 73 1.441656 1 0 0 1 + 74 1.434952 1 1 0 0 + 75 1.402860 1 0 0 0 + 76 1.453363 1 0 1 0 + 77 1.432909 1 1 0 0 + 78 1.435103 1 0 0 1 + 79 1.434462 1 0 0 1 + 80 1.434661 1 0 0 0 + 81 1.445881 1 0 0 0 + 82 1.442548 1 0 0 1 + 83 1.430097 1 0 1 0 + 84 1.430119 1 0 0 1 + 85 1.430315 1 1 0 0 + 86 1.437584 1 0 0 1 + 87 1.409738 1 0 1 0 + 88 1.422388 1 0 1 0 + 89 1.422509 1 0 1 0 + 90 1.439432 1 0 0 0 + 91 1.430175 1 0 0 0 + 92 1.418002 1 0 0 0 + 93 1.423812 1 0 0 0 + 94 1.423473 1 0 1 0 + 95 1.434412 1 1 0 0 + 96 1.450844 1 0 0 0 + 97 1.433371 1 0 1 0 + 98 1.444378 1 0 0 1 + 99 1.422523 1 1 0 0 + 100 1.410394 1 0 0 0 + + $m3a$mu_reg_norm + [1] 0 + + $m3a$tau_reg_norm + [1] 1e-04 + + $m3a$shape_tau_norm + [1] 0.01 + + $m3a$rate_tau_norm + [1] 0.01 + + + $m3b + $m3b$M_lvlone + C1 M2 (Intercept) M22 M23 M24 + 1 1.410531 4 1 NA NA NA + 2 1.434183 1 1 NA NA NA + 3 1.430994 3 1 NA NA NA + 4 1.453096 3 1 NA NA NA + 5 1.438344 4 1 NA NA NA + 6 1.453207 4 1 NA NA NA + 7 1.425176 1 1 NA NA NA + 8 1.437908 1 1 NA NA NA + 9 1.416911 2 1 NA NA NA + 10 1.448638 2 1 NA NA NA + 11 1.428375 3 1 NA NA NA + 12 1.450130 3 1 NA NA NA + 13 1.420545 2 1 NA NA NA + 14 1.423005 3 1 NA NA NA + 15 1.435902 2 1 NA NA NA + 16 1.423901 1 1 NA NA NA + 17 1.457208 4 1 NA NA NA + 18 1.414280 2 1 NA NA NA + 19 1.443383 3 1 NA NA NA + 20 1.434954 3 1 NA NA NA + 21 1.429499 2 1 NA NA NA + 22 1.441897 2 1 NA NA NA + 23 1.423713 3 1 NA NA NA + 24 1.435395 3 1 NA NA NA + 25 1.425944 2 1 NA NA NA + 26 1.437115 2 1 NA NA NA + 27 1.441326 1 1 NA NA NA + 28 1.422953 3 1 NA NA NA + 29 1.437797 4 1 NA NA NA + 30 1.472121 2 1 NA NA NA + 31 1.421782 NA 1 NA NA NA + 32 1.457672 4 1 NA NA NA + 33 1.430842 4 1 NA NA NA + 34 1.431523 4 1 NA NA NA + 35 1.421395 2 1 NA NA NA + 36 1.434496 1 1 NA NA NA + 37 1.425383 3 1 NA NA NA + 38 1.421802 4 1 NA NA NA + 39 1.430094 3 1 NA NA NA + 40 1.447621 3 1 NA NA NA + 41 1.434797 1 1 NA NA NA + 42 1.446091 4 1 NA NA NA + 43 1.445306 1 1 NA NA NA + 44 1.448783 2 1 NA NA NA + 45 1.450617 2 1 NA NA NA + 46 1.415055 1 1 NA NA NA + 47 1.436590 4 1 NA NA NA + 48 1.433938 2 1 NA NA NA + 49 1.414941 4 1 NA NA NA + 50 1.421807 1 1 NA NA NA + 51 1.453203 4 1 NA NA NA + 52 1.452129 4 1 NA NA NA + 53 1.431510 4 1 NA NA NA + 54 1.430082 3 1 NA NA NA + 55 1.443492 3 1 NA NA NA + 56 1.436460 3 1 NA NA NA + 57 1.418119 2 1 NA NA NA + 58 1.434971 3 1 NA NA NA + 59 1.445599 3 1 NA NA NA + 60 1.437097 4 1 NA NA NA + 61 1.428360 2 1 NA NA NA + 62 1.440550 2 1 NA NA NA + 63 1.443014 1 1 NA NA NA + 64 1.424298 2 1 NA NA NA + 65 1.448823 2 1 NA NA NA + 66 1.425834 3 1 NA NA NA + 67 1.427102 2 1 NA NA NA + 68 1.414240 4 1 NA NA NA + 69 1.456218 NA 1 NA NA NA + 70 1.470594 1 1 NA NA NA + 71 1.425058 2 1 NA NA NA + 72 1.432371 3 1 NA NA NA + 73 1.441656 4 1 NA NA NA + 74 1.434952 4 1 NA NA NA + 75 1.402860 1 1 NA NA NA + 76 1.453363 4 1 NA NA NA + 77 1.432909 NA 1 NA NA NA + 78 1.435103 2 1 NA NA NA + 79 1.434462 2 1 NA NA NA + 80 1.434661 2 1 NA NA NA + 81 1.445881 4 1 NA NA NA + 82 1.442548 3 1 NA NA NA + 83 1.430097 3 1 NA NA NA + 84 1.430119 1 1 NA NA NA + 85 1.430315 2 1 NA NA NA + 86 1.437584 1 1 NA NA NA + 87 1.409738 3 1 NA NA NA + 88 1.422388 1 1 NA NA NA + 89 1.422509 2 1 NA NA NA + 90 1.439432 1 1 NA NA NA + 91 1.430175 3 1 NA NA NA + 92 1.418002 1 1 NA NA NA + 93 1.423812 4 1 NA NA NA + 94 1.423473 4 1 NA NA NA + 95 1.434412 1 1 NA NA NA + 96 1.450844 4 1 NA NA NA + 97 1.433371 4 1 NA NA NA + 98 1.444378 3 1 NA NA NA + 99 1.422523 3 1 NA NA NA + 100 1.410394 4 1 NA NA NA + + $m3b$mu_reg_norm + [1] 0 + + $m3b$tau_reg_norm + [1] 1e-04 + + $m3b$shape_tau_norm + [1] 0.01 + + $m3b$rate_tau_norm + [1] 0.01 + + $m3b$mu_reg_multinomial + [1] 0 + + $m3b$tau_reg_multinomial + [1] 1e-04 + + + $m4a + $m4a$M_lvlone + M1 C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24 abs(C1 - C2) + 1 1 0.144065882 4 4 1 NA NA NA NA NA NA NA + 2 2 0.032778478 1 4 1 NA NA NA NA NA NA NA + 3 2 0.343008492 3 4 1 NA NA NA NA NA NA NA + 4 1 -0.361887858 3 1 1 NA NA NA NA NA NA NA + 5 3 -0.389600647 4 2 1 NA NA NA NA NA NA NA + 6 3 -0.205306841 4 3 1 NA NA NA NA NA NA NA + 7 3 0.079434830 1 4 1 NA NA NA NA NA NA NA + 8 3 -0.331246757 1 2 1 NA NA NA NA NA NA NA + 9 1 -0.329638800 2 4 1 NA NA NA NA NA NA NA + 10 2 0.167597533 2 3 1 NA NA NA NA NA NA NA + 11 1 0.860207989 3 2 1 NA NA NA NA NA NA NA + 12 2 0.022730640 3 1 1 NA NA NA NA NA NA NA + 13 1 0.217171172 2 1 1 NA NA NA NA NA NA NA + 14 4 -0.403002412 3 1 1 NA NA NA NA NA NA NA + 15 2 0.087369742 2 4 1 NA NA NA NA NA NA NA + 16 3 -0.183870429 1 3 1 NA NA NA NA NA NA NA + 17 3 -0.194577002 4 3 1 NA NA NA NA NA NA NA + 18 1 -0.349718516 2 1 1 NA NA NA NA NA NA NA + 19 2 -0.508781244 3 3 1 NA NA NA NA NA NA NA + 20 2 0.494883111 3 1 1 NA NA NA NA NA NA NA + 21 1 0.258041067 2 3 1 NA NA NA NA NA NA NA + 22 4 -0.922621989 2 3 1 NA NA NA NA NA NA NA + 23 1 0.431254949 3 2 1 NA NA NA NA NA NA NA + 24 2 -0.294218881 3 3 1 NA NA NA NA NA NA NA + 25 1 -0.425548895 2 2 1 NA NA NA NA NA NA NA + 26 1 0.057176054 2 2 1 NA NA NA NA NA NA NA + 27 2 0.289090158 1 1 1 NA NA NA NA NA NA NA + 28 1 -0.473079489 3 4 1 NA NA NA NA NA NA NA + 29 3 -0.385664863 4 3 1 NA NA NA NA NA NA NA + 30 1 -0.154780107 2 3 1 NA NA NA NA NA NA NA + 31 4 0.100536296 NA 2 1 NA NA NA NA NA NA NA + 32 1 0.634791958 4 2 1 NA NA NA NA NA NA NA + 33 4 -0.387252617 4 1 1 NA NA NA NA NA NA NA + 34 2 -0.181741088 4 1 1 NA NA NA NA NA NA NA + 35 1 -0.311562695 2 4 1 NA NA NA NA NA NA NA + 36 1 -0.044115907 1 3 1 NA NA NA NA NA NA NA + 37 1 -0.657409991 3 3 1 NA NA NA NA NA NA NA + 38 2 0.159577214 4 1 1 NA NA NA NA NA NA NA + 39 2 -0.460416933 3 2 1 NA NA NA NA NA NA NA + 40 2 NA 3 3 1 NA NA NA NA NA NA NA + 41 4 -0.248909867 1 3 1 NA NA NA NA NA NA NA + 42 4 -0.609021545 4 3 1 NA NA NA NA NA NA NA + 43 4 0.025471883 1 3 1 NA NA NA NA NA NA NA + 44 4 0.066648592 2 4 1 NA NA NA NA NA NA NA + 45 2 -0.276108719 2 4 1 NA NA NA NA NA NA NA + 46 1 -0.179737577 1 1 1 NA NA NA NA NA NA NA + 47 3 0.181190937 4 4 1 NA NA NA NA NA NA NA + 48 3 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NA NA NA + 70 2 -0.234575269 1 1 1 NA NA NA NA NA NA NA + 71 3 0.006827078 2 4 1 NA NA NA NA NA NA NA + 72 2 -0.456055171 3 4 1 NA NA NA NA NA NA NA + 73 4 0.346486708 4 2 1 NA NA NA NA NA NA NA + 74 2 0.205092215 4 4 1 NA NA NA NA NA NA NA + 75 1 -0.136596858 1 3 1 NA NA NA NA NA NA NA + 76 3 -0.500179043 4 2 1 NA NA NA NA NA NA NA + 77 2 0.527352086 NA 2 1 NA NA NA NA NA NA NA + 78 4 0.022742250 2 3 1 NA NA NA NA NA NA NA + 79 4 NA 2 2 1 NA NA NA NA NA NA NA + 80 1 -0.002032440 2 1 1 NA NA NA NA NA NA NA + 81 1 -0.154246160 4 4 1 NA NA NA NA NA NA NA + 82 4 0.140201825 3 2 1 NA NA NA NA NA NA NA + 83 3 -0.141417121 3 4 1 NA NA NA NA NA NA NA + 84 4 NA 1 1 1 NA NA NA NA NA NA NA + 85 2 -0.021285339 2 1 1 NA NA NA NA NA NA NA + 86 4 -0.010196306 1 2 1 NA NA NA NA NA NA NA + 87 3 -0.089747520 3 3 1 NA NA NA NA NA NA NA + 88 3 -0.083699898 1 3 1 NA NA NA NA NA NA NA + 89 3 -0.044061996 2 2 1 NA NA NA NA NA NA NA + 90 1 -0.209291697 1 4 1 NA NA NA NA NA NA NA + 91 1 0.639036426 3 2 1 NA 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1.453363 + 77 0.3597065 NA NA NA 1.432909 + 78 0.3612366 NA NA NA 1.435103 + 79 0.3607899 NA NA NA 1.434462 + 80 0.3609283 NA NA NA 1.434661 + 81 0.3687189 NA NA NA 1.445881 + 82 0.3664112 NA NA NA 1.442548 + 83 0.3577425 NA NA NA 1.430097 + 84 0.3577579 NA NA NA 1.430119 + 85 0.3578947 NA NA NA 1.430315 + 86 0.3629637 NA NA NA 1.437584 + 87 0.3434041 NA NA NA 1.409738 + 88 0.3523374 NA NA NA 1.422388 + 89 0.3524220 NA NA NA 1.422509 + 90 0.3642486 NA NA NA 1.439432 + 91 0.3577968 NA NA NA 1.430175 + 92 0.3492491 NA NA NA 1.418002 + 93 0.3533376 NA NA NA 1.423812 + 94 0.3530999 NA NA NA 1.423473 + 95 0.3607553 NA NA NA 1.434412 + 96 0.3721453 NA NA NA 1.450844 + 97 0.3600291 NA NA NA 1.433371 + 98 0.3676785 NA NA NA 1.444378 + 99 0.3524318 NA NA NA 1.422523 + 100 0.3438689 NA NA NA 1.410394 + + $m4a$spM_lvlone + center scale + M1 NA NA + C2 -0.06490582 0.333173465 + M2 NA NA + O2 NA NA + (Intercept) NA NA + M22 NA NA + M23 NA NA + M24 NA NA + O22 NA NA + O23 NA NA + O24 NA NA + abs(C1 - C2) 1.49900534 0.334214181 + log(C1) 0.36049727 0.009050336 + O22:abs(C1 - C2) 0.31342466 0.618807150 + O23:abs(C1 - C2) 0.47068368 0.762352624 + O24:abs(C1 - C2) 0.40568706 0.692690317 + C1 1.43410054 0.012996511 + + $m4a$mu_reg_norm + [1] 0 + + $m4a$tau_reg_norm + [1] 1e-04 + + $m4a$shape_tau_norm + [1] 0.01 + + $m4a$rate_tau_norm + [1] 0.01 + + $m4a$mu_reg_multinomial + [1] 0 + + $m4a$tau_reg_multinomial + [1] 1e-04 + + $m4a$mu_reg_ordinal + [1] 0 + + $m4a$tau_reg_ordinal + [1] 1e-04 + + $m4a$mu_delta_ordinal + [1] 0 + + $m4a$tau_delta_ordinal + [1] 1e-04 + + + $m4b + $m4b$M_lvlone + M1 C2 M2 (Intercept) + 1 1 0.144065882 4 1 + 2 2 0.032778478 1 1 + 3 2 0.343008492 3 1 + 4 1 -0.361887858 3 1 + 5 3 -0.389600647 4 1 + 6 3 -0.205306841 4 1 + 7 3 0.079434830 1 1 + 8 3 -0.331246757 1 1 + 9 1 -0.329638800 2 1 + 10 2 0.167597533 2 1 + 11 1 0.860207989 3 1 + 12 2 0.022730640 3 1 + 13 1 0.217171172 2 1 + 14 4 -0.403002412 3 1 + 15 2 0.087369742 2 1 + 16 3 -0.183870429 1 1 + 17 3 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100 0.2236068 -0.5 -0.6708204 1.410394 3 + + $m4b$spM_lvlone + center + M1 NA + C2 -0.06490582 + M2 NA + (Intercept) NA + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0.39175258 + abs(C1 - C2) 1.49900534 + log(C1) 0.36049727 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0.60211251 + M22 NA + M23 NA + M24 NA + O1.L NA + O1.Q NA + O1.C NA + C1 1.43410054 + O1 NA + scale + M1 NA + C2 0.333173465 + M2 NA + (Intercept) NA + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0.490677700 + abs(C1 - C2) 0.334214181 + log(C1) 0.009050336 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0.778929778 + M22 NA + M23 NA + M24 NA + O1.L NA + O1.Q NA + O1.C NA + C1 0.012996511 + O1 NA + + $m4b$mu_reg_norm + [1] 0 + + $m4b$tau_reg_norm + [1] 1e-04 + + $m4b$shape_tau_norm + [1] 0.01 + + $m4b$rate_tau_norm + [1] 0.01 + + $m4b$mu_reg_multinomial + [1] 0 + + $m4b$tau_reg_multinomial + [1] 1e-04 + + + +# jagsmodel remains the same + + Code + lapply(models, "[[", "jagsmodel") + Output + $m0a + model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] + log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[2] + log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[3] + } + + # Priors for the model for M1 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } + $m0b + model { + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 2] * beta[1] + log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[2] + log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[3] + } + + # Priors for the model for M2 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } + $m1a + model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[5] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6] + } + + # Priors for the model for M1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } + $m1b + model { + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[5] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6] + } + + # Priors for the model for M2 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } + $m2a + model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + log(phi_M1[i, 3]) <- M_lvlone[i, 3] * beta[3] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + log(phi_M1[i, 4]) <- M_lvlone[i, 3] * beta[5] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] + } + + # Priors for the model for M1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2b + model { + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + log(phi_M2[i, 3]) <- M_lvlone[i, 3] * beta[3] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + log(phi_M2[i, 4]) <- M_lvlone[i, 3] * beta[5] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] + } + + # Priors for the model for M2 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m3a + model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] + + M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4] + } + + # Priors for the model for C1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + } + $m3b + model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + } + + # Priors for the model for C1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 2] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 3] * alpha[1] + log(phi_M2[i, 3]) <- M_lvlone[i, 3] * alpha[2] + log(phi_M2[i, 4]) <- M_lvlone[i, 3] * alpha[3] + + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } + $m4a + model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] + log(phi_M1[i, 3]) <- M_lvlone[i, 5] * beta[13] + M_lvlone[i, 6] * beta[14] + + M_lvlone[i, 7] * beta[15] + M_lvlone[i, 8] * beta[16] + + M_lvlone[i, 9] * beta[17] + M_lvlone[i, 10] * beta[18] + + M_lvlone[i, 11] * beta[19] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[20] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[21] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[22] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[23] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[24] + log(phi_M1[i, 4]) <- M_lvlone[i, 5] * beta[25] + M_lvlone[i, 6] * beta[26] + + M_lvlone[i, 7] * beta[27] + M_lvlone[i, 8] * beta[28] + + M_lvlone[i, 9] * beta[29] + M_lvlone[i, 10] * beta[30] + + M_lvlone[i, 11] * beta[31] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[32] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[33] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[34] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[35] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[36] + } + + # Priors for the model for M1 + for (k in 1:36) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } + $m4b + model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + log(phi_M1[i, 3]) <- M_lvlone[i, 4] * beta[6] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[9] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[10] + log(phi_M1[i, 4]) <- M_lvlone[i, 4] * beta[11] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[12] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[14] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[15] + } + + # Priors for the model for M1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] + + M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] + + M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] + + M_lvlone[i, 14] * alpha[7] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8] + + M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 4] * alpha[9] + M_lvlone[i, 12] * alpha[10] + + M_lvlone[i, 13] * alpha[11] + M_lvlone[i, 14] * alpha[12] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 4] * alpha[14] + M_lvlone[i, 12] * alpha[15] + + M_lvlone[i, 13] * alpha[16] + M_lvlone[i, 14] * alpha[17] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 4] * alpha[19] + M_lvlone[i, 12] * alpha[20] + + M_lvlone[i, 13] * alpha[21] + M_lvlone[i, 14] * alpha[22] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[23] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + + + M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] + } + + } + +# GRcrit and MCerror give same result + + Code + lapply(models0, GR_crit, multivariate = FALSE) + Output + $m0a + Potential scale reduction factors: + + Point est. Upper C.I. + M12: (Intercept) NaN NaN + M13: (Intercept) NaN NaN + M14: (Intercept) NaN NaN + + + $m0b + Potential scale reduction factors: + + Point est. Upper C.I. + M22: (Intercept) NaN NaN + M23: (Intercept) NaN NaN + M24: (Intercept) NaN NaN + + + $m1a + Potential scale reduction factors: + + Point est. Upper C.I. + M12: (Intercept) NaN NaN + M12: C1 NaN NaN + M13: (Intercept) NaN NaN + M13: C1 NaN NaN + M14: (Intercept) NaN NaN + M14: C1 NaN NaN + + + $m1b + Potential scale reduction factors: + + Point est. Upper C.I. + M22: (Intercept) NaN NaN + M22: C1 NaN NaN + M23: (Intercept) NaN NaN + M23: C1 NaN NaN + M24: (Intercept) NaN NaN + M24: C1 NaN NaN + + + $m2a + Potential scale reduction factors: + + Point est. Upper C.I. + M12: (Intercept) NaN NaN + M12: C2 NaN NaN + M13: (Intercept) NaN NaN + M13: C2 NaN NaN + M14: (Intercept) NaN NaN + M14: C2 NaN NaN + + + $m2b + Potential scale reduction factors: + + Point est. Upper C.I. + M22: (Intercept) NaN NaN + M22: C2 NaN NaN + M23: (Intercept) NaN NaN + M23: C2 NaN NaN + M24: (Intercept) NaN NaN + M24: C2 NaN NaN + + + $m3a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + M12 NaN NaN + M13 NaN NaN + M14 NaN NaN + sigma_C1 NaN NaN + + + $m3b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + M22 NaN NaN + M23 NaN NaN + M24 NaN NaN + sigma_C1 NaN NaN + + + $m4a + Potential scale reduction factors: + + Point est. Upper C.I. + M12: (Intercept) NaN NaN + M12: M22 NaN NaN + M12: M23 NaN NaN + M12: M24 NaN NaN + M12: O22 NaN NaN + M12: O23 NaN NaN + M12: O24 NaN NaN + M12: abs(C1 - C2) NaN NaN + M12: log(C1) NaN NaN + M12: O22:abs(C1 - C2) NaN NaN + M12: O23:abs(C1 - C2) NaN NaN + M12: O24:abs(C1 - C2) NaN NaN + M13: (Intercept) NaN NaN + M13: M22 NaN NaN + M13: M23 NaN NaN + M13: M24 NaN NaN + M13: O22 NaN NaN + M13: O23 NaN NaN + M13: O24 NaN NaN + M13: abs(C1 - C2) NaN NaN + M13: log(C1) NaN NaN + M13: O22:abs(C1 - C2) NaN NaN + M13: O23:abs(C1 - C2) NaN NaN + M13: O24:abs(C1 - C2) NaN NaN + M14: (Intercept) NaN NaN + M14: M22 NaN NaN + M14: M23 NaN NaN + M14: M24 NaN NaN + M14: O22 NaN NaN + M14: O23 NaN NaN + M14: O24 NaN NaN + M14: abs(C1 - C2) NaN NaN + M14: log(C1) NaN NaN + M14: O22:abs(C1 - C2) NaN NaN + M14: O23:abs(C1 - C2) NaN NaN + M14: O24:abs(C1 - C2) NaN NaN + + + $m4b + Potential scale reduction factors: + + Point est. + M12: (Intercept) NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M12: abs(C1 - C2) NaN + M12: log(C1) NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + M13: (Intercept) NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M13: abs(C1 - C2) NaN + M13: log(C1) NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + M14: (Intercept) NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M14: abs(C1 - C2) NaN + M14: log(C1) NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + Upper C.I. + M12: (Intercept) NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M12: abs(C1 - C2) NaN + M12: log(C1) NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + M13: (Intercept) NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M13: abs(C1 - C2) NaN + M13: log(C1) NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + M14: (Intercept) NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M14: abs(C1 - C2) NaN + M14: log(C1) NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + + + +--- + + Code + lapply(models0, MC_error) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + est MCSE SD MCSE/SD + M12: (Intercept) 0 0 0 NaN + M13: (Intercept) 0 0 0 NaN + M14: (Intercept) 0 0 0 NaN + + $m0b + est MCSE SD MCSE/SD + M22: (Intercept) 0 0 0 NaN + M23: (Intercept) 0 0 0 NaN + M24: (Intercept) 0 0 0 NaN + + $m1a + est MCSE SD MCSE/SD + M12: (Intercept) 0 0 0 NaN + M12: C1 0 0 0 NaN + M13: (Intercept) 0 0 0 NaN + M13: C1 0 0 0 NaN + M14: (Intercept) 0 0 0 NaN + M14: C1 0 0 0 NaN + + $m1b + est MCSE SD MCSE/SD + M22: (Intercept) 0 0 0 NaN + M22: C1 0 0 0 NaN + M23: (Intercept) 0 0 0 NaN + M23: C1 0 0 0 NaN + M24: (Intercept) 0 0 0 NaN + M24: C1 0 0 0 NaN + + $m2a + est MCSE SD MCSE/SD + M12: (Intercept) 0 0 0 NaN + M12: C2 0 0 0 NaN + M13: (Intercept) 0 0 0 NaN + M13: C2 0 0 0 NaN + M14: (Intercept) 0 0 0 NaN + M14: C2 0 0 0 NaN + + $m2b + est MCSE SD MCSE/SD + M22: (Intercept) 0 0 0 NaN + M22: C2 0 0 0 NaN + M23: (Intercept) 0 0 0 NaN + M23: C2 0 0 0 NaN + M24: (Intercept) 0 0 0 NaN + M24: C2 0 0 0 NaN + + $m3a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + M12 0 0 0 NaN + M13 0 0 0 NaN + M14 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m3b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + M22 0 0 0 NaN + M23 0 0 0 NaN + M24 0 0 0 NaN + sigma_C1 0 0 0 NaN + + $m4a + est MCSE SD MCSE/SD + M12: (Intercept) 0 0 0 NaN + M12: M22 0 0 0 NaN + M12: M23 0 0 0 NaN + M12: M24 0 0 0 NaN + M12: O22 0 0 0 NaN + M12: O23 0 0 0 NaN + M12: O24 0 0 0 NaN + M12: abs(C1 - C2) 0 0 0 NaN + M12: log(C1) 0 0 0 NaN + M12: O22:abs(C1 - C2) 0 0 0 NaN + M12: O23:abs(C1 - C2) 0 0 0 NaN + M12: O24:abs(C1 - C2) 0 0 0 NaN + M13: (Intercept) 0 0 0 NaN + M13: M22 0 0 0 NaN + M13: M23 0 0 0 NaN + M13: M24 0 0 0 NaN + M13: O22 0 0 0 NaN + M13: O23 0 0 0 NaN + M13: O24 0 0 0 NaN + M13: abs(C1 - C2) 0 0 0 NaN + M13: log(C1) 0 0 0 NaN + M13: O22:abs(C1 - C2) 0 0 0 NaN + M13: O23:abs(C1 - C2) 0 0 0 NaN + M13: O24:abs(C1 - C2) 0 0 0 NaN + M14: (Intercept) 0 0 0 NaN + M14: M22 0 0 0 NaN + M14: M23 0 0 0 NaN + M14: M24 0 0 0 NaN + M14: O22 0 0 0 NaN + M14: O23 0 0 0 NaN + M14: O24 0 0 0 NaN + M14: abs(C1 - C2) 0 0 0 NaN + M14: log(C1) 0 0 0 NaN + M14: O22:abs(C1 - C2) 0 0 0 NaN + M14: O23:abs(C1 - C2) 0 0 0 NaN + M14: O24:abs(C1 - C2) 0 0 0 NaN + + $m4b + est MCSE SD + M12: (Intercept) 0 0 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M12: abs(C1 - C2) 0 0 0 + M12: log(C1) 0 0 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + M13: (Intercept) 0 0 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M13: abs(C1 - C2) 0 0 0 + M13: log(C1) 0 0 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + M14: (Intercept) 0 0 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M14: abs(C1 - C2) 0 0 0 + M14: log(C1) 0 0 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + MCSE/SD + M12: (Intercept) NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M12: abs(C1 - C2) NaN + M12: log(C1) NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + M13: (Intercept) NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M13: abs(C1 - C2) NaN + M13: log(C1) NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + M14: (Intercept) NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN + M14: abs(C1 - C2) NaN + M14: log(C1) NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN + + +# summary output remained the same + + Code + lapply(models0, print) + Output + + Call: + mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) (Intercept) (Intercept) + 0 0 0 + + Call: + mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M2" + + + Coefficients: + (Intercept) (Intercept) (Intercept) + 0 0 0 + + Call: + mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) C1 (Intercept) C1 (Intercept) C1 + 0 0 0 0 0 0 + + Call: + mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M2" + + + Coefficients: + (Intercept) C1 (Intercept) C1 (Intercept) C1 + 0 0 0 0 0 0 + + Call: + mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) C2 (Intercept) C2 (Intercept) C2 + 0 0 0 0 0 0 + + Call: + mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M2" + + + Coefficients: + (Intercept) C2 (Intercept) C2 (Intercept) C2 + 0 0 0 0 0 0 + + Call: + lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) M12 M13 M14 + 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + Call: + mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"), + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + Call: + mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1), + 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + $m0a + + Call: + mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) (Intercept) (Intercept) + 0 0 0 + + $m0b + + Call: + mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M2" + + + Coefficients: + (Intercept) (Intercept) (Intercept) + 0 0 0 + + $m1a + + Call: + mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) C1 (Intercept) C1 (Intercept) C1 + 0 0 0 0 0 0 + + $m1b + + Call: + mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M2" + + + Coefficients: + (Intercept) C1 (Intercept) C1 (Intercept) C1 + 0 0 0 0 0 0 + + $m2a + + Call: + mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) C2 (Intercept) C2 (Intercept) C2 + 0 0 0 0 0 0 + + $m2b + + Call: + mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M2" + + + Coefficients: + (Intercept) C2 (Intercept) C2 (Intercept) C2 + 0 0 0 0 0 0 + + $m3a + + Call: + lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) M12 M13 M14 + 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m3b + + Call: + lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear model for "C1" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + + + Residual standard deviation: + sigma_C1 + 0 + + $m4a + + Call: + mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"), + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + $m4b + + Call: + mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1), + 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian multinomial logit model for "M1" + + + Coefficients: + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + + +--- + + Code + lapply(models0, coef) + Output + $m0a + $m0a$M1 + (Intercept) (Intercept) (Intercept) + 0 0 0 + + + $m0b + $m0b$M2 + (Intercept) (Intercept) (Intercept) + 0 0 0 + + + $m1a + $m1a$M1 + (Intercept) C1 (Intercept) C1 (Intercept) C1 + 0 0 0 0 0 0 + + + $m1b + $m1b$M2 + (Intercept) C1 (Intercept) C1 (Intercept) C1 + 0 0 0 0 0 0 + + + $m2a + $m2a$M1 + (Intercept) C2 (Intercept) C2 (Intercept) C2 + 0 0 0 0 0 0 + + + $m2b + $m2b$M2 + (Intercept) C2 (Intercept) C2 (Intercept) C2 + 0 0 0 0 0 0 + + + $m3a + $m3a$C1 + (Intercept) M12 M13 M14 sigma_C1 + 0 0 0 0 0 + + + $m3b + $m3b$C1 + (Intercept) M22 M23 M24 sigma_C1 + 0 0 0 0 0 + + + $m4a + $m4a$M1 + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + (Intercept) M22 M23 M24 + 0 0 0 0 + O22 O23 O24 abs(C1 - C2) + 0 0 0 0 + log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) + 0 0 0 0 + + + $m4b + $m4b$M1 + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + (Intercept) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) + 0 + + + +--- + + Code + lapply(models0, confint) + Output + $m0a + $m0a$M1 + 2.5% 97.5% + (Intercept) 0 0 + (Intercept) 0 0 + (Intercept) 0 0 + + + $m0b + $m0b$M2 + 2.5% 97.5% + (Intercept) 0 0 + (Intercept) 0 0 + (Intercept) 0 0 + + + $m1a + $m1a$M1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + (Intercept) 0 0 + C1 0 0 + (Intercept) 0 0 + C1 0 0 + + + $m1b + $m1b$M2 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + (Intercept) 0 0 + C1 0 0 + (Intercept) 0 0 + C1 0 0 + + + $m2a + $m2a$M1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + (Intercept) 0 0 + C2 0 0 + (Intercept) 0 0 + C2 0 0 + + + $m2b + $m2b$M2 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + (Intercept) 0 0 + C2 0 0 + (Intercept) 0 0 + C2 0 0 + + + $m3a + $m3a$C1 + 2.5% 97.5% + (Intercept) 0 0 + M12 0 0 + M13 0 0 + M14 0 0 + sigma_C1 0 0 + + + $m3b + $m3b$C1 + 2.5% 97.5% + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + sigma_C1 0 0 + + + $m4a + $m4a$M1 + 2.5% 97.5% + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + O22:abs(C1 - C2) 0 0 + O23:abs(C1 - C2) 0 0 + O24:abs(C1 - C2) 0 0 + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + O22:abs(C1 - C2) 0 0 + O23:abs(C1 - C2) 0 0 + O24:abs(C1 - C2) 0 0 + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + O22 0 0 + O23 0 0 + O24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + O22:abs(C1 - C2) 0 0 + O23:abs(C1 - C2) 0 0 + O24:abs(C1 - C2) 0 0 + + + $m4b + $m4b$M1 + 2.5% 97.5% + (Intercept) 0 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 + (Intercept) 0 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 + (Intercept) 0 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 + + + +--- + + Code + lapply(models0, summary) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + + Bayesian multinomial logit model fitted with JointAI + + Call: + mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M12: (Intercept) 0 0 0 0 0 NaN NaN + M13: (Intercept) 0 0 0 0 0 NaN NaN + M14: (Intercept) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m0b + + Bayesian multinomial logit model fitted with JointAI + + Call: + mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22: (Intercept) 0 0 0 0 0 NaN NaN + M23: (Intercept) 0 0 0 0 0 NaN NaN + M24: (Intercept) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m1a + + Bayesian multinomial logit model fitted with JointAI + + Call: + mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M12: (Intercept) 0 0 0 0 0 NaN NaN + M12: C1 0 0 0 0 0 NaN NaN + M13: (Intercept) 0 0 0 0 0 NaN NaN + M13: C1 0 0 0 0 0 NaN NaN + M14: (Intercept) 0 0 0 0 0 NaN NaN + M14: C1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m1b + + Bayesian multinomial logit model fitted with JointAI + + Call: + mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22: (Intercept) 0 0 0 0 0 NaN NaN + M22: C1 0 0 0 0 0 NaN NaN + M23: (Intercept) 0 0 0 0 0 NaN NaN + M23: C1 0 0 0 0 0 NaN NaN + M24: (Intercept) 0 0 0 0 0 NaN NaN + M24: C1 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m2a + + Bayesian multinomial logit model fitted with JointAI + + Call: + mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M12: (Intercept) 0 0 0 0 0 NaN NaN + M12: C2 0 0 0 0 0 NaN NaN + M13: (Intercept) 0 0 0 0 0 NaN NaN + M13: C2 0 0 0 0 0 NaN NaN + M14: (Intercept) 0 0 0 0 0 NaN NaN + M14: C2 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m2b + + Bayesian multinomial logit model fitted with JointAI + + Call: + mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22: (Intercept) 0 0 0 0 0 NaN NaN + M22: C2 0 0 0 0 0 NaN NaN + M23: (Intercept) 0 0 0 0 0 NaN NaN + M23: C2 0 0 0 0 0 NaN NaN + M24: (Intercept) 0 0 0 0 0 NaN NaN + M24: C2 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m3a + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M12 0 0 0 0 0 NaN NaN + M13 0 0 0 0 0 NaN NaN + M14 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m3b + + Bayesian linear model fitted with JointAI + + Call: + lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10, + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_C1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m4a + + Bayesian multinomial logit model fitted with JointAI + + Call: + mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, + n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"), + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M12: (Intercept) 0 0 0 0 0 NaN NaN + M12: M22 0 0 0 0 0 NaN NaN + M12: M23 0 0 0 0 0 NaN NaN + M12: M24 0 0 0 0 0 NaN NaN + M12: O22 0 0 0 0 0 NaN NaN + M12: O23 0 0 0 0 0 NaN NaN + M12: O24 0 0 0 0 0 NaN NaN + M12: abs(C1 - C2) 0 0 0 0 0 NaN NaN + M12: log(C1) 0 0 0 0 0 NaN NaN + M12: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M12: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M12: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M13: (Intercept) 0 0 0 0 0 NaN NaN + M13: M22 0 0 0 0 0 NaN NaN + M13: M23 0 0 0 0 0 NaN NaN + M13: M24 0 0 0 0 0 NaN NaN + M13: O22 0 0 0 0 0 NaN NaN + M13: O23 0 0 0 0 0 NaN NaN + M13: O24 0 0 0 0 0 NaN NaN + M13: abs(C1 - C2) 0 0 0 0 0 NaN NaN + M13: log(C1) 0 0 0 0 0 NaN NaN + M13: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M13: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M13: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M14: (Intercept) 0 0 0 0 0 NaN NaN + M14: M22 0 0 0 0 0 NaN NaN + M14: M23 0 0 0 0 0 NaN NaN + M14: M24 0 0 0 0 0 NaN NaN + M14: O22 0 0 0 0 0 NaN NaN + M14: O23 0 0 0 0 0 NaN NaN + M14: O24 0 0 0 0 0 NaN NaN + M14: abs(C1 - C2) 0 0 0 0 0 NaN NaN + M14: log(C1) 0 0 0 0 0 NaN NaN + M14: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M14: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M14: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + $m4b + + Bayesian multinomial logit model fitted with JointAI + + Call: + mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1), + 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, + n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% + M12: (Intercept) 0 0 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M12: abs(C1 - C2) 0 0 0 + M12: log(C1) 0 0 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + M13: (Intercept) 0 0 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M13: abs(C1 - C2) 0 0 0 + M13: log(C1) 0 0 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + M14: (Intercept) 0 0 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M14: abs(C1 - C2) 0 0 0 + M14: log(C1) 0 0 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + 97.5% + M12: (Intercept) 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M12: abs(C1 - C2) 0 + M12: log(C1) 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + M13: (Intercept) 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M13: abs(C1 - C2) 0 + M13: log(C1) 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + M14: (Intercept) 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M14: abs(C1 - C2) 0 + M14: log(C1) 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + tail-prob. + M12: (Intercept) 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M12: abs(C1 - C2) 0 + M12: log(C1) 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + M13: (Intercept) 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M13: abs(C1 - C2) 0 + M13: log(C1) 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + M14: (Intercept) 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M14: abs(C1 - C2) 0 + M14: log(C1) 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + GR-crit MCE/SD + M12: (Intercept) NaN NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN + M12: abs(C1 - C2) NaN NaN + M12: log(C1) NaN NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN + M13: (Intercept) NaN NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN + M13: abs(C1 - C2) NaN NaN + M13: log(C1) NaN NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN + M14: (Intercept) NaN NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN + M14: abs(C1 - C2) NaN NaN + M14: log(C1) NaN NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 100 + + +--- + + Code + lapply(models0, function(x) coef(summary(x))) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + $m0a$M1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M12: (Intercept) 0 0 0 0 0 NaN NaN + M13: (Intercept) 0 0 0 0 0 NaN NaN + M14: (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b + $m0b$M2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22: (Intercept) 0 0 0 0 0 NaN NaN + M23: (Intercept) 0 0 0 0 0 NaN NaN + M24: (Intercept) 0 0 0 0 0 NaN NaN + + + $m1a + $m1a$M1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M12: (Intercept) 0 0 0 0 0 NaN NaN + M12: C1 0 0 0 0 0 NaN NaN + M13: (Intercept) 0 0 0 0 0 NaN NaN + M13: C1 0 0 0 0 0 NaN NaN + M14: (Intercept) 0 0 0 0 0 NaN NaN + M14: C1 0 0 0 0 0 NaN NaN + + + $m1b + $m1b$M2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22: (Intercept) 0 0 0 0 0 NaN NaN + M22: C1 0 0 0 0 0 NaN NaN + M23: (Intercept) 0 0 0 0 0 NaN NaN + M23: C1 0 0 0 0 0 NaN NaN + M24: (Intercept) 0 0 0 0 0 NaN NaN + M24: C1 0 0 0 0 0 NaN NaN + + + $m2a + $m2a$M1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M12: (Intercept) 0 0 0 0 0 NaN NaN + M12: C2 0 0 0 0 0 NaN NaN + M13: (Intercept) 0 0 0 0 0 NaN NaN + M13: C2 0 0 0 0 0 NaN NaN + M14: (Intercept) 0 0 0 0 0 NaN NaN + M14: C2 0 0 0 0 0 NaN NaN + + + $m2b + $m2b$M2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M22: (Intercept) 0 0 0 0 0 NaN NaN + M22: C2 0 0 0 0 0 NaN NaN + M23: (Intercept) 0 0 0 0 0 NaN NaN + M23: C2 0 0 0 0 0 NaN NaN + M24: (Intercept) 0 0 0 0 0 NaN NaN + M24: C2 0 0 0 0 0 NaN NaN + + + $m3a + $m3a$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M12 0 0 0 0 0 NaN NaN + M13 0 0 0 0 0 NaN NaN + M14 0 0 0 0 0 NaN NaN + + + $m3b + $m3b$C1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + M22 0 0 0 0 0 NaN NaN + M23 0 0 0 0 0 NaN NaN + M24 0 0 0 0 0 NaN NaN + + + $m4a + $m4a$M1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + M12: (Intercept) 0 0 0 0 0 NaN NaN + M12: M22 0 0 0 0 0 NaN NaN + M12: M23 0 0 0 0 0 NaN NaN + M12: M24 0 0 0 0 0 NaN NaN + M12: O22 0 0 0 0 0 NaN NaN + M12: O23 0 0 0 0 0 NaN NaN + M12: O24 0 0 0 0 0 NaN NaN + M12: abs(C1 - C2) 0 0 0 0 0 NaN NaN + M12: log(C1) 0 0 0 0 0 NaN NaN + M12: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M12: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M12: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M13: (Intercept) 0 0 0 0 0 NaN NaN + M13: M22 0 0 0 0 0 NaN NaN + M13: M23 0 0 0 0 0 NaN NaN + M13: M24 0 0 0 0 0 NaN NaN + M13: O22 0 0 0 0 0 NaN NaN + M13: O23 0 0 0 0 0 NaN NaN + M13: O24 0 0 0 0 0 NaN NaN + M13: abs(C1 - C2) 0 0 0 0 0 NaN NaN + M13: log(C1) 0 0 0 0 0 NaN NaN + M13: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M13: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M13: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M14: (Intercept) 0 0 0 0 0 NaN NaN + M14: M22 0 0 0 0 0 NaN NaN + M14: M23 0 0 0 0 0 NaN NaN + M14: M24 0 0 0 0 0 NaN NaN + M14: O22 0 0 0 0 0 NaN NaN + M14: O23 0 0 0 0 0 NaN NaN + M14: O24 0 0 0 0 0 NaN NaN + M14: abs(C1 - C2) 0 0 0 0 0 NaN NaN + M14: log(C1) 0 0 0 0 0 NaN NaN + M14: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M14: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN + M14: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + $m4b + $m4b$M1 + Mean SD 2.5% + M12: (Intercept) 0 0 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M12: abs(C1 - C2) 0 0 0 + M12: log(C1) 0 0 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + M13: (Intercept) 0 0 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M13: abs(C1 - C2) 0 0 0 + M13: log(C1) 0 0 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + M14: (Intercept) 0 0 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 + M14: abs(C1 - C2) 0 0 0 + M14: log(C1) 0 0 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 + 97.5% + M12: (Intercept) 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M12: abs(C1 - C2) 0 + M12: log(C1) 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + M13: (Intercept) 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M13: abs(C1 - C2) 0 + M13: log(C1) 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + M14: (Intercept) 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M14: abs(C1 - C2) 0 + M14: log(C1) 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + tail-prob. + M12: (Intercept) 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M12: abs(C1 - C2) 0 + M12: log(C1) 0 + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + M13: (Intercept) 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M13: abs(C1 - C2) 0 + M13: log(C1) 0 + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + M14: (Intercept) 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 + M14: abs(C1 - C2) 0 + M14: log(C1) 0 + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 + GR-crit MCE/SD + M12: (Intercept) NaN NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN + M12: abs(C1 - C2) NaN NaN + M12: log(C1) NaN NaN + M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN + M13: (Intercept) NaN NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN + M13: abs(C1 - C2) NaN NaN + M13: log(C1) NaN NaN + M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN + M14: (Intercept) NaN NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN + M14: abs(C1 - C2) NaN NaN + M14: log(C1) NaN NaN + M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN + + + diff --git a/tests/testthat/_snaps/mlogitmm.md b/tests/testthat/_snaps/mlogitmm.md new file mode 100644 index 00000000..c639806d --- /dev/null +++ b/tests/testthat/_snaps/mlogitmm.md @@ -0,0 +1,12527 @@ +# data_list remains the same + + Code + lapply(models, "[[", "data_list") + Output + $m0a + $m0a$M_id + (Intercept) + 1 1 + 2 1 + 3 1 + 4 1 + 5 1 + 6 1 + 7 1 + 8 1 + 9 1 + 10 1 + 11 1 + 12 1 + 13 1 + 14 1 + 15 1 + 16 1 + 17 1 + 18 1 + 19 1 + 20 1 + 21 1 + 22 1 + 23 1 + 24 1 + 25 1 + 26 1 + 27 1 + 28 1 + 29 1 + 30 1 + 31 1 + 32 1 + 33 1 + 34 1 + 35 1 + 36 1 + 37 1 + 38 1 + 39 1 + 40 1 + 41 1 + 42 1 + 43 1 + 44 1 + 45 1 + 46 1 + 47 1 + 48 1 + 49 1 + 50 1 + 51 1 + 52 1 + 53 1 + 54 1 + 55 1 + 56 1 + 57 1 + 58 1 + 59 1 + 60 1 + 61 1 + 62 1 + 63 1 + 64 1 + 65 1 + 66 1 + 67 1 + 68 1 + 69 1 + 70 1 + 71 1 + 72 1 + 73 1 + 74 1 + 75 1 + 76 1 + 77 1 + 78 1 + 79 1 + 80 1 + 81 1 + 82 1 + 83 1 + 84 1 + 85 1 + 86 1 + 87 1 + 88 1 + 89 1 + 90 1 + 91 1 + 92 1 + 93 1 + 94 1 + 95 1 + 96 1 + 97 1 + 98 1 + 99 1 + 100 1 + + $m0a$M_lvlone + m1 + 1 2 + 1.1 2 + 1.2 2 + 1.3 2 + 2 1 + 2.1 1 + 2.2 1 + 3 2 + 3.1 1 + 3.2 2 + 4 3 + 4.1 3 + 4.2 3 + 4.3 2 + 5 2 + 5.1 2 + 5.2 3 + 5.3 2 + 6 2 + 7 1 + 7.1 1 + 7.2 1 + 8 3 + 8.1 3 + 8.2 3 + 8.3 1 + 8.4 2 + 8.5 3 + 9 2 + 9.1 2 + 9.2 3 + 10 1 + 10.1 3 + 11 3 + 11.1 1 + 11.2 2 + 11.3 1 + 11.4 3 + 12 2 + 13 1 + 13.1 3 + 14 1 + 14.1 3 + 14.2 3 + 14.3 2 + 15 2 + 15.1 2 + 15.2 3 + 15.3 3 + 16 1 + 16.1 2 + 16.2 1 + 16.3 3 + 16.4 2 + 16.5 3 + 17 1 + 17.1 2 + 17.2 1 + 17.3 2 + 17.4 2 + 18 1 + 19 2 + 19.1 1 + 19.2 2 + 19.3 3 + 20 2 + 20.1 3 + 20.2 2 + 20.3 2 + 20.4 1 + 20.5 2 + 21 3 + 21.1 2 + 21.2 2 + 22 2 + 22.1 1 + 23 1 + 23.1 1 + 24 3 + 25 3 + 25.1 3 + 25.2 1 + 25.3 1 + 25.4 2 + 25.5 2 + 26 1 + 26.1 1 + 26.2 2 + 26.3 1 + 27 1 + 27.1 2 + 28 3 + 28.1 2 + 28.2 3 + 28.3 2 + 29 1 + 29.1 3 + 29.2 1 + 29.3 3 + 30 2 + 30.1 2 + 30.2 1 + 31 3 + 32 3 + 32.1 1 + 32.2 1 + 32.3 3 + 33 3 + 33.1 2 + 34 3 + 34.1 2 + 34.2 1 + 34.3 2 + 35 3 + 35.1 1 + 35.2 2 + 36 1 + 36.1 2 + 36.2 3 + 36.3 2 + 36.4 3 + 37 1 + 37.1 2 + 37.2 3 + 38 1 + 39 3 + 39.1 3 + 39.2 3 + 39.3 3 + 39.4 2 + 39.5 2 + 40 2 + 40.1 3 + 40.2 2 + 40.3 2 + 41 1 + 41.1 2 + 41.2 2 + 41.3 2 + 41.4 1 + 42 3 + 42.1 2 + 43 3 + 43.1 3 + 43.2 1 + 44 2 + 44.1 2 + 44.2 3 + 44.3 1 + 45 1 + 45.1 1 + 46 2 + 46.1 1 + 46.2 3 + 47 1 + 47.1 1 + 47.2 2 + 47.3 3 + 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56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m3b$shape_diag_RinvD + [1] "0.01" + + $m3b$rate_diag_RinvD + [1] "0.001" + + + $m4a + $m4a$M_id + M2 C2 (Intercept) M22 M23 M24 abs(C1 - C2) log(C1) C1 + 1 NA -1.381594459 1 NA NA NA NA -0.3318617 0.7175865 + 2 1 0.344426024 1 NA NA NA NA -0.2867266 0.7507170 + 3 2 NA 1 NA NA NA NA -0.3207627 0.7255954 + 4 2 -0.228910007 1 NA NA NA NA -0.2917769 0.7469352 + 5 1 NA 1 NA NA NA NA -0.3369956 0.7139120 + 6 NA -2.143955482 1 NA NA NA NA -0.3102679 0.7332505 + 7 NA -1.156567023 1 NA NA NA 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NA + 98.1 1 3 NA NA NA NA + 98.2 3 1 NA NA NA NA + 99 1 2 NA NA NA NA + 99.1 1 2 NA NA NA NA + 99.2 2 NA NA NA NA NA + 100 3 2 NA NA NA NA + 100.1 2 NA NA NA NA NA + 100.2 2 3 NA NA NA NA + 100.3 3 3 NA NA NA NA + 100.4 1 3 NA NA NA NA + + $m4a$spM_id + center scale + M2 NA NA + C2 -0.6240921 0.68571078 + (Intercept) NA NA + M22 NA NA + M23 NA NA + M24 NA NA + abs(C1 - C2) 1.3664060 0.67847389 + log(C1) -0.3049822 0.01990873 + C1 0.7372814 0.01472882 + + $m4a$spM_lvlone + center scale + m1 NA NA + m2 NA NA + m2B NA NA + m2C NA NA + m2B:abs(C1 - C2) 0.4354752 0.7317719 + m2C:abs(C1 - C2) 0.4780416 0.7965218 + + $m4a$mu_reg_norm + [1] 0 + + $m4a$tau_reg_norm + [1] 1e-04 + + $m4a$shape_tau_norm + [1] 0.01 + + $m4a$rate_tau_norm + [1] 0.01 + + $m4a$mu_reg_multinomial + [1] 0 + + $m4a$tau_reg_multinomial + [1] 1e-04 + + $m4a$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m4a$shape_diag_RinvD + [1] "0.01" + + $m4a$rate_diag_RinvD + [1] "0.001" + + + $m4b + $m4b$M_id + C2 (Intercept) abs(C1 - C2) log(C1) M12 M13 M14 C1 M1 + 1 -1.381594459 1 NA -0.3318617 0 0 0 0.7175865 1 + 2 0.344426024 1 NA -0.2867266 0 0 1 0.7507170 4 + 3 NA 1 NA -0.3207627 0 0 0 0.7255954 1 + 4 -0.228910007 1 NA -0.2917769 0 0 0 0.7469352 1 + 5 NA 1 NA -0.3369956 0 0 1 0.7139120 4 + 6 -2.143955482 1 NA -0.3102679 0 0 1 0.7332505 4 + 7 -1.156567023 1 NA -0.3084388 0 0 1 0.7345929 4 + 8 -0.598827660 1 NA -0.2675411 0 0 1 0.7652589 4 + 9 NA 1 NA -0.3284176 1 0 0 0.7200622 2 + 10 -1.006719032 1 NA -0.2978834 0 0 0 0.7423879 1 + 11 0.239801450 1 NA -0.2960573 0 1 0 0.7437448 3 + 12 -1.064969789 1 NA -0.2948450 0 0 0 0.7446470 1 + 13 -0.538082688 1 NA -0.2836654 0 1 0 0.7530186 3 + 14 NA 1 NA -0.3434574 0 1 0 0.7093137 3 + 15 -1.781049276 1 NA -0.3104469 0 0 1 0.7331192 4 + 16 NA 1 NA -0.3550492 0 0 0 0.7011390 1 + 17 NA 1 NA -0.2967369 1 0 0 0.7432395 2 + 18 -0.014579883 1 NA -0.2816747 0 1 0 0.7545191 3 + 19 -2.121550136 1 NA -0.2838910 0 0 0 0.7528487 1 + 20 NA 1 NA -0.2727455 1 0 0 0.7612865 2 + 21 -0.363239698 1 NA -0.3213465 1 0 0 0.7251719 2 + 22 -0.121568514 1 NA -0.3146245 0 0 1 0.7300630 4 + 23 -0.951271111 1 NA -0.3442879 0 0 1 0.7087249 4 + 24 NA 1 NA -0.3021952 0 0 0 0.7391938 1 + 25 -0.974288621 1 NA -0.2458186 0 0 0 0.7820641 1 + 26 -1.130632418 1 NA -0.3399165 0 1 0 0.7118298 3 + 27 0.114339868 1 NA -0.3242275 0 0 0 0.7230857 1 + 28 0.238334648 1 NA -0.2891027 0 0 1 0.7489353 4 + 29 0.840744958 1 NA -0.2862314 0 0 0 0.7510888 1 + 30 NA 1 NA -0.3146125 0 1 0 0.7300717 3 + 31 NA 1 NA -0.2809421 0 1 0 0.7550721 3 + 32 -1.466312154 1 NA -0.3117155 0 0 0 0.7321898 1 + 33 -0.637352277 1 NA -0.3138326 0 1 0 0.7306414 3 + 34 NA 1 NA -0.2974340 0 0 1 0.7427216 4 + 35 NA 1 NA -0.3294709 0 0 1 0.7193042 4 + 36 NA 1 NA -0.3129468 0 0 0 0.7312888 1 + 37 NA 1 NA -0.3424289 1 0 0 0.7100436 2 + 38 NA 1 NA -0.2652444 0 0 1 0.7670184 4 + 39 0.006728205 1 NA -0.3010445 0 1 0 0.7400449 3 + 40 NA 1 NA -0.3014695 1 0 0 0.7397304 2 + 41 -1.663281353 1 NA -0.2888874 1 0 0 0.7490966 2 + 42 0.161184794 1 NA -0.2985038 0 0 0 0.7419274 1 + 43 0.457939180 1 NA -0.2839809 0 0 0 0.7527810 1 + 44 -0.307070331 1 NA -0.2999821 0 1 0 0.7408315 3 + 45 NA 1 NA -0.3082181 1 0 0 0.7347550 2 + 46 -1.071668276 1 NA -0.3102825 1 0 0 0.7332398 2 + 47 -0.814751321 1 NA -0.3042884 0 0 0 0.7376481 1 + 48 -0.547630662 1 NA -0.3084048 0 0 0 0.7346179 1 + 49 NA 1 NA -0.3106911 0 0 0 0.7329402 1 + 50 -1.350213782 1 NA -0.3201451 1 0 0 0.7260436 2 + 51 0.719054706 1 NA -0.3225621 0 0 0 0.7242910 1 + 52 NA 1 NA -0.3149755 0 0 1 0.7298067 4 + 53 -1.207130750 1 NA -0.3209299 0 0 0 0.7254741 1 + 54 NA 1 NA -0.2820889 1 0 0 0.7542067 2 + 55 -0.408600991 1 NA -0.3024638 0 1 0 0.7389952 3 + 56 -0.271380529 1 NA -0.2849341 0 1 0 0.7520638 3 + 57 -1.361925974 1 NA -0.3257359 0 0 1 0.7219958 4 + 58 NA 1 NA -0.3202560 1 0 0 0.7259632 2 + 59 NA 1 NA -0.2932166 0 0 1 0.7458606 4 + 60 -0.323712205 1 NA -0.2649529 0 0 0 0.7672421 1 + 61 NA 1 NA -0.3205938 0 0 0 0.7257179 1 + 62 NA 1 NA -0.3299089 0 0 1 0.7189892 4 + 63 -1.386906880 1 NA -0.3101519 0 0 1 0.7333356 4 + 64 NA 1 NA -0.3119416 0 0 1 0.7320243 4 + 65 NA 1 NA -0.2906584 1 0 0 0.7477711 2 + 66 -0.565191691 1 NA -0.3087049 0 1 0 0.7343974 3 + 67 -0.382899912 1 NA -0.2887994 1 0 0 0.7491624 2 + 68 NA 1 NA -0.2899866 0 0 1 0.7482736 4 + 69 -0.405642769 1 NA -0.3094824 0 0 0 0.7338267 1 + 70 NA 1 NA -0.2734187 0 0 1 0.7607742 4 + 71 -0.843748427 1 NA -0.2513372 0 0 1 0.7777600 4 + 72 0.116003683 1 NA -0.3000053 0 0 1 0.7408143 4 + 73 -0.778634325 1 NA -0.3218221 0 0 0 0.7248271 1 + 74 NA 1 NA -0.3058575 0 1 0 0.7364916 3 + 75 NA 1 NA -0.2923695 0 0 1 0.7464926 4 + 76 NA 1 NA -0.3071463 1 0 0 0.7355430 2 + 77 -0.632974758 1 NA -0.3273313 1 0 0 0.7208449 2 + 78 NA 1 NA -0.3046827 0 0 0 0.7373573 1 + 79 -0.778064615 1 NA -0.2746896 1 0 0 0.7598079 2 + 80 NA 1 NA -0.3064688 0 1 0 0.7360415 3 + 81 NA 1 NA -0.3155423 0 0 0 0.7293932 1 + 82 -0.246123253 1 NA -0.3175491 0 0 0 0.7279309 1 + 83 -1.239659782 1 NA -0.3086139 1 0 0 0.7344643 2 + 84 -0.467772280 1 NA -0.3032222 0 0 1 0.7384350 4 + 85 NA 1 NA -0.3114673 0 1 0 0.7323716 3 + 86 -2.160485036 1 NA -0.2775210 1 0 0 0.7576597 2 + 87 -0.657675572 1 NA -0.2881970 0 0 1 0.7496139 4 + 88 NA 1 NA -0.3181084 0 1 0 0.7275239 3 + 89 -0.696710744 1 NA -0.3214942 0 0 1 0.7250648 4 + 90 NA 1 NA -0.3098919 1 0 0 0.7335262 2 + 91 -0.179395847 1 NA -0.3087042 0 0 1 0.7343980 4 + 92 -0.441545568 1 NA -0.3037539 1 0 0 0.7380425 2 + 93 -0.685799334 1 NA -0.3025305 0 0 1 0.7389460 4 + 94 NA 1 NA -0.3202120 0 0 1 0.7259951 4 + 95 0.191929445 1 NA -0.3170642 0 0 0 0.7282840 1 + 96 NA 1 NA -0.3172240 1 0 0 0.7281676 2 + 97 -0.069760671 1 NA -0.3221849 0 0 0 0.7245642 1 + 98 NA 1 NA -0.2840967 0 0 1 0.7526938 4 + 99 NA 1 NA -0.3185112 1 0 0 0.7272309 2 + 100 NA 1 NA -0.3033427 1 0 0 0.7383460 2 + + $m4b$M_lvlone + m1 m2 ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) + 1 2 3 NA + 1.1 2 3 NA + 1.2 2 1 NA + 1.3 2 2 NA + 2 1 3 NA + 2.1 1 1 NA + 2.2 1 NA NA + 3 2 1 NA + 3.1 1 1 NA + 3.2 2 3 NA + 4 3 2 NA + 4.1 3 3 NA + 4.2 3 3 NA + 4.3 2 2 NA + 5 2 1 NA + 5.1 2 1 NA + 5.2 3 3 NA + 5.3 2 3 NA + 6 2 2 NA + 7 1 3 NA + 7.1 1 1 NA + 7.2 1 1 NA + 8 3 3 NA + 8.1 3 NA NA + 8.2 3 2 NA + 8.3 1 1 NA + 8.4 2 1 NA + 8.5 3 3 NA + 9 2 NA NA + 9.1 2 2 NA + 9.2 3 2 NA + 10 1 2 NA + 10.1 3 2 NA + 11 3 3 NA + 11.1 1 2 NA + 11.2 2 1 NA + 11.3 1 2 NA + 11.4 3 1 NA + 12 2 3 NA + 13 1 NA NA + 13.1 3 2 NA + 14 1 2 NA + 14.1 3 1 NA + 14.2 3 3 NA + 14.3 2 2 NA + 15 2 NA NA + 15.1 2 1 NA + 15.2 3 1 NA + 15.3 3 3 NA + 16 1 3 NA + 16.1 2 2 NA + 16.2 1 NA NA + 16.3 3 3 NA + 16.4 2 1 NA + 16.5 3 NA NA + 17 1 3 NA + 17.1 2 1 NA + 17.2 1 3 NA + 17.3 2 3 NA + 17.4 2 1 NA + 18 1 1 NA + 19 2 3 NA + 19.1 1 2 NA + 19.2 2 1 NA + 19.3 3 1 NA + 20 2 1 NA + 20.1 3 3 NA + 20.2 2 2 NA + 20.3 2 NA NA + 20.4 1 3 NA + 20.5 2 3 NA + 21 3 1 NA + 21.1 2 2 NA + 21.2 2 3 NA + 22 2 NA NA + 22.1 1 2 NA + 23 1 2 NA + 23.1 1 2 NA + 24 3 1 NA + 25 3 NA NA + 25.1 3 1 NA + 25.2 1 1 NA + 25.3 1 2 NA + 25.4 2 NA NA + 25.5 2 2 NA + 26 1 3 NA + 26.1 1 1 NA + 26.2 2 2 NA + 26.3 1 1 NA + 27 1 1 NA + 27.1 2 3 NA + 28 3 3 NA + 28.1 2 NA NA + 28.2 3 NA NA + 28.3 2 NA NA + 29 1 3 NA + 29.1 3 1 NA + 29.2 1 1 NA + 29.3 3 2 NA + 30 2 3 NA + 30.1 2 2 NA + 30.2 1 2 NA + 31 3 NA NA + 32 3 1 NA + 32.1 1 NA NA + 32.2 1 3 NA + 32.3 3 2 NA + 33 3 1 NA + 33.1 2 2 NA + 34 3 1 NA + 34.1 2 3 NA + 34.2 1 3 NA + 34.3 2 3 NA + 35 3 1 NA + 35.1 1 3 NA + 35.2 2 2 NA + 36 1 3 NA + 36.1 2 NA NA + 36.2 3 3 NA + 36.3 2 NA NA + 36.4 3 NA NA + 37 1 NA NA + 37.1 2 2 NA + 37.2 3 3 NA + 38 1 2 NA + 39 3 NA NA + 39.1 3 2 NA + 39.2 3 1 NA + 39.3 3 NA NA + 39.4 2 2 NA + 39.5 2 1 NA + 40 2 3 NA + 40.1 3 3 NA + 40.2 2 NA NA + 40.3 2 2 NA + 41 1 1 NA + 41.1 2 2 NA + 41.2 2 3 NA + 41.3 2 3 NA + 41.4 1 1 NA + 42 3 2 NA + 42.1 2 NA NA + 43 3 2 NA + 43.1 3 3 NA + 43.2 1 3 NA + 44 2 3 NA + 44.1 2 1 NA + 44.2 3 2 NA + 44.3 1 1 NA + 45 1 3 NA + 45.1 1 2 NA + 46 2 2 NA + 46.1 1 3 NA + 46.2 3 2 NA + 47 1 3 NA + 47.1 1 3 NA + 47.2 2 2 NA + 47.3 3 3 NA + 47.4 2 2 NA + 48 3 NA NA + 48.1 3 1 NA + 49 1 2 NA + 50 2 NA NA + 51 2 NA NA + 52 3 2 NA + 52.1 3 3 NA + 52.2 2 3 NA + 52.3 2 2 NA + 52.4 1 2 NA + 52.5 2 1 NA + 53 3 2 NA + 53.1 2 2 NA + 53.2 1 3 NA + 54 1 NA NA + 54.1 3 3 NA + 54.2 1 3 NA + 54.3 3 1 NA + 54.4 1 NA NA + 55 3 1 NA + 55.1 3 1 NA + 55.2 1 3 NA + 55.3 3 1 NA + 55.4 1 1 NA + 56 3 3 NA + 56.1 3 1 NA + 56.2 2 2 NA + 56.3 3 2 NA + 56.4 1 3 NA + 56.5 2 2 NA + 57 2 3 NA + 57.1 1 2 NA + 57.2 3 NA NA + 57.3 2 1 NA + 58 2 1 NA + 58.1 3 NA NA + 58.2 1 2 NA + 58.3 1 3 NA + 58.4 1 3 NA + 58.5 1 2 NA + 59 1 2 NA + 59.1 1 3 NA + 60 3 NA NA + 61 1 NA NA + 61.1 2 NA NA + 61.2 1 1 NA + 61.3 1 2 NA + 61.4 2 3 NA + 62 2 3 NA + 62.1 3 3 NA + 62.2 3 2 NA + 62.3 1 2 NA + 63 1 1 NA + 63.1 2 1 NA + 64 3 1 NA + 65 3 2 NA + 65.1 3 NA NA + 65.2 2 2 NA + 65.3 2 1 NA + 66 2 3 NA + 66.1 3 1 NA + 66.2 2 3 NA + 67 3 3 NA + 68 3 2 NA + 68.1 1 3 NA + 68.2 3 1 NA + 68.3 3 2 NA + 68.4 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93.1 NA NA NA + 93.2 NA NA NA + 93.3 NA NA NA + 93.4 NA NA NA + 94 NA NA NA + 94.1 NA NA NA + 94.2 NA NA NA + 94.3 NA NA NA + 94.4 NA NA NA + 94.5 NA NA NA + 95 NA NA NA + 95.1 NA NA NA + 95.2 NA NA NA + 96 NA NA NA + 96.1 NA NA NA + 96.2 NA NA NA + 96.3 NA NA NA + 96.4 NA NA NA + 96.5 NA NA NA + 97 NA NA NA + 97.1 NA NA NA + 98 NA NA NA + 98.1 NA NA NA + 98.2 NA NA NA + 99 NA NA NA + 99.1 NA NA NA + 99.2 NA NA NA + 100 NA NA NA + 100.1 NA NA NA + 100.2 NA NA NA + 100.3 NA NA NA + 100.4 NA NA NA + + $m4b$spM_id + center scale + C2 -0.6240921 0.68571078 + (Intercept) NA NA + abs(C1 - C2) 1.3664060 0.67847389 + log(C1) -0.3049822 0.01990873 + M12 NA NA + M13 NA NA + M14 NA NA + C1 0.7372814 0.01472882 + M1 NA NA + + $m4b$spM_lvlone + center scale + m1 NA NA + m2 NA NA + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0.2974910 0.4579759 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.4760733 0.8309134 + m2B NA NA + m2C NA NA + + $m4b$mu_reg_norm + [1] 0 + + $m4b$tau_reg_norm 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85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m4c$shape_diag_RinvD + [1] "0.01" + + $m4c$rate_diag_RinvD + [1] "0.001" + + $m4c$RinvD_m1_id + [,1] [,2] [,3] [,4] + [1,] NA 0 0 0 + [2,] 0 NA 0 0 + [3,] 0 0 NA 0 + [4,] 0 0 0 NA + + $m4c$KinvD_m1_id + id + 5 + + + $m4d + $m4d$M_id + (Intercept) C1 + 1 1 0.7175865 + 2 1 0.7507170 + 3 1 0.7255954 + 4 1 0.7469352 + 5 1 0.7139120 + 6 1 0.7332505 + 7 1 0.7345929 + 8 1 0.7652589 + 9 1 0.7200622 + 10 1 0.7423879 + 11 1 0.7437448 + 12 1 0.7446470 + 13 1 0.7530186 + 14 1 0.7093137 + 15 1 0.7331192 + 16 1 0.7011390 + 17 1 0.7432395 + 18 1 0.7545191 + 19 1 0.7528487 + 20 1 0.7612865 + 21 1 0.7251719 + 22 1 0.7300630 + 23 1 0.7087249 + 24 1 0.7391938 + 25 1 0.7820641 + 26 1 0.7118298 + 27 1 0.7230857 + 28 1 0.7489353 + 29 1 0.7510888 + 30 1 0.7300717 + 31 1 0.7550721 + 32 1 0.7321898 + 33 1 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4.4639474002 1.992683e+01 NA 1.4628960401 3.2986159518 NA + 94 1 NA 0.8488043118 7.204688e-01 NA -0.2904211940 0.6162277526 NA + 94.1 1 0 1.0552454425 1.113543e+00 NA 0.0147813580 0.7661029975 NA + 94.2 2 0 1.9445500884 3.781275e+00 NA -0.4536774482 1.4117337932 NA + 94.3 2 NA 3.0710722448 9.431485e+00 NA 0.6793464917 2.2295833342 NA + 94.4 2 0 3.0872731935 9.531256e+00 NA -0.9411356550 2.2413451432 NA + 94.5 1 1 4.3805759016 1.918945e+01 NA 0.5683867264 3.1802765437 NA + 95 1 0 2.0199063048 4.080021e+00 NA 0.2375652188 1.4710654666 NA + 95.1 2 NA 4.0184444457 1.614790e+01 NA 0.0767152977 2.9265688411 NA + 95.2 2 0 4.5596531732 2.079044e+01 NA -0.6886731251 3.3207225043 NA + 96 2 0 0.0311333477 9.692853e-04 NA 0.7813892121 0.0226702966 NA + 96.1 3 0 0.1324267720 1.753685e-02 NA 0.3391519695 0.0964288912 NA + 96.2 1 0 0.6701303425 4.490747e-01 NA -0.4857246503 0.4879672359 NA + 96.3 3 NA 2.1775037691 4.741523e+00 NA 0.8771471244 1.5855877997 NA + 96.4 2 1 2.2246142488 4.948909e+00 NA 1.9030768981 1.6198921270 NA + 96.5 2 1 4.2377650598 1.795865e+01 NA -0.1684332749 3.0858034196 NA + 97 1 0 1.1955102731 1.429245e+00 NA 1.3775130083 0.8662239621 NA + 97.1 3 0 4.9603108643 2.460468e+01 NA -1.7323228619 3.5940637456 NA + 98 3 0 0.2041732438 4.168671e-02 NA -1.2648518889 0.1536799385 NA + 98.1 1 0 0.4309578973 1.857247e-01 NA -0.9042716241 0.3243793452 NA + 98.2 3 1 3.5172611906 1.237113e+01 NA -0.1560385207 2.6474207553 NA + 99 1 0 0.3531786101 1.247351e-01 NA 0.7993356425 0.2568424071 NA + 99.1 1 0 4.6789444226 2.189252e+01 NA 1.0355522332 3.4026730772 NA + 99.2 2 0 4.9927084171 2.492714e+01 NA -0.1150895843 3.6308519569 NA + 100 3 NA 1.0691387602 1.143058e+00 NA 0.0369067906 0.7893943379 NA + 100.1 2 NA 1.5109344281 2.282923e+00 NA 1.6023713093 1.1155924065 NA + 100.2 2 0 2.1502332564 4.623503e+00 NA 0.8861545820 1.5876161457 NA + 100.3 3 NA 3.8745574222 1.501220e+01 NA 0.1277046316 2.8607640137 NA + 100.4 1 0 4.6567608765 2.168542e+01 NA -0.0834577654 3.4383008132 NA + + $m4d$spM_id + center scale + (Intercept) NA NA + C1 0.7372814 0.01472882 + + $m4d$spM_lvlone + center scale + m1 NA NA + b2 NA NA + time 2.53394028 1.3818094 + I(time^2) 8.32444679 7.0900029 + b21 NA NA + c1 0.25599956 0.6718095 + C1:time 1.86876118 1.0180574 + b21:c1 0.04082297 0.2677776 + + $m4d$mu_reg_binom + [1] 0 + + $m4d$tau_reg_binom + [1] 1e-04 + + $m4d$mu_reg_multinomial + [1] 0 + + $m4d$tau_reg_multinomial + [1] 1e-04 + + $m4d$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m4d$shape_diag_RinvD + [1] "0.01" + + $m4d$rate_diag_RinvD + [1] "0.001" + + $m4d$RinvD_m1_id + [,1] [,2] + [1,] NA 0 + [2,] 0 NA + + $m4d$KinvD_m1_id + id + 3 + + + $m4e + $m4e$M_id + (Intercept) C1 + 1 1 0.7175865 + 2 1 0.7507170 + 3 1 0.7255954 + 4 1 0.7469352 + 5 1 0.7139120 + 6 1 0.7332505 + 7 1 0.7345929 + 8 1 0.7652589 + 9 1 0.7200622 + 10 1 0.7423879 + 11 1 0.7437448 + 12 1 0.7446470 + 13 1 0.7530186 + 14 1 0.7093137 + 15 1 0.7331192 + 16 1 0.7011390 + 17 1 0.7432395 + 18 1 0.7545191 + 19 1 0.7528487 + 20 1 0.7612865 + 21 1 0.7251719 + 22 1 0.7300630 + 23 1 0.7087249 + 24 1 0.7391938 + 25 1 0.7820641 + 26 1 0.7118298 + 27 1 0.7230857 + 28 1 0.7489353 + 29 1 0.7510888 + 30 1 0.7300717 + 31 1 0.7550721 + 32 1 0.7321898 + 33 1 0.7306414 + 34 1 0.7427216 + 35 1 0.7193042 + 36 1 0.7312888 + 37 1 0.7100436 + 38 1 0.7670184 + 39 1 0.7400449 + 40 1 0.7397304 + 41 1 0.7490966 + 42 1 0.7419274 + 43 1 0.7527810 + 44 1 0.7408315 + 45 1 0.7347550 + 46 1 0.7332398 + 47 1 0.7376481 + 48 1 0.7346179 + 49 1 0.7329402 + 50 1 0.7260436 + 51 1 0.7242910 + 52 1 0.7298067 + 53 1 0.7254741 + 54 1 0.7542067 + 55 1 0.7389952 + 56 1 0.7520638 + 57 1 0.7219958 + 58 1 0.7259632 + 59 1 0.7458606 + 60 1 0.7672421 + 61 1 0.7257179 + 62 1 0.7189892 + 63 1 0.7333356 + 64 1 0.7320243 + 65 1 0.7477711 + 66 1 0.7343974 + 67 1 0.7491624 + 68 1 0.7482736 + 69 1 0.7338267 + 70 1 0.7607742 + 71 1 0.7777600 + 72 1 0.7408143 + 73 1 0.7248271 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1.136655e-01 2 0.3371432109 + 4.1 3 0.06700602 1.143407e+00 4 1.0693019140 + 4.2 3 0.96122482 6.837688e+00 2 2.6148973033 + 4.3 2 1.14219951 9.819783e+00 6 3.1336532847 + 5 2 0.07348511 1.158319e+00 6 1.0762525082 + 5.1 2 0.58291628 3.208593e+00 2 1.7912546196 + 5.2 3 1.02819270 7.817661e+00 3 2.7960080339 + 5.3 2 1.03389386 7.907311e+00 2 2.8119940578 + 6 2 0.57748169 3.173907e+00 4 1.7815462884 + 7 1 1.19616503 1.093895e+01 2 3.3074087673 + 7.1 1 1.30855992 1.369622e+01 6 3.7008403614 + 7.2 1 1.56269618 2.276883e+01 4 4.7716691741 + 8 3 0.11746285 1.264815e+00 2 1.1246398522 + 8.1 3 0.58928609 3.249731e+00 2 1.8027009873 + 8.2 3 0.59750733 3.303606e+00 1 1.8175825174 + 8.3 1 1.04324992 8.056666e+00 2 2.8384267003 + 8.4 2 1.21284162 1.130995e+01 2 3.3630275307 + 8.5 3 1.48977222 1.967885e+01 4 4.4360849704 + 9 2 -0.04000943 9.230989e-01 3 0.9607803822 + 9.1 2 1.07082146 8.513413e+00 3 2.9177753383 + 9.2 3 1.57071564 2.313696e+01 2 4.8100892501 + 10 1 0.83184373 5.278740e+00 4 2.2975509102 + 10.1 3 1.42873389 1.741737e+01 5 4.1734118364 + 11 3 0.16827866 1.400119e+00 2 1.1832662905 + 11.1 1 0.21075122 1.524250e+00 4 1.2346051680 + 11.2 2 0.49684736 2.701196e+00 6 1.6435316263 + 11.3 1 1.21962028 1.146433e+01 2 3.3859017969 + 11.4 3 1.57107306 2.315350e+01 1 4.8118087661 + 12 2 -0.04165702 9.200622e-01 5 0.9591987054 + 13 1 -2.78209660 3.832672e-03 2 0.0619085738 + 13.1 3 1.27035199 1.268860e+01 6 3.5621061502 + 14 1 1.39536386 1.629287e+01 3 4.0364430007 + 14.1 3 1.49762465 1.999034e+01 2 4.4710561272 + 14.2 3 1.53383464 2.149175e+01 4 4.6359198843 + 14.3 2 1.54513729 2.198311e+01 2 4.6886152599 + 15 2 -0.61580408 2.918229e-01 4 0.5402063532 + 15.1 2 0.17338010 1.414477e+00 7 1.1893180816 + 15.2 3 0.41176123 2.278512e+00 4 1.5094739688 + 15.3 3 1.59317589 2.419998e+01 3 4.9193474615 + 16 1 0.21655500 1.542046e+00 3 1.2417913869 + 16.1 2 0.94296095 6.592429e+00 2 2.5675726333 + 16.2 1 0.97546872 7.035280e+00 5 2.6524101500 + 16.3 3 1.26933963 1.266294e+01 3 3.5585018690 + 16.4 2 1.32475013 1.414697e+01 2 3.7612454291 + 16.5 3 1.38257779 1.588151e+01 6 3.9851612889 + 17 1 0.46532748 2.536170e+00 3 1.5925356350 + 17.1 2 0.89093325 5.940935e+00 1 2.4374032998 + 17.2 1 1.10712558 9.154551e+00 4 3.0256489082 + 17.3 2 1.20384548 1.110828e+01 5 3.3329089405 + 17.4 2 1.35309323 1.497207e+01 5 3.8693758985 + 18 1 0.89094389 5.941061e+00 8 2.4374292302 + 19 2 -0.02304702 9.549522e-01 5 0.9772165376 + 19.1 1 0.13683034 1.314769e+00 6 1.1466335913 + 19.2 2 0.81532616 5.107205e+00 4 2.2599126538 + 19.3 3 1.43780097 1.773610e+01 3 4.2114245973 + 20 2 0.54058790 2.948144e+00 5 1.7170160066 + 20.1 3 0.56320376 3.084555e+00 8 1.7562902288 + 20.2 2 0.81162182 5.069507e+00 3 2.2515566566 + 20.3 2 0.81576844 5.111725e+00 3 2.2609123867 + 20.4 1 1.25028462 1.218943e+01 3 3.4913365287 + 20.5 2 1.42865863 1.741475e+01 3 4.1730977828 + 21 3 0.52689085 2.868478e+00 3 1.6936582839 + 21.1 2 1.08421553 8.744554e+00 3 2.9571191233 + 21.2 2 1.33203313 1.435454e+01 4 3.7887385779 + 22 2 0.90406535 6.099036e+00 6 2.4696226232 + 22.1 1 1.15141431 1.000244e+01 3 3.1626627257 + 23 1 0.43272573 2.376079e+00 3 1.5414533857 + 23.1 1 0.84885676 5.461446e+00 2 2.3369736120 + 24 3 1.03968065 7.999358e+00 1 2.8283136466 + 25 3 -0.61958002 2.896274e-01 2 0.5381704110 + 25.1 3 0.47435262 2.582364e+00 0 1.6069735331 + 25.2 1 0.49214585 2.675916e+00 6 1.6358226922 + 25.3 1 1.18316390 1.065818e+01 6 3.2646870392 + 25.4 2 1.40566126 1.663190e+01 2 4.0782226040 + 25.5 2 1.42456012 1.727258e+01 2 4.1560292873 + 26 1 -1.42183601 5.821152e-02 6 0.2412706357 + 26.1 1 0.89411619 5.978875e+00 0 2.4451737676 + 26.2 2 1.28062152 1.295191e+01 1 3.5988757887 + 26.3 1 1.43084610 1.749110e+01 4 4.1822362854 + 27 1 1.30713818 1.365733e+01 2 3.6955824879 + 27.1 2 1.44577562 1.802124e+01 4 4.2451434687 + 28 3 -0.55399075 3.302248e-01 5 0.5746519344 + 28.1 2 1.02761614 7.808651e+00 0 2.7943964268 + 28.2 3 1.43766547 1.773129e+01 7 4.2108539480 + 28.3 2 1.49751193 1.998584e+01 3 4.4705521734 + 29 1 0.17385954 1.415834e+00 4 1.1898884235 + 29.1 3 0.56667988 3.106075e+00 1 1.7624059319 + 29.2 1 0.70361255 4.084605e+00 4 2.0210406382 + 29.3 3 1.22608972 1.161363e+01 3 3.4078777023 + 30 2 0.81692848 5.123598e+00 5 2.2635366488 + 30.1 2 1.27921945 1.291564e+01 5 3.5938334477 + 30.2 1 1.28477952 1.306006e+01 6 3.6138710892 + 31 3 1.48133498 1.934957e+01 1 4.3988140998 + 32 3 0.51552709 2.804020e+00 2 1.6745209007 + 32.1 1 1.06912058 8.484502e+00 5 2.9128167813 + 32.2 1 1.08777236 8.806981e+00 5 2.9676558380 + 32.3 3 1.43745941 1.772399e+01 6 4.2099863547 + 33 3 -4.67360146 8.720901e-05 4 0.0093385763 + 33.1 2 1.24101546 1.196554e+01 7 3.4591242753 + 34 3 0.40538339 2.249632e+00 2 1.4998774312 + 34.1 2 1.34136920 1.462509e+01 5 3.8242761395 + 34.2 1 1.36282745 1.526641e+01 6 3.9072251692 + 34.3 2 1.37579253 1.566745e+01 2 3.9582124643 + 35 3 0.28475022 1.767384e+00 3 1.3294299203 + 35.1 1 0.42376113 2.333857e+00 2 1.5276966314 + 35.2 2 1.50465325 2.027334e+01 3 4.5025920868 + 36 1 -0.33923248 5.073953e-01 3 0.7123168337 + 36.1 2 0.58625734 3.230105e+00 1 1.7972493160 + 36.2 3 0.60227552 3.335261e+00 6 1.8262697803 + 36.3 2 1.45488994 1.835276e+01 4 4.2840119381 + 36.4 3 1.53027488 2.133929e+01 1 4.6194464504 + 37 1 0.69408336 4.007496e+00 4 2.0018732361 + 37.1 2 1.29901486 1.343724e+01 6 3.6656836793 + 37.2 3 1.37785731 1.573228e+01 8 3.9663937816 + 38 1 -0.01750115 9.656032e-01 3 0.9826511063 + 39 3 -0.36790804 4.791143e-01 2 0.6921808305 + 39.1 3 -0.10227727 8.150103e-01 3 0.9027792048 + 39.2 3 0.26663623 1.704501e+00 6 1.3055654289 + 39.3 3 0.43261602 2.375557e+00 4 1.5412842878 + 39.4 2 1.15798114 1.013467e+01 3 3.1834997435 + 39.5 2 1.42055487 1.713477e+01 6 4.1394166439 + 40 2 0.12490390 1.283779e+00 1 1.1330395646 + 40.1 3 0.99106398 7.258172e+00 3 2.6940994046 + 40.2 2 1.11174613 9.239542e+00 0 3.0396614212 + 40.3 2 1.54250672 2.186776e+01 4 4.6762977762 + 41 1 0.65944345 3.739257e+00 1 1.9337158254 + 41.1 2 1.16178439 1.021205e+01 4 3.1956304458 + 41.2 2 1.18927300 1.078920e+01 7 3.2846923557 + 41.3 2 1.21827591 1.143355e+01 5 3.3813529415 + 41.4 1 1.26646761 1.259041e+01 2 3.5482964432 + 42 3 -0.72170038 2.361234e-01 1 0.4859252973 + 42.1 2 1.46540897 1.874295e+01 3 4.3293134298 + 43 3 -0.57685600 3.154636e-01 5 0.5616614548 + 43.1 3 0.07172323 1.154245e+00 2 1.0743579536 + 43.2 1 0.96056779 6.828709e+00 3 2.6131797966 + 44 2 -0.26622789 5.871613e-01 3 0.7662644819 + 44.1 2 0.97419323 7.017356e+00 3 2.6490291790 + 44.2 3 1.20512946 1.113684e+01 3 3.3371910988 + 44.3 1 1.41474092 1.693668e+01 4 4.1154200875 + 45 1 -1.63094249 3.831610e-02 4 0.1957449992 + 45.1 1 0.69133712 3.985546e+00 2 1.9963831536 + 46 2 0.29845548 1.816499e+00 8 1.3477755385 + 46.1 1 1.04962489 8.160046e+00 5 2.8565793915 + 46.2 3 1.48525084 1.950170e+01 5 4.4160729996 + 47 1 -0.50872427 3.615162e-01 3 0.6012621359 + 47.1 1 0.87950730 5.806713e+00 5 2.4097121472 + 47.2 2 1.09780510 8.985482e+00 5 2.9975794035 + 47.3 3 1.15781314 1.013127e+01 2 3.1829649757 + 47.4 2 1.53041755 2.134538e+01 5 4.6201055450 + 48 3 1.05107914 8.183814e+00 2 2.8607365978 + 48.1 3 1.06809653 8.467142e+00 5 2.9098354396 + 49 1 0.99988735 7.387392e+00 4 2.7179756400 + 50 2 0.16229406 1.383461e+00 1 1.1762060679 + 51 2 0.35798466 2.046169e+00 9 1.4304436720 + 52 3 0.75455484 4.522702e+00 3 2.1266646020 + 52.1 3 1.13141972 9.610339e+00 3 3.1000545993 + 52.2 2 1.14002538 9.777177e+00 4 3.1268477370 + 52.3 2 1.27288653 1.275308e+01 11 3.5711459327 + 52.4 1 1.56827544 2.302432e+01 3 4.7983659909 + 52.5 2 1.60579658 2.481859e+01 3 4.9818264414 + 53 3 -0.70001084 2.465916e-01 5 0.4965799209 + 53.1 2 1.26709851 1.260630e+01 3 3.5505357443 + 53.2 1 1.52148981 2.096763e+01 2 4.5790420019 + 54 1 0.33894951 1.969735e+00 1 1.4034724841 + 54.1 3 0.63192994 3.539056e+00 4 1.8812377600 + 54.2 1 0.92058507 6.303910e+00 2 2.5107589352 + 54.3 3 1.02419066 7.755338e+00 2 2.7848406672 + 54.4 1 1.38988484 1.611531e+01 6 4.0143877396 + 55 3 -0.49126437 3.743632e-01 1 0.6118522980 + 55.1 3 -0.29252747 5.570753e-01 2 0.7463747414 + 55.2 1 1.03677973 7.953081e+00 2 2.8201208171 + 55.3 3 1.14187711 9.813453e+00 3 3.1326431572 + 55.4 1 1.16994340 1.038006e+01 5 3.2218102901 + 56 3 0.20141578 1.496055e+00 5 1.2231332215 + 56.1 3 0.85752547 5.556959e+00 5 2.3573202139 + 56.2 2 0.94293018 6.592024e+00 2 2.5674936292 + 56.3 3 1.08204800 8.706727e+00 3 2.9507164378 + 56.4 1 1.17163752 1.041529e+01 6 3.2272730360 + 56.5 2 1.22892457 1.167966e+01 1 3.4175522043 + 57 2 -1.43955530 5.618471e-02 3 0.2370331448 + 57.1 1 -1.39374403 6.157569e-02 6 0.2481445030 + 57.2 3 0.13151815 1.300874e+00 3 1.1405586067 + 57.3 2 0.74923856 4.474869e+00 2 2.1153886721 + 58 2 0.19967837 1.490865e+00 6 1.2210099772 + 58.1 3 0.49067877 2.668076e+00 5 1.6334245703 + 58.2 1 0.51830932 2.819667e+00 2 1.6791862890 + 58.3 1 0.96774864 6.927488e+00 4 2.6320121693 + 58.4 1 1.04653734 8.109812e+00 4 2.8477731440 + 58.5 1 1.27300163 1.275602e+01 4 3.5715569824 + 59 1 0.64311617 3.619125e+00 6 1.9023998594 + 59.1 1 1.60415640 2.473731e+01 4 4.9736620474 + 60 3 1.05968098 8.325824e+00 7 2.8854503250 + 61 1 -0.32661269 5.203647e-01 6 0.7213630795 + 61.1 2 0.84100443 5.376345e+00 3 2.3186947661 + 61.2 1 0.91937849 6.288716e+00 2 2.5077313243 + 61.3 1 1.15471134 1.006861e+01 5 3.1731073430 + 61.4 2 1.28156493 1.297637e+01 4 3.6022726283 + 62 2 -0.62796412 2.848114e-01 1 0.5336771999 + 62.1 3 -0.35843842 4.882748e-01 1 0.6987666548 + 62.2 3 1.24081502 1.196074e+01 2 3.4584309917 + 62.3 1 1.56921516 2.306763e+01 4 4.8028772371 + 63 1 1.03309021 7.894611e+00 6 2.8097350930 + 63.1 2 1.37760053 1.572420e+01 2 3.9653754211 + 64 3 1.41564212 1.696724e+01 2 4.1191305732 + 65 3 -0.34585475 5.007194e-01 3 0.7076152589 + 65.1 3 0.70568063 4.101535e+00 4 2.0252246363 + 65.2 2 1.13550282 9.689140e+00 2 3.1127382827 + 65.3 2 1.16218434 1.022023e+01 2 3.1969087943 + 66 2 1.25114607 1.221045e+01 6 3.4943454154 + 66.1 3 1.32647633 1.419589e+01 0 3.7677437009 + 66.2 2 1.37336460 1.559155e+01 5 3.9486138616 + 67 3 1.42859659 1.741258e+01 8 4.1728388879 + 68 3 -2.04645568 1.669057e-02 5 0.1291919907 + 68.1 1 0.57715501 3.171834e+00 5 1.7809643946 + 68.2 3 0.71750831 4.199715e+00 4 2.0493205660 + 68.3 3 1.07864325 8.647640e+00 3 2.9406870750 + 68.4 1 1.39640979 1.632699e+01 1 4.0406670363 + 69 2 1.42193171 1.718202e+01 5 4.1451198701 + 70 2 -1.61316627 3.970284e-02 6 0.1992557163 + 70.1 3 -0.72778533 2.332672e-01 2 0.4829774413 + 71 1 -0.25597601 5.993245e-01 4 0.7741605386 + 71.1 3 0.39768944 2.215280e+00 2 1.4883817220 + 71.2 1 1.40507996 1.661257e+01 5 4.0758526395 + 71.3 2 1.54858834 2.213537e+01 10 4.7048238723 + 71.4 1 1.55271500 2.231881e+01 2 4.7242791823 + 72 1 -0.07029413 8.688470e-01 2 0.9321196121 + 72.1 2 0.16551374 1.392398e+00 4 1.1799991806 + 72.2 3 0.63750589 3.578744e+00 8 1.8917567329 + 72.3 3 1.24857116 1.214773e+01 6 3.4853593935 + 72.4 1 1.30519980 1.360449e+01 4 3.6884259700 + 72.5 2 1.40742346 1.669062e+01 1 4.0854155901 + 73 1 1.52648860 2.117830e+01 1 4.6019889915 + 74 2 0.38027083 2.139435e+00 1 1.4626806753 + 75 1 1.17940201 1.057829e+01 6 3.2524286874 + 76 3 0.59193402 3.266987e+00 3 1.8074807397 + 76.1 1 1.45126419 1.822015e+01 4 4.2685073183 + 76.2 1 1.60319315 2.468970e+01 5 4.9688734859 + 77 1 -0.16735013 7.155525e-01 1 0.8459033852 + 78 1 -0.19466612 6.775091e-01 2 0.8231094317 + 79 2 -2.84074848 3.408452e-03 2 0.0583819521 + 79.1 2 0.89225918 5.956710e+00 6 2.4406372628 + 79.2 3 1.19278625 1.086528e+01 5 3.2962526032 + 80 3 -0.10702187 8.073131e-01 5 0.8985060186 + 80.1 2 0.29525363 1.804904e+00 1 1.3434670598 + 80.2 3 1.03054400 7.854511e+00 4 2.8025900386 + 81 2 -4.59200757 1.026675e-04 4 0.0101324962 + 81.1 3 -0.05956855 8.876861e-01 5 0.9421709494 + 81.2 2 1.11653255 9.328415e+00 2 3.0542453879 + 81.3 1 1.20766489 1.119346e+01 5 3.3456630446 + 82 3 0.32143184 1.901920e+00 1 1.3791010005 + 82.1 2 0.56537123 3.097956e+00 2 1.7601010622 + 82.2 1 0.96443810 6.881772e+00 5 2.6233131927 + 83 1 -2.92360830 2.887926e-03 5 0.0537394290 + 83.1 3 1.06683161 8.445749e+00 1 2.9061570496 + 83.2 2 1.13749504 9.727823e+00 1 3.1189457362 + 83.3 2 1.56158380 2.271823e+01 4 4.7663642222 + 84 2 1.00261742 7.427838e+00 1 2.7254060237 + 84.1 1 1.20491590 1.113209e+01 5 3.3364784659 + 85 1 -1.21141501 8.867032e-02 6 0.2977756259 + 85.1 1 0.55354693 3.025553e+00 5 1.7394116637 + 85.2 2 0.98754404 7.207254e+00 3 2.6846330194 + 85.3 2 1.15084929 9.991139e+00 2 3.1608762743 + 85.4 3 1.37250101 1.556465e+01 2 3.9452053758 + 85.5 3 1.50613203 2.033338e+01 6 4.5092553482 + 86 1 -0.16531361 7.184729e-01 3 0.8476278360 + 86.1 2 0.01179313 1.023867e+00 3 1.0118629411 + 86.2 3 0.22403591 1.565291e+00 6 1.2511159515 + 86.3 3 0.78255612 4.783212e+00 5 2.1870554925 + 86.4 1 0.89743141 6.018649e+00 5 2.4532935000 + 86.5 3 1.34040901 1.459703e+01 4 3.8206058508 + 87 2 0.99582370 7.327595e+00 3 2.7069531474 + 87.1 3 1.23728720 1.187665e+01 6 3.4462517721 + 87.2 2 1.50943340 2.046808e+01 2 4.5241666853 + 88 1 -7.43666969 3.472088e-07 1 0.0005892443 + 88.1 3 -0.34022529 5.063888e-01 6 0.7116099866 + 88.2 3 0.91439786 6.226384e+00 1 2.4952722900 + 88.3 1 1.19379568 1.088724e+01 6 3.2995816297 + 89 1 -0.43663289 4.175856e-01 7 0.6462086167 + 90 3 -1.77429443 2.876520e-02 3 0.1696030737 + 90.1 2 0.95475675 6.749804e+00 8 2.5980385230 + 90.2 2 0.98025630 7.102967e+00 4 2.6651392167 + 90.3 2 1.13920034 9.761057e+00 2 3.1242690247 + 91 1 -0.44900667 4.073782e-01 4 0.6382618390 + 91.1 3 0.96409219 6.877013e+00 2 2.6224059286 + 91.2 2 1.56386564 2.282214e+01 5 4.7772527603 + 92 2 -2.60768143 5.432462e-03 3 0.0737052364 + 93 2 -1.27693454 7.778015e-02 3 0.2788909199 + 93.1 1 0.03515090 1.072832e+00 3 1.0357759963 + 93.2 1 0.91294719 6.208345e+00 4 2.4916551099 + 93.3 3 1.06043020 8.338309e+00 2 2.8876129608 + 93.4 2 1.49603344 1.992683e+01 6 4.4639474002 + 94 1 -0.16392661 7.204688e-01 2 0.8488043118 + 94.1 1 0.05377339 1.113543e+00 4 1.0552454425 + 94.2 2 0.66503063 3.781275e+00 2 1.9445500884 + 94.3 2 1.12202677 9.431485e+00 6 3.0710722448 + 94.4 2 1.12728824 9.531256e+00 5 3.0872731935 + 94.5 1 1.47718020 1.918945e+01 5 4.3805759016 + 95 1 0.70305113 4.080021e+00 8 2.0199063048 + 95.1 2 1.39089487 1.614790e+01 4 4.0184444457 + 95.2 2 1.51724656 2.079044e+01 1 4.5596531732 + 96 2 -3.46947576 9.692853e-04 2 0.0311333477 + 96.1 3 -2.02172545 1.753685e-02 3 0.1324267720 + 96.2 1 -0.40028304 4.490747e-01 2 0.6701303425 + 96.3 3 0.77817916 4.741523e+00 6 2.1775037691 + 96.4 2 0.79958353 4.948909e+00 6 2.2246142488 + 96.5 2 1.44403602 1.795865e+01 3 4.2377650598 + 97 1 0.17857310 1.429245e+00 2 1.1955102731 + 97.1 3 1.60146841 2.460468e+01 5 4.9603108643 + 98 3 -1.58878641 4.168671e-02 7 0.2041732438 + 98.1 1 -0.84174488 1.857247e-01 2 0.4309578973 + 98.2 3 1.25768262 1.237113e+01 6 3.5172611906 + 99 1 -1.04078137 1.247351e-01 3 0.3531786101 + 99.1 1 1.54307253 2.189252e+01 4 4.6789444226 + 99.2 2 1.60797853 2.492714e+01 5 4.9927084171 + 100 3 0.06685343 1.143058e+00 2 1.0691387602 + 100.1 2 0.41272829 2.282923e+00 3 1.5109344281 + 100.2 2 0.76557633 4.623503e+00 3 2.1502332564 + 100.3 3 1.35443144 1.501220e+01 7 3.8745574222 + 100.4 1 1.53832012 2.168542e+01 6 4.6567608765 + + $m4e$spM_id + center scale + (Intercept) NA NA + C1 0.7372814 0.01472882 + + $m4e$spM_lvlone + center scale + m1 NA NA + log(time) 0.6318779 1.063214 + I(time^2) 8.3244468 7.090003 + p1 3.7264438 1.946996 + time 2.5339403 1.381809 + + $m4e$mu_reg_multinomial + [1] 0 + + $m4e$tau_reg_multinomial + [1] 1e-04 + + $m4e$group_id + [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 + [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 + [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 + [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 + [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 + [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 + [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 + [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 + [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 + [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 + [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 + [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 + [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 + [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 + [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 + [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 + [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 + [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 + [325] 100 100 100 100 100 + + $m4e$shape_diag_RinvD + [1] "0.01" + + $m4e$rate_diag_RinvD + [1] "0.001" + + + +# jagsmodel remains the same + + Code + lapply(models, "[[", "jagsmodel") + Output + $m0a + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + } + $m0b + model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + } + $m1a + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 1] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + } + $m1b + model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 1] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + } + $m1c + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + } + $m1d + model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + } + $m2a + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 2] + + beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2b + model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 2] + + beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } + $m2c + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m2d + model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } + $m3a + model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 2] + + beta[3] * M_lvlone[i, 3] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + } + $m3b + model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 3] + + beta[3] * M_lvlone[i, 4] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + alpha[1] * M_id[group_id[i], 1] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + alpha[2] * M_id[group_id[i], 1] + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:2) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + } + $m4a + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 3] + + beta[2] * M_id[group_id[i], 4] + + beta[3] * M_id[group_id[i], 5] + + beta[4] * M_id[group_id[i], 6] + + beta[5] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] + + beta[6] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + + beta[13] * M_lvlone[i, 3] + beta[14] * M_lvlone[i, 4] + + beta[15] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[16] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[7] * M_id[group_id[i], 3] + + beta[8] * M_id[group_id[i], 4] + + beta[9] * M_id[group_id[i], 5] + + beta[10] * M_id[group_id[i], 6] + + beta[11] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] + + beta[12] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + + beta[17] * M_lvlone[i, 3] + beta[18] * M_lvlone[i, 4] + + beta[19] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[20] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:20) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + alpha[1] * M_id[group_id[i], 3] + + alpha[2] * M_id[group_id[i], 4] + + alpha[3] * M_id[group_id[i], 5] + + alpha[4] * M_id[group_id[i], 6] + + alpha[5] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] + + alpha[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + alpha[7] * M_id[group_id[i], 3] + + alpha[8] * M_id[group_id[i], 4] + + alpha[9] * M_id[group_id[i], 5] + + alpha[10] * M_id[group_id[i], 6] + + alpha[11] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] + + alpha[12] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:12) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 3] * alpha[13] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[14] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[15] + log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[16] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[17] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[18] + log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[19] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[20] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[21] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 13:21) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 3] * alpha[22] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[23] + + M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2]) + + + } + + # Priors for the model for C2 + for (k in 22:23) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_lvlone[i, 3] * M_id[group_id[i], 7] + M_lvlone[i, 6] <- M_lvlone[i, 4] * M_id[group_id[i], 7] + } + + } + $m4b + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[4] * M_id[group_id[i], 2] + + beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[6] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + alpha[1] * M_id[group_id[i], 2] + + alpha[2] * M_id[group_id[i], 5] + + alpha[3] * M_id[group_id[i], 6] + + alpha[4] * M_id[group_id[i], 7] + + alpha[5] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + + alpha[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + alpha[7] * M_id[group_id[i], 2] + + alpha[8] * M_id[group_id[i], 5] + + alpha[9] * M_id[group_id[i], 6] + + alpha[10] * M_id[group_id[i], 7] + + alpha[11] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + + alpha[12] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + + + + M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0) + + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:12) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[13] + M_id[ii, 5] * alpha[14] + + M_id[ii, 6] * alpha[15] + M_id[ii, 7] * alpha[16] + + (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[17] + + M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1]) + + + } + + # Priors for the model for C2 + for (k in 13:17) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3] + } + + } + $m4c + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[3] * M_id[group_id[i], 4] + + beta[7] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[4] * M_id[group_id[i], 2] + + beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[6] * M_id[group_id[i], 4] + + beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[10] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:4] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + mu_b_m1_id[ii, 2] <- 0 + mu_b_m1_id[ii, 3] <- 0 + mu_b_m1_id[ii, 4] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + for (k in 1:4) { + RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_m1_id[1:4, 1:4] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id) + D_m1_id[1:4, 1:4] <- inverse(invD_m1_id[ , ]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for time + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + M_id[ii, 4] * alpha[7] + } + + # Priors for the model for c1 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } + $m4d + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[1] * M_id[group_id[i], 1] + + beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[7] * M_lvlone[i, 5] + + beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + + beta[10] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[3] * M_id[group_id[i], 1] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[12] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[13] * M_lvlone[i, 5] + + beta[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[15] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + + beta[16] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:2] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + mu_b_m1_id[ii, 2] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:16) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + for (k in 1:2) { + RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_m1_id[1:2, 1:2] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id) + D_m1_id[1:2, 1:2] <- inverse(invD_m1_id[ , ]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + } + + # Priors for the model for b2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] + } + + } + $m4e + model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[5] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[6] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 1] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial_ridge_beta[k]) + tau_reg_multinomial_ridge_beta[k] ~ dgamma(0.01, 0.01) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + } + +# GRcrit and MCerror give same result + + Code + lapply(models0, GR_crit, multivariate = FALSE) + Output + $m0a + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1C: (Intercept) NaN NaN + D_m1_id[1,1] NaN NaN + + + $m0b + Potential scale reduction factors: + + Point est. Upper C.I. + m2B: (Intercept) NaN NaN + m2C: (Intercept) NaN NaN + D_m2_id[1,1] NaN NaN + + + $m1a + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1B: C1 NaN NaN + m1C: (Intercept) NaN NaN + m1C: C1 NaN NaN + D_m1_id[1,1] NaN NaN + + + $m1b + Potential scale reduction factors: + + Point est. Upper C.I. + m2B: (Intercept) NaN NaN + m2B: C1 NaN NaN + m2C: (Intercept) NaN NaN + m2C: C1 NaN NaN + D_m2_id[1,1] NaN NaN + + + $m1c + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1C: (Intercept) NaN NaN + m1B: c1 NaN NaN + m1C: c1 NaN NaN + D_m1_id[1,1] NaN NaN + + + $m1d + Potential scale reduction factors: + + Point est. Upper C.I. + m2B: (Intercept) NaN NaN + m2C: (Intercept) NaN NaN + m2B: c1 NaN NaN + m2C: c1 NaN NaN + D_m2_id[1,1] NaN NaN + + + $m2a + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1B: C2 NaN NaN + m1C: (Intercept) NaN NaN + m1C: C2 NaN NaN + D_m1_id[1,1] NaN NaN + + + $m2b + Potential scale reduction factors: + + Point est. Upper C.I. + m2B: (Intercept) NaN NaN + m2B: C2 NaN NaN + m2C: (Intercept) NaN NaN + m2C: C2 NaN NaN + D_m2_id[1,1] NaN NaN + + + $m2c + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1C: (Intercept) NaN NaN + m1B: c2 NaN NaN + m1C: c2 NaN NaN + D_m1_id[1,1] NaN NaN + + + $m2d + Potential scale reduction factors: + + Point est. Upper C.I. + m2B: (Intercept) NaN NaN + m2C: (Intercept) NaN NaN + m2B: c2 NaN NaN + m2C: c2 NaN NaN + D_m2_id[1,1] NaN NaN + + + $m3a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + m1B NaN NaN + m1C NaN NaN + sigma_c1 NaN NaN + D_c1_id[1,1] NaN NaN + + + $m3b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + m2B NaN NaN + m2C NaN NaN + sigma_c1 NaN NaN + D_c1_id[1,1] NaN NaN + + + $m4a + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1B: M22 NaN NaN + m1B: M23 NaN NaN + m1B: M24 NaN NaN + m1B: abs(C1 - C2) NaN NaN + m1B: log(C1) NaN NaN + m1C: (Intercept) NaN NaN + m1C: M22 NaN NaN + m1C: M23 NaN NaN + m1C: M24 NaN NaN + m1C: abs(C1 - C2) NaN NaN + m1C: log(C1) NaN NaN + m1B: m2B NaN NaN + m1B: m2C NaN NaN + m1B: m2B:abs(C1 - C2) NaN NaN + m1B: m2C:abs(C1 - C2) NaN NaN + m1C: m2B NaN NaN + m1C: m2C NaN NaN + m1C: m2B:abs(C1 - C2) NaN NaN + m1C: m2C:abs(C1 - C2) NaN NaN + D_m1_id[1,1] NaN NaN + + + $m4b + Potential scale reduction factors: + + Point est. + m1B: (Intercept) NaN + m1B: abs(C1 - C2) NaN + m1B: log(C1) NaN + m1C: (Intercept) NaN + m1C: abs(C1 - C2) NaN + m1C: log(C1) NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + D_m1_id[1,1] NaN + Upper C.I. + m1B: (Intercept) NaN + m1B: abs(C1 - C2) NaN + m1B: log(C1) NaN + m1C: (Intercept) NaN + m1C: abs(C1 - C2) NaN + m1C: log(C1) NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + D_m1_id[1,1] NaN + + + $m4c + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1B: C1 NaN NaN + m1B: B21 NaN NaN + m1C: (Intercept) NaN NaN + m1C: C1 NaN NaN + m1C: B21 NaN NaN + m1B: time NaN NaN + m1B: c1 NaN NaN + m1C: time NaN NaN + m1C: c1 NaN NaN + D_m1_id[1,1] NaN NaN + D_m1_id[1,2] NaN NaN + D_m1_id[2,2] NaN NaN + D_m1_id[1,3] NaN NaN + D_m1_id[2,3] NaN NaN + D_m1_id[3,3] NaN NaN + D_m1_id[1,4] NaN NaN + D_m1_id[2,4] NaN NaN + D_m1_id[3,4] NaN NaN + D_m1_id[4,4] NaN NaN + + + $m4d + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1B: C1 NaN NaN + m1C: (Intercept) NaN NaN + m1C: C1 NaN NaN + m1B: time NaN NaN + m1B: I(time^2) NaN NaN + m1B: b21 NaN NaN + m1B: c1 NaN NaN + m1B: C1:time NaN NaN + m1B: b21:c1 NaN NaN + m1C: time NaN NaN + m1C: I(time^2) NaN NaN + m1C: b21 NaN NaN + m1C: c1 NaN NaN + m1C: C1:time NaN NaN + m1C: b21:c1 NaN NaN + D_m1_id[1,1] NaN NaN + D_m1_id[1,2] NaN NaN + D_m1_id[2,2] NaN NaN + + + $m4e + Potential scale reduction factors: + + Point est. Upper C.I. + m1B: (Intercept) NaN NaN + m1B: C1 NaN NaN + m1C: (Intercept) NaN NaN + m1C: C1 NaN NaN + m1B: log(time) NaN NaN + m1B: I(time^2) NaN NaN + m1B: p1 NaN NaN + m1C: log(time) NaN NaN + m1C: I(time^2) NaN NaN + m1C: p1 NaN NaN + D_m1_id[1,1] NaN NaN + + + +--- + + Code + lapply(models0, MC_error) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + + $m0b + est MCSE SD MCSE/SD + m2B: (Intercept) 0 0 0 NaN + m2C: (Intercept) 0 0 0 NaN + D_m2_id[1,1] 0 0 0 NaN + + $m1a + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1B: C1 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + m1C: C1 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + + $m1b + est MCSE SD MCSE/SD + m2B: (Intercept) 0 0 0 NaN + m2B: C1 0 0 0 NaN + m2C: (Intercept) 0 0 0 NaN + m2C: C1 0 0 0 NaN + D_m2_id[1,1] 0 0 0 NaN + + $m1c + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + m1B: c1 0 0 0 NaN + m1C: c1 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + + $m1d + est MCSE SD MCSE/SD + m2B: (Intercept) 0 0 0 NaN + m2C: (Intercept) 0 0 0 NaN + m2B: c1 0 0 0 NaN + m2C: c1 0 0 0 NaN + D_m2_id[1,1] 0 0 0 NaN + + $m2a + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1B: C2 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + m1C: C2 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + + $m2b + est MCSE SD MCSE/SD + m2B: (Intercept) 0 0 0 NaN + m2B: C2 0 0 0 NaN + m2C: (Intercept) 0 0 0 NaN + m2C: C2 0 0 0 NaN + D_m2_id[1,1] 0 0 0 NaN + + $m2c + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + m1B: c2 0 0 0 NaN + m1C: c2 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + + $m2d + est MCSE SD MCSE/SD + m2B: (Intercept) 0 0 0 NaN + m2C: (Intercept) 0 0 0 NaN + m2B: c2 0 0 0 NaN + m2C: c2 0 0 0 NaN + D_m2_id[1,1] 0 0 0 NaN + + $m3a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + m1B 0 0 0 NaN + m1C 0 0 0 NaN + sigma_c1 0 0 0 NaN + D_c1_id[1,1] 0 0 0 NaN + + $m3b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + m2B 0 0 0 NaN + m2C 0 0 0 NaN + sigma_c1 0 0 0 NaN + D_c1_id[1,1] 0 0 0 NaN + + $m4a + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1B: M22 0 0 0 NaN + m1B: M23 0 0 0 NaN + m1B: M24 0 0 0 NaN + m1B: abs(C1 - C2) 0 0 0 NaN + m1B: log(C1) 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + m1C: M22 0 0 0 NaN + m1C: M23 0 0 0 NaN + m1C: M24 0 0 0 NaN + m1C: abs(C1 - C2) 0 0 0 NaN + m1C: log(C1) 0 0 0 NaN + m1B: m2B 0 0 0 NaN + m1B: m2C 0 0 0 NaN + m1B: m2B:abs(C1 - C2) 0 0 0 NaN + m1B: m2C:abs(C1 - C2) 0 0 0 NaN + m1C: m2B 0 0 0 NaN + m1C: m2C 0 0 0 NaN + m1C: m2B:abs(C1 - C2) 0 0 0 NaN + m1C: m2C:abs(C1 - C2) 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + + $m4b + est MCSE SD + m1B: (Intercept) 0 0 0 + m1B: abs(C1 - C2) 0 0 0 + m1B: log(C1) 0 0 0 + m1C: (Intercept) 0 0 0 + m1C: abs(C1 - C2) 0 0 0 + m1C: log(C1) 0 0 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 + D_m1_id[1,1] 0 0 0 + MCSE/SD + m1B: (Intercept) NaN + m1B: abs(C1 - C2) NaN + m1B: log(C1) NaN + m1C: (Intercept) NaN + m1C: abs(C1 - C2) NaN + m1C: log(C1) NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN + D_m1_id[1,1] NaN + + $m4c + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1B: C1 0 0 0 NaN + m1B: B21 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + m1C: C1 0 0 0 NaN + m1C: B21 0 0 0 NaN + m1B: time 0 0 0 NaN + m1B: c1 0 0 0 NaN + m1C: time 0 0 0 NaN + m1C: c1 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + D_m1_id[1,2] 0 0 0 NaN + D_m1_id[2,2] 0 0 0 NaN + D_m1_id[1,3] 0 0 0 NaN + D_m1_id[2,3] 0 0 0 NaN + D_m1_id[3,3] 0 0 0 NaN + D_m1_id[1,4] 0 0 0 NaN + D_m1_id[2,4] 0 0 0 NaN + D_m1_id[3,4] 0 0 0 NaN + D_m1_id[4,4] 0 0 0 NaN + + $m4d + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1B: C1 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + m1C: C1 0 0 0 NaN + m1B: time 0 0 0 NaN + m1B: I(time^2) 0 0 0 NaN + m1B: b21 0 0 0 NaN + m1B: c1 0 0 0 NaN + m1B: C1:time 0 0 0 NaN + m1B: b21:c1 0 0 0 NaN + m1C: time 0 0 0 NaN + m1C: I(time^2) 0 0 0 NaN + m1C: b21 0 0 0 NaN + m1C: c1 0 0 0 NaN + m1C: C1:time 0 0 0 NaN + m1C: b21:c1 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + D_m1_id[1,2] 0 0 0 NaN + D_m1_id[2,2] 0 0 0 NaN + + $m4e + est MCSE SD MCSE/SD + m1B: (Intercept) 0 0 0 NaN + m1B: C1 0 0 0 NaN + m1C: (Intercept) 0 0 0 NaN + m1C: C1 0 0 0 NaN + m1B: log(time) 0 0 0 NaN + m1B: I(time^2) 0 0 0 NaN + m1B: p1 0 0 0 NaN + m1C: log(time) 0 0 0 NaN + m1C: I(time^2) 0 0 0 NaN + m1C: p1 0 0 0 NaN + D_m1_id[1,1] 0 0 0 NaN + + +# summary output remained the same + + Code + lapply(models0, print) + Output + + Call: + mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) (Intercept) + 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) (Intercept) + 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C1 (Intercept) C1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) C1 (Intercept) C1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) (Intercept) c1 c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) (Intercept) c1 c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C2 (Intercept) C2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) C2 (Intercept) C2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) (Intercept) c2 c2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) (Intercept) c2 c2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + Call: + lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) m1B m1C + 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + Call: + lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) m2B m2C + 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + Call: + mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) + + (1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) M22 M23 M24 + 0 0 0 0 + abs(C1 - C2) log(C1) (Intercept) M22 + 0 0 0 0 + M23 M24 abs(C1 - C2) log(C1) + 0 0 0 0 + m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) + 0 0 0 0 + m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + (Intercept) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + Call: + mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time | + id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C1 B21 (Intercept) C1 B21 + 0 0 0 0 0 0 + time c1 time c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 m1 m1 m1 + (Intercept) c1 time c1:time + m1 (Intercept) 0 0 0 0 + m1 c1 0 0 0 0 + m1 time 0 0 0 0 + m1 c1:time 0 0 0 0 + + + Call: + mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C1 (Intercept) C1 time I(time^2) + 0 0 0 0 0 0 + b21 c1 C1:time b21:c1 time I(time^2) + 0 0 0 0 0 0 + b21 c1 C1:time b21:c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 m1 + (Intercept) time + m1 (Intercept) 0 0 + m1 time 0 0 + + + Call: + mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, + random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C1 (Intercept) C1 log(time) I(time^2) + 0 0 0 0 0 0 + p1 log(time) I(time^2) p1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + $m0a + + Call: + mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) (Intercept) + 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + $m0b + + Call: + mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) (Intercept) + 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + $m1a + + Call: + mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C1 (Intercept) C1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + $m1b + + Call: + mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) C1 (Intercept) C1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + $m1c + + Call: + mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) (Intercept) c1 c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + $m1d + + Call: + mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) (Intercept) c1 c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + $m2a + + Call: + mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C2 (Intercept) C2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + $m2b + + Call: + mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) C2 (Intercept) C2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + $m2c + + Call: + mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) (Intercept) c2 c2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + $m2d + + Call: + mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m2" + + Fixed effects: + (Intercept) (Intercept) c2 c2 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m2 + (Intercept) + m2 (Intercept) 0 + + + $m3a + + Call: + lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) m1B m1C + 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + $m3b + + Call: + lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian linear mixed model for "c1" + + Fixed effects: + (Intercept) m2B m2C + 0 0 0 + + + Random effects covariance matrix: + $id + c1 + (Intercept) + c1 (Intercept) 0 + + + + Residual standard deviation: + sigma_c1 + 0 + + $m4a + + Call: + mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) + + (1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) M22 M23 M24 + 0 0 0 0 + abs(C1 - C2) log(C1) (Intercept) M22 + 0 0 0 0 + M23 M24 abs(C1 - C2) log(C1) + 0 0 0 0 + m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) + 0 0 0 0 + m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + $m4b + + Call: + mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + (Intercept) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + $m4c + + Call: + mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time | + id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C1 B21 (Intercept) C1 B21 + 0 0 0 0 0 0 + time c1 time c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 m1 m1 m1 + (Intercept) c1 time c1:time + m1 (Intercept) 0 0 0 0 + m1 c1 0 0 0 0 + m1 time 0 0 0 0 + m1 c1:time 0 0 0 0 + + + $m4d + + Call: + mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C1 (Intercept) C1 time I(time^2) + 0 0 0 0 0 0 + b21 c1 C1:time b21:c1 time I(time^2) + 0 0 0 0 0 0 + b21 c1 C1:time b21:c1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 m1 + (Intercept) time + m1 (Intercept) 0 0 + m1 time 0 0 + + + $m4e + + Call: + mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, + random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + Bayesian multinomial logit mixed model for "m1" + + Fixed effects: + (Intercept) C1 (Intercept) C1 log(time) I(time^2) + 0 0 0 0 0 0 + p1 log(time) I(time^2) p1 + 0 0 0 0 + + + Random effects covariance matrix: + $id + m1 + (Intercept) + m1 (Intercept) 0 + + + +--- + + Code + lapply(models0, coef) + Output + $m0a + $m0a$m1 + (Intercept) (Intercept) D_m1_id[1,1] + 0 0 0 + + + $m0b + $m0b$m2 + (Intercept) (Intercept) D_m2_id[1,1] + 0 0 0 + + + $m1a + $m1a$m1 + (Intercept) C1 (Intercept) C1 D_m1_id[1,1] + 0 0 0 0 0 + + + $m1b + $m1b$m2 + (Intercept) C1 (Intercept) C1 D_m2_id[1,1] + 0 0 0 0 0 + + + $m1c + $m1c$m1 + (Intercept) (Intercept) c1 c1 D_m1_id[1,1] + 0 0 0 0 0 + + + $m1d + $m1d$m2 + (Intercept) (Intercept) c1 c1 D_m2_id[1,1] + 0 0 0 0 0 + + + $m2a + $m2a$m1 + (Intercept) C2 (Intercept) C2 D_m1_id[1,1] + 0 0 0 0 0 + + + $m2b + $m2b$m2 + (Intercept) C2 (Intercept) C2 D_m2_id[1,1] + 0 0 0 0 0 + + + $m2c + $m2c$m1 + (Intercept) (Intercept) c2 c2 D_m1_id[1,1] + 0 0 0 0 0 + + + $m2d + $m2d$m2 + (Intercept) (Intercept) c2 c2 D_m2_id[1,1] + 0 0 0 0 0 + + + $m3a + $m3a$c1 + (Intercept) m1B m1C sigma_c1 D_c1_id[1,1] + 0 0 0 0 0 + + + $m3b + $m3b$c1 + (Intercept) m2B m2C sigma_c1 D_c1_id[1,1] + 0 0 0 0 0 + + + $m4a + $m4a$m1 + (Intercept) M22 M23 M24 + 0 0 0 0 + abs(C1 - C2) log(C1) (Intercept) M22 + 0 0 0 0 + M23 M24 abs(C1 - C2) log(C1) + 0 0 0 0 + m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) + 0 0 0 0 + m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) + 0 0 0 0 + D_m1_id[1,1] + 0 + + + $m4b + $m4b$m1 + (Intercept) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + (Intercept) + 0 + abs(C1 - C2) + 0 + log(C1) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) + 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) + 0 + D_m1_id[1,1] + 0 + + + $m4c + $m4c$m1 + (Intercept) C1 B21 (Intercept) C1 B21 + 0 0 0 0 0 0 + time c1 time c1 D_m1_id[1,1] D_m1_id[1,2] + 0 0 0 0 0 0 + D_m1_id[2,2] D_m1_id[1,3] D_m1_id[2,3] D_m1_id[3,3] D_m1_id[1,4] D_m1_id[2,4] + 0 0 0 0 0 0 + D_m1_id[3,4] D_m1_id[4,4] + 0 0 + + + $m4d + $m4d$m1 + (Intercept) C1 (Intercept) C1 time I(time^2) + 0 0 0 0 0 0 + b21 c1 C1:time b21:c1 time I(time^2) + 0 0 0 0 0 0 + b21 c1 C1:time b21:c1 D_m1_id[1,1] D_m1_id[1,2] + 0 0 0 0 0 0 + D_m1_id[2,2] + 0 + + + $m4e + $m4e$m1 + (Intercept) C1 (Intercept) C1 log(time) I(time^2) + 0 0 0 0 0 0 + p1 log(time) I(time^2) p1 D_m1_id[1,1] + 0 0 0 0 0 + + + +--- + + Code + lapply(models0, confint) + Output + $m0a + $m0a$m1 + 2.5% 97.5% + (Intercept) 0 0 + (Intercept) 0 0 + D_m1_id[1,1] 0 0 + + + $m0b + $m0b$m2 + 2.5% 97.5% + (Intercept) 0 0 + (Intercept) 0 0 + D_m2_id[1,1] 0 0 + + + $m1a + $m1a$m1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + (Intercept) 0 0 + C1 0 0 + D_m1_id[1,1] 0 0 + + + $m1b + $m1b$m2 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + (Intercept) 0 0 + C1 0 0 + D_m2_id[1,1] 0 0 + + + $m1c + $m1c$m1 + 2.5% 97.5% + (Intercept) 0 0 + (Intercept) 0 0 + c1 0 0 + c1 0 0 + D_m1_id[1,1] 0 0 + + + $m1d + $m1d$m2 + 2.5% 97.5% + (Intercept) 0 0 + (Intercept) 0 0 + c1 0 0 + c1 0 0 + D_m2_id[1,1] 0 0 + + + $m2a + $m2a$m1 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + (Intercept) 0 0 + C2 0 0 + D_m1_id[1,1] 0 0 + + + $m2b + $m2b$m2 + 2.5% 97.5% + (Intercept) 0 0 + C2 0 0 + (Intercept) 0 0 + C2 0 0 + D_m2_id[1,1] 0 0 + + + $m2c + $m2c$m1 + 2.5% 97.5% + (Intercept) 0 0 + (Intercept) 0 0 + c2 0 0 + c2 0 0 + D_m1_id[1,1] 0 0 + + + $m2d + $m2d$m2 + 2.5% 97.5% + (Intercept) 0 0 + (Intercept) 0 0 + c2 0 0 + c2 0 0 + D_m2_id[1,1] 0 0 + + + $m3a + $m3a$c1 + 2.5% 97.5% + (Intercept) 0 0 + m1B 0 0 + m1C 0 0 + sigma_c1 0 0 + D_c1_id[1,1] 0 0 + + + $m3b + $m3b$c1 + 2.5% 97.5% + (Intercept) 0 0 + m2B 0 0 + m2C 0 0 + sigma_c1 0 0 + D_c1_id[1,1] 0 0 + + + $m4a + $m4a$m1 + 2.5% 97.5% + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + (Intercept) 0 0 + M22 0 0 + M23 0 0 + M24 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + m2B 0 0 + m2C 0 0 + m2B:abs(C1 - C2) 0 0 + m2C:abs(C1 - C2) 0 0 + m2B 0 0 + m2C 0 0 + m2B:abs(C1 - C2) 0 0 + m2C:abs(C1 - C2) 0 0 + D_m1_id[1,1] 0 0 + + + $m4b + $m4b$m1 + 2.5% 97.5% + (Intercept) 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + (Intercept) 0 0 + abs(C1 - C2) 0 0 + log(C1) 0 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 + ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 + D_m1_id[1,1] 0 0 + + + $m4c + $m4c$m1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + B21 0 0 + (Intercept) 0 0 + C1 0 0 + B21 0 0 + time 0 0 + c1 0 0 + time 0 0 + c1 0 0 + D_m1_id[1,1] 0 0 + D_m1_id[1,2] 0 0 + D_m1_id[2,2] 0 0 + D_m1_id[1,3] 0 0 + D_m1_id[2,3] 0 0 + D_m1_id[3,3] 0 0 + D_m1_id[1,4] 0 0 + D_m1_id[2,4] 0 0 + D_m1_id[3,4] 0 0 + D_m1_id[4,4] 0 0 + + + $m4d + $m4d$m1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + (Intercept) 0 0 + C1 0 0 + time 0 0 + I(time^2) 0 0 + b21 0 0 + c1 0 0 + C1:time 0 0 + b21:c1 0 0 + time 0 0 + I(time^2) 0 0 + b21 0 0 + c1 0 0 + C1:time 0 0 + b21:c1 0 0 + D_m1_id[1,1] 0 0 + D_m1_id[1,2] 0 0 + D_m1_id[2,2] 0 0 + + + $m4e + $m4e$m1 + 2.5% 97.5% + (Intercept) 0 0 + C1 0 0 + (Intercept) 0 0 + C1 0 0 + log(time) 0 0 + I(time^2) 0 0 + p1 0 0 + log(time) 0 0 + I(time^2) 0 0 + p1 0 0 + D_m1_id[1,1] 0 0 + + + +--- + + Code + lapply(models0, summary) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m0b + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m1a + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C1 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m1b + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2B: C1 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + m2C: C1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m1c + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1B: c1 0 0 0 0 0 NaN NaN + m1C: c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m1d + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + m2B: c1 0 0 0 0 0 NaN NaN + m2C: c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m2a + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C2 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m2b + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2B: C2 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + m2C: C2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m2c + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1B: c2 0 0 0 0 0 NaN NaN + m1C: c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m2d + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + m2B: c2 0 0 0 0 0 NaN NaN + m2C: c2 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m2_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m3a + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + m1B 0 0 0 0 0 NaN NaN + m1C 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_c1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_c1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 1:10 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m3b + + Bayesian linear mixed model fitted with JointAI + + Call: + lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + m2B 0 0 0 0 0 NaN NaN + m2C 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_c1_id[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of residual std. deviation: + Mean SD 2.5% 97.5% GR-crit MCE/SD + sigma_c1 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4a + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) + + (1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: M22 0 0 0 0 0 NaN NaN + m1B: M23 0 0 0 0 0 NaN NaN + m1B: M24 0 0 0 0 0 NaN NaN + m1B: abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1B: log(C1) 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: M22 0 0 0 0 0 NaN NaN + m1C: M23 0 0 0 0 0 NaN NaN + m1C: M24 0 0 0 0 0 NaN NaN + m1C: abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1C: log(C1) 0 0 0 0 0 NaN NaN + m1B: m2B 0 0 0 0 0 NaN NaN + m1B: m2C 0 0 0 0 0 NaN NaN + m1B: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1B: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1C: m2B 0 0 0 0 0 NaN NaN + m1C: m2C 0 0 0 0 0 NaN NaN + m1C: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1C: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4b + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1), + 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, + n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% + m1B: (Intercept) 0 0 0 + m1B: abs(C1 - C2) 0 0 0 + m1B: log(C1) 0 0 0 + m1C: (Intercept) 0 0 0 + m1C: abs(C1 - C2) 0 0 0 + m1C: log(C1) 0 0 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 + 97.5% + m1B: (Intercept) 0 + m1B: abs(C1 - C2) 0 + m1B: log(C1) 0 + m1C: (Intercept) 0 + m1C: abs(C1 - C2) 0 + m1C: log(C1) 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 + tail-prob. + m1B: (Intercept) 0 + m1B: abs(C1 - C2) 0 + m1B: log(C1) 0 + m1C: (Intercept) 0 + m1C: abs(C1 - C2) 0 + m1C: log(C1) 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 + GR-crit MCE/SD + m1B: (Intercept) NaN NaN + m1B: abs(C1 - C2) NaN NaN + m1B: log(C1) NaN NaN + m1C: (Intercept) NaN NaN + m1C: abs(C1 - C2) NaN NaN + m1C: log(C1) NaN NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4c + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time | + id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C1 0 0 0 0 0 NaN NaN + m1B: B21 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C1 0 0 0 0 0 NaN NaN + m1C: B21 0 0 0 0 0 NaN NaN + m1B: time 0 0 0 0 0 NaN NaN + m1B: c1 0 0 0 0 0 NaN NaN + m1C: time 0 0 0 0 0 NaN NaN + m1C: c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + D_m1_id[1,2] 0 0 0 0 0 NaN NaN + D_m1_id[2,2] 0 0 0 0 NaN NaN + D_m1_id[1,3] 0 0 0 0 0 NaN NaN + D_m1_id[2,3] 0 0 0 0 0 NaN NaN + D_m1_id[3,3] 0 0 0 0 NaN NaN + D_m1_id[1,4] 0 0 0 0 0 NaN NaN + D_m1_id[2,4] 0 0 0 0 0 NaN NaN + D_m1_id[3,4] 0 0 0 0 0 NaN NaN + D_m1_id[4,4] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4d + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, + random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C1 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C1 0 0 0 0 0 NaN NaN + m1B: time 0 0 0 0 0 NaN NaN + m1B: I(time^2) 0 0 0 0 0 NaN NaN + m1B: b21 0 0 0 0 0 NaN NaN + m1B: c1 0 0 0 0 0 NaN NaN + m1B: C1:time 0 0 0 0 0 NaN NaN + m1B: b21:c1 0 0 0 0 0 NaN NaN + m1C: time 0 0 0 0 0 NaN NaN + m1C: I(time^2) 0 0 0 0 0 NaN NaN + m1C: b21 0 0 0 0 0 NaN NaN + m1C: c1 0 0 0 0 0 NaN NaN + m1C: C1:time 0 0 0 0 0 NaN NaN + m1C: b21:c1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + D_m1_id[1,2] 0 0 0 0 0 NaN NaN + D_m1_id[2,2] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + $m4e + + Bayesian multinomial logit mixed model fitted with JointAI + + Call: + mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, + random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", + seed = 2020, warn = FALSE, mess = FALSE) + + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C1 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C1 0 0 0 0 0 NaN NaN + m1B: log(time) 0 0 0 0 0 NaN NaN + m1B: I(time^2) 0 0 0 0 0 NaN NaN + m1B: p1 0 0 0 0 0 NaN NaN + m1C: log(time) 0 0 0 0 0 NaN NaN + m1C: I(time^2) 0 0 0 0 0 NaN NaN + m1C: p1 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_m1_id[1,1] 0 0 0 0 NaN NaN + + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 329 + Number of groups: + - id: 100 + + +--- + + Code + lapply(models0, function(x) coef(summary(x))) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + $m0a$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + + + $m0b + $m0b$m2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + + + $m1a + $m1a$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C1 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C1 0 0 0 0 0 NaN NaN + + + $m1b + $m1b$m2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2B: C1 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + m2C: C1 0 0 0 0 0 NaN NaN + + + $m1c + $m1c$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1B: c1 0 0 0 0 0 NaN NaN + m1C: c1 0 0 0 0 0 NaN NaN + + + $m1d + $m1d$m2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + m2B: c1 0 0 0 0 0 NaN NaN + m2C: c1 0 0 0 0 0 NaN NaN + + + $m2a + $m2a$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C2 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C2 0 0 0 0 0 NaN NaN + + + $m2b + $m2b$m2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2B: C2 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + m2C: C2 0 0 0 0 0 NaN NaN + + + $m2c + $m2c$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1B: c2 0 0 0 0 0 NaN NaN + m1C: c2 0 0 0 0 0 NaN NaN + + + $m2d + $m2d$m2 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m2B: (Intercept) 0 0 0 0 0 NaN NaN + m2C: (Intercept) 0 0 0 0 0 NaN NaN + m2B: c2 0 0 0 0 0 NaN NaN + m2C: c2 0 0 0 0 0 NaN NaN + + + $m3a + $m3a$c1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + m1B 0 0 0 0 0 NaN NaN + m1C 0 0 0 0 0 NaN NaN + + + $m3b + $m3b$c1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + m2B 0 0 0 0 0 NaN NaN + m2C 0 0 0 0 0 NaN NaN + + + $m4a + $m4a$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: M22 0 0 0 0 0 NaN NaN + m1B: M23 0 0 0 0 0 NaN NaN + m1B: M24 0 0 0 0 0 NaN NaN + m1B: abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1B: log(C1) 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: M22 0 0 0 0 0 NaN NaN + m1C: M23 0 0 0 0 0 NaN NaN + m1C: M24 0 0 0 0 0 NaN NaN + m1C: abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1C: log(C1) 0 0 0 0 0 NaN NaN + m1B: m2B 0 0 0 0 0 NaN NaN + m1B: m2C 0 0 0 0 0 NaN NaN + m1B: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1B: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1C: m2B 0 0 0 0 0 NaN NaN + m1C: m2C 0 0 0 0 0 NaN NaN + m1C: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN + m1C: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN + + + $m4b + $m4b$m1 + Mean SD 2.5% + m1B: (Intercept) 0 0 0 + m1B: abs(C1 - C2) 0 0 0 + m1B: log(C1) 0 0 0 + m1C: (Intercept) 0 0 0 + m1C: abs(C1 - C2) 0 0 0 + m1C: log(C1) 0 0 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 + 97.5% + m1B: (Intercept) 0 + m1B: abs(C1 - C2) 0 + m1B: log(C1) 0 + m1C: (Intercept) 0 + m1C: abs(C1 - C2) 0 + m1C: log(C1) 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 + tail-prob. + m1B: (Intercept) 0 + m1B: abs(C1 - C2) 0 + m1B: log(C1) 0 + m1C: (Intercept) 0 + m1C: abs(C1 - C2) 0 + m1C: log(C1) 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 + GR-crit MCE/SD + m1B: (Intercept) NaN NaN + m1B: abs(C1 - C2) NaN NaN + m1B: log(C1) NaN NaN + m1C: (Intercept) NaN NaN + m1C: abs(C1 - C2) NaN NaN + m1C: log(C1) NaN NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN + m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN + m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN + + + $m4c + $m4c$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C1 0 0 0 0 0 NaN NaN + m1B: B21 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C1 0 0 0 0 0 NaN NaN + m1C: B21 0 0 0 0 0 NaN NaN + m1B: time 0 0 0 0 0 NaN NaN + m1B: c1 0 0 0 0 0 NaN NaN + m1C: time 0 0 0 0 0 NaN NaN + m1C: c1 0 0 0 0 0 NaN NaN + + + $m4d + $m4d$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C1 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C1 0 0 0 0 0 NaN NaN + m1B: time 0 0 0 0 0 NaN NaN + m1B: I(time^2) 0 0 0 0 0 NaN NaN + m1B: b21 0 0 0 0 0 NaN NaN + m1B: c1 0 0 0 0 0 NaN NaN + m1B: C1:time 0 0 0 0 0 NaN NaN + m1B: b21:c1 0 0 0 0 0 NaN NaN + m1C: time 0 0 0 0 0 NaN NaN + m1C: I(time^2) 0 0 0 0 0 NaN NaN + m1C: b21 0 0 0 0 0 NaN NaN + m1C: c1 0 0 0 0 0 NaN NaN + m1C: C1:time 0 0 0 0 0 NaN NaN + m1C: b21:c1 0 0 0 0 0 NaN NaN + + + $m4e + $m4e$m1 + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + m1B: (Intercept) 0 0 0 0 0 NaN NaN + m1B: C1 0 0 0 0 0 NaN NaN + m1C: (Intercept) 0 0 0 0 0 NaN NaN + m1C: C1 0 0 0 0 0 NaN NaN + m1B: log(time) 0 0 0 0 0 NaN NaN + m1B: I(time^2) 0 0 0 0 0 NaN NaN + m1B: p1 0 0 0 0 0 NaN NaN + m1C: log(time) 0 0 0 0 0 NaN NaN + m1C: I(time^2) 0 0 0 0 0 NaN NaN + m1C: p1 0 0 0 0 0 NaN NaN + + + diff --git a/tests/testthat/_snaps/survreg.md b/tests/testthat/_snaps/survreg.md new file mode 100644 index 00000000..15ac1aa0 --- /dev/null +++ b/tests/testthat/_snaps/survreg.md @@ -0,0 +1,4093 @@ +# data_list remains the same + + Code + lapply(models, "[[", "data_list") + Output + $m0a + $m0a$M_lvlone + futime status != "censored" (Intercept) + 1 400 1 1 + 3 5169 0 1 + 12 1012 1 1 + 16 1925 1 1 + 23 1505 1 1 + 29 2503 1 1 + 35 2501 0 1 + 42 2466 1 1 + 50 2400 1 1 + 57 51 1 1 + 58 3762 1 1 + 70 304 1 1 + 72 4247 0 1 + 84 1217 1 1 + 91 3584 1 1 + 102 4345 0 1 + 115 769 1 1 + 118 132 1 1 + 119 4901 0 1 + 134 1356 1 1 + 138 3657 1 1 + 150 673 1 1 + 153 264 1 1 + 155 4079 1 1 + 168 4796 0 1 + 180 1444 1 1 + 186 77 1 1 + 187 549 1 1 + 190 5074 1 1 + 200 321 1 1 + 203 3839 1 1 + 215 5192 0 1 + 231 3170 1 1 + 241 4602 0 1 + 255 2847 1 1 + 259 4281 0 1 + 270 223 1 1 + 272 3244 1 1 + 282 2297 1 1 + 290 5136 0 1 + 305 1350 1 1 + 309 5122 0 1 + 325 5225 0 1 + 340 3428 1 1 + 351 4694 0 1 + 360 2256 1 1 + 368 3245 0 1 + 375 5096 0 1 + 384 708 1 1 + 388 2598 1 1 + 397 3853 1 1 + 407 2386 1 1 + 416 1000 1 1 + 419 1434 1 1 + 424 1360 1 1 + 430 1847 1 1 + 436 3282 1 1 + 447 5128 0 1 + 463 2224 1 1 + 470 5034 0 1 + 483 4925 0 1 + 497 3090 1 1 + 507 859 1 1 + 510 1487 1 1 + 516 4842 0 1 + 522 4191 1 1 + 535 2769 1 1 + 545 4708 0 1 + 559 1170 1 1 + 563 3683 1 1 + 576 4865 0 1 + 587 4853 0 1 + 593 4859 0 1 + 608 1827 1 1 + 612 1191 1 1 + 617 71 1 1 + 618 326 1 1 + 620 1690 1 1 + 624 4376 0 1 + 635 890 1 1 + 639 2540 1 1 + 649 3574 1 1 + 659 4719 0 1 + 674 4701 0 1 + 677 3358 1 1 + 688 1657 1 1 + 689 198 1 1 + 691 3076 1 1 + 695 1741 1 1 + 699 2689 1 1 + 708 460 1 1 + 711 389 1 1 + 712 4583 0 1 + 727 750 1 1 + 730 137 1 1 + 731 4520 0 1 + 745 620 1 1 + 749 4492 0 1 + 763 4489 0 1 + 776 552 1 1 + 780 4250 0 1 + 792 3770 0 1 + 804 110 1 1 + 805 3086 1 1 + 815 3092 1 1 + 825 3222 1 1 + 830 4058 0 1 + 841 2583 1 1 + 849 3173 0 1 + 859 2044 1 1 + 866 2350 1 1 + 874 3445 1 1 + 885 951 1 1 + 890 3395 1 1 + 901 4091 0 1 + 913 4015 0 1 + 924 1083 1 1 + 929 2288 1 1 + 936 515 1 1 + 938 2033 1 1 + 946 191 1 1 + 947 3966 0 1 + 957 971 1 1 + 959 3903 1 1 + 960 2468 1 1 + 969 824 1 1 + 974 3924 0 1 + 985 1037 1 1 + 990 3908 0 1 + 1002 1411 1 1 + 1008 850 1 1 + 1012 3613 0 1 + 1018 2796 1 1 + 1027 3818 0 1 + 1039 3819 0 1 + 1049 3767 0 1 + 1058 3659 0 1 + 1070 1297 1 1 + 1076 2357 1 1 + 1082 3728 0 1 + 1094 3719 0 1 + 1097 2419 1 1 + 1106 786 1 1 + 1109 945 1 1 + 1113 3645 0 1 + 1117 3086 1 1 + 1124 3382 1 1 + 1129 1427 1 1 + 1135 762 1 1 + 1138 3560 0 1 + 1147 3539 0 1 + 1158 1152 1 1 + 1161 3532 0 1 + 1171 140 1 1 + 1172 3516 0 1 + 1175 853 1 1 + 1179 3504 0 1 + 1190 2475 1 1 + 1199 1536 1 1 + 1203 3441 0 1 + 1211 3466 0 1 + 1222 186 1 1 + 1223 2055 1 1 + 1226 276 1 1 + 1227 1076 1 1 + 1232 3390 0 1 + 1240 1684 1 1 + 1246 3384 0 1 + 1256 1212 1 1 + 1261 3361 0 1 + 1262 3243 0 1 + 1272 2970 0 1 + 1279 3326 0 1 + 1288 3313 0 1 + 1298 3293 0 1 + 1308 1492 1 1 + 1314 3278 0 1 + 1315 3249 0 1 + 1321 3242 0 1 + 1324 3232 0 1 + 1334 3225 0 1 + 1335 3224 0 1 + 1340 2241 1 1 + 1348 974 1 1 + 1353 2882 1 1 + 1358 1576 1 1 + 1362 733 1 1 + 1366 2635 0 1 + 1374 3125 0 1 + 1379 3173 0 1 + 1383 216 1 1 + 1384 3112 0 1 + 1394 797 1 1 + 1398 3118 0 1 + 1403 2999 0 1 + 1404 2555 1 1 + 1412 3034 0 1 + 1421 3025 0 1 + 1429 2991 0 1 + 1432 2932 1 1 + 1443 2963 0 1 + 1453 2941 0 1 + 1455 2890 0 1 + 1464 2090 1 1 + 1472 2081 1 1 + 1477 2924 0 1 + 1485 2840 0 1 + 1492 904 1 1 + 1495 2888 0 1 + 1497 2893 0 1 + 1507 2865 0 1 + 1515 2845 0 1 + 1521 2189 0 1 + 1523 1786 1 1 + 1527 1080 1 1 + 1531 2336 0 1 + 1538 790 1 1 + 1542 2839 0 1 + 1552 2826 0 1 + 1558 1235 1 1 + 1563 2719 0 1 + 1571 597 1 1 + 1575 334 1 1 + 1576 2614 0 1 + 1584 2691 0 1 + 1593 2647 0 1 + 1602 999 1 1 + 1606 2636 0 1 + 1610 348 1 1 + 1613 2648 0 1 + 1617 1165 1 1 + 1621 2620 0 1 + 1625 2601 0 1 + 1626 2445 0 1 + 1628 2302 1 1 + 1630 2577 0 1 + 1632 1947 1 1 + 1634 1874 1 1 + 1637 694 1 1 + 1640 2500 0 1 + 1648 837 1 1 + 1652 2128 1 1 + 1660 930 1 1 + 1662 1690 1 1 + 1666 2459 0 1 + 1668 1435 1 1 + 1674 940 1 1 + 1678 2454 0 1 + 1686 2452 0 1 + 1689 2327 1 1 + 1691 2307 0 1 + 1692 2439 0 1 + 1695 2434 0 1 + 1703 737 1 1 + 1707 2405 0 1 + 1713 2370 0 1 + 1719 2283 0 1 + 1722 2371 0 1 + 1730 2284 0 1 + 1735 1674 1 1 + 1736 2348 0 1 + 1742 1850 1 1 + 1745 1303 1 1 + 1750 1542 1 1 + 1754 1084 1 1 + 1758 2287 0 1 + 1765 179 1 1 + 1766 1191 1 1 + 1768 1898 1 1 + 1775 2010 1 1 + 1778 2238 0 1 + 1786 2194 0 1 + 1792 1649 1 1 + 1795 1447 1 1 + 1800 2020 0 1 + 1804 2147 0 1 + 1806 2105 0 1 + 1811 1996 1 1 + 1816 2102 0 1 + 1823 2081 0 1 + 1830 41 1 1 + 1831 1673 1 1 + 1835 2095 0 1 + 1839 2087 0 1 + 1846 2070 0 1 + 1847 2077 0 1 + 1849 1552 0 1 + 1853 1067 1 1 + 1855 799 1 1 + 1859 2032 0 1 + 1866 901 1 1 + 1871 1785 1 1 + 1874 1989 0 1 + 1876 1971 0 1 + 1883 875 1 1 + 1885 1990 0 1 + 1888 533 1 1 + 1891 1969 0 1 + 1894 1962 0 1 + 1895 207 1 1 + 1897 1969 0 1 + 1900 1940 0 1 + 1905 1597 1 1 + 1909 1899 0 1 + 1910 1885 0 1 + 1915 1885 0 1 + 1917 1818 0 1 + 1922 1822 0 1 + 1927 1663 0 1 + 1932 1608 0 1 + 1937 1508 0 1 + 1941 1457 0 1 + + $m0a$mu_reg_surv + [1] 0 + + $m0a$tau_reg_surv + [1] 0.001 + + $m0a$cens_Srv_ftm_stts_cn + 1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102 + 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 + 115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215 + 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 + 231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375 + 0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1 + 384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510 + 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 + 516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635 + 1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0 + 639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731 + 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 + 745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874 + 0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0 + 885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985 + 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 + 990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109 + 1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0 + 1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203 + 1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1 + 1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308 + 1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0 + 1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384 + 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1 + 1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492 + 0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0 + 1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576 + 1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 + 1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640 + 1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1 + 1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713 + 0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1 + 1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786 + 1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1 + 1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853 + 0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0 + 1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909 + 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1 + 1910 1915 1917 1922 1927 1932 1937 1941 + 1 1 1 1 1 1 1 1 + + $m0a$Srv_ftm_stts_cn + [1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584 + [16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321 + [31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA + [46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA + [61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191 + [76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689 + [91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092 + [106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033 + [121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA + [136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA + [151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076 + [166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA + [181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA + [196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA + [211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA + [226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA + [241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA + [256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010 + [271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA + [286] NA NA 1067 799 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0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0 + 1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576 + 1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 + 1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640 + 1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1 + 1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713 + 0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1 + 1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786 + 1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1 + 1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853 + 0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0 + 1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909 + 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1 + 1910 1915 1917 1922 1927 1932 1937 1941 + 1 1 1 1 1 1 1 1 + + $m3b$Srv_ftm_stts_cn + [1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584 + [16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321 + [31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA + [46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA + [61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191 + [76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689 + [91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092 + [106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033 + [121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA + [136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA + [151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076 + [166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA + [181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA + [196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA + [211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA + [226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA + [241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA + [256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010 + [271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA + [286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207 + [301] NA NA 1597 NA NA NA NA NA NA NA NA NA + + + +# jagsmodel remains the same + + Code + lapply(models, "[[", "jagsmodel") + Output + $m0a + model { + + # Weibull survival model for Srv_ftm_stts_cn ------------------------------------ + for (i in 1:312) { + Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn) + cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1]) + log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1]) + } + + + # Priors for the model for Srv_ftm_stts_cn + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + shape_Srv_ftm_stts_cn ~ dexp(0.01) + + } + $m1a + model { + + # Weibull survival model for Srv_ftm_stts_cn ------------------------------------ + for (i in 1:312) { + Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn) + cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1]) + log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] + + M_lvlone[i, 5] * beta[3]) + } + + + # Priors for the model for Srv_ftm_stts_cn + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + shape_Srv_ftm_stts_cn ~ dexp(0.01) + + } + $m1b + model { + + # Weibull survival model for Srv_ftm_stts_cn ------------------------------------ + for (i in 1:312) { + Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn) + cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1]) + log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] + + M_lvlone[i, 5] * beta[3]) + } + + + # Priors for the model for Srv_ftm_stts_cn + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + shape_Srv_ftm_stts_cn ~ dexp(0.01) + + } + $m2a + model { + + # Weibull survival model for Srv_ftm_stts_cn ------------------------------------ + for (i in 1:312) { + Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn) + cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1]) + log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]) + } + + + # Priors for the model for Srv_ftm_stts_cn + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + shape_Srv_ftm_stts_cn ~ dexp(0.01) + + + + # Normal model for copper ------------------------------------------------------- + for (i in 1:312) { + M_lvlone[i, 3] ~ dnorm(mu_copper[i], tau_copper) + mu_copper[i] <- M_lvlone[i, 4] * alpha[1] + } + + # Priors for the model for copper + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_copper <- sqrt(1/tau_copper) + + } + $m3a + model { + + # Weibull survival model for Srv_ftm_stts_cn ------------------------------------ + for (i in 1:312) { + Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn) + cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1]) + log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 5] * beta[1] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] + + M_lvlone[i, 6] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + + (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * beta[6]) + } + + + # Priors for the model for Srv_ftm_stts_cn + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + shape_Srv_ftm_stts_cn ~ dexp(0.01) + + + + # Normal model for trig --------------------------------------------------------- + for (i in 1:312) { + M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, ) + mu_trig[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[4] + + M_lvlone[i, 9] <- log(M_lvlone[i, 3]) + + + } + + # Priors for the model for trig + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_trig <- sqrt(1/tau_trig) + + + + # Normal model for copper ------------------------------------------------------- + for (i in 1:312) { + M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper) + mu_copper[i] <- M_lvlone[i, 5] * alpha[5] + M_lvlone[i, 6] * alpha[6] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[7] + + M_lvlone[i, 8] <- abs(M_lvlone[i, 7] - M_lvlone[i, 4]) + + + } + + # Priors for the model for copper + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_copper <- sqrt(1/tau_copper) + + } + $m3b + model { + + # Weibull survival model for Srv_ftm_stts_cn ------------------------------------ + for (i in 1:312) { + Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn) + cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1]) + log(rate_Srv_ftm_stts_cn[i]) <- -(b_Srv_ftm_stts_cn_center[group_center[i], 1] + + beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[3] * M_lvlone[i, 5] + + beta[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[5] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + + beta[6] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]) + } + + for (ii in 1:10) { + b_Srv_ftm_stts_cn_center[ii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[ii, ], invD_Srv_ftm_stts_cn_center[ , ]) + mu_b_Srv_ftm_stts_cn_center[ii, 1] <- M_center[ii, 1] * beta[1] + } + + + # Priors for the model for Srv_ftm_stts_cn + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) + } + + shape_Srv_ftm_stts_cn ~ dexp(0.01) + + invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1]) + + + # Normal mixed effects model for trig ------------------------------------------- + for (i in 1:312) { + M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, ) + mu_trig[i] <- b_trig_center[group_center[i], 1] + + alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[3] * M_lvlone[i, 5] + + alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 8] <- log(M_lvlone[i, 3]) + + } + + for (ii in 1:10) { + b_trig_center[ii, 1:1] ~ dnorm(mu_b_trig_center[ii, ], invD_trig_center[ , ]) + mu_b_trig_center[ii, 1] <- M_center[ii, 1] * alpha[1] + } + + # Priors for the model for trig + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_trig <- sqrt(1/tau_trig) + + invD_trig_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_trig_center[1, 1] <- 1 / (invD_trig_center[1, 1]) + + + # Normal mixed effects model for copper ----------------------------------------- + for (i in 1:312) { + M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper) + mu_copper[i] <- b_copper_center[group_center[i], 1] + alpha[6] * M_lvlone[i, 5] + + alpha[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 7] <- abs(M_lvlone[i, 6] - M_lvlone[i, 4]) + + } + + for (ii in 1:10) { + b_copper_center[ii, 1:1] ~ dnorm(mu_b_copper_center[ii, ], invD_copper_center[ , ]) + mu_b_copper_center[ii, 1] <- M_center[ii, 1] * alpha[5] + } + + # Priors for the model for copper + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_copper <- sqrt(1/tau_copper) + + invD_copper_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_copper_center[1, 1] <- 1 / (invD_copper_center[1, 1]) + } + +# GRcrit and MCerror give same result + + Code + lapply(models0, GR_crit, multivariate = FALSE) + Output + $m0a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + shape_Srv_ftm_stts_cn NaN NaN + + + $m1a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + age NaN NaN + sexfemale NaN NaN + shape_Srv_ftm_stts_cn NaN NaN + + + $m1b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + age NaN NaN + sexfemale NaN NaN + shape_Srv_ftm_stts_cn NaN NaN + + + $m2a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + copper NaN NaN + shape_Srv_ftm_stts_cn NaN NaN + + + $m3a + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + copper NaN NaN + sexfemale NaN NaN + age NaN NaN + abs(age - copper) NaN NaN + log(trig) NaN NaN + shape_Srv_ftm_stts_cn NaN NaN + + + $m3b + Potential scale reduction factors: + + Point est. Upper C.I. + (Intercept) NaN NaN + copper NaN NaN + sexfemale NaN NaN + age NaN NaN + abs(age - copper) NaN NaN + log(trig) NaN NaN + shape_Srv_ftm_stts_cn NaN NaN + D_Srv_ftm_stts_cn_center[1,1] NaN NaN + + + +--- + + Code + lapply(models0, MC_error) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + shape_Srv_ftm_stts_cn 0 0 0 NaN + + $m1a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + age 0 0 0 NaN + sexfemale 0 0 0 NaN + shape_Srv_ftm_stts_cn 0 0 0 NaN + + $m1b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + age 0 0 0 NaN + sexfemale 0 0 0 NaN + shape_Srv_ftm_stts_cn 0 0 0 NaN + + $m2a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + copper 0 0 0 NaN + shape_Srv_ftm_stts_cn 0 0 0 NaN + + $m3a + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + copper 0 0 0 NaN + sexfemale 0 0 0 NaN + age 0 0 0 NaN + abs(age - copper) 0 0 0 NaN + log(trig) 0 0 0 NaN + shape_Srv_ftm_stts_cn 0 0 0 NaN + + $m3b + est MCSE SD MCSE/SD + (Intercept) 0 0 0 NaN + copper 0 0 0 NaN + sexfemale 0 0 0 NaN + age 0 0 0 NaN + abs(age - copper) 0 0 0 NaN + log(trig) 0 0 0 NaN + shape_Srv_ftm_stts_cn 0 0 0 NaN + D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN + + +# summary output remained the same + + Code + lapply(models0, print) + Output + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ 1, + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) + 0 + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ age + + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) age sexfemale + 0 0 0 + + Call: + survreg_imp(formula = Surv(futime, I(status != "censored")) ~ + age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian weibull survival model for "Surv(futime, I(status != "censored"))" + + + Coefficients: + (Intercept) age sexfemale + 0 0 0 + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper, + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) copper + 0 0 + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, + NA))) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) copper sexfemale age + 0 0 0 0 + abs(age - copper) log(trig) + 0 0 + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig) + (1 | center), + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(trig = c(1e-04, NA))) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) copper sexfemale age + 0 0 0 0 + abs(age - copper) log(trig) + 0 0 + $m0a + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ 1, + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) + 0 + + $m1a + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ age + + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) age sexfemale + 0 0 0 + + $m1b + + Call: + survreg_imp(formula = Surv(futime, I(status != "censored")) ~ + age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + Bayesian weibull survival model for "Surv(futime, I(status != "censored"))" + + + Coefficients: + (Intercept) age sexfemale + 0 0 0 + + $m2a + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper, + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) copper + 0 0 + + $m3a + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, + NA))) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) copper sexfemale age + 0 0 0 0 + abs(age - copper) log(trig) + 0 0 + + $m3b + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig) + (1 | center), + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(trig = c(1e-04, NA))) + + Bayesian weibull survival model for "Surv(futime, status != "censored")" + + + Coefficients: + (Intercept) copper sexfemale age + 0 0 0 0 + abs(age - copper) log(trig) + 0 0 + + +--- + + Code + lapply(models0, coef) + Output + $m0a + $m0a$`Surv(futime, status != "censored")` + (Intercept) shape_Srv_ftm_stts_cn + 0 0 + + + $m1a + $m1a$`Surv(futime, status != "censored")` + (Intercept) age sexfemale + 0 0 0 + shape_Srv_ftm_stts_cn + 0 + + + $m1b + $m1b$`Surv(futime, I(status != "censored"))` + (Intercept) age sexfemale + 0 0 0 + shape_Srv_ftm_stts_cn + 0 + + + $m2a + $m2a$`Surv(futime, status != "censored")` + (Intercept) copper shape_Srv_ftm_stts_cn + 0 0 0 + + + $m3a + $m3a$`Surv(futime, status != "censored")` + (Intercept) copper sexfemale + 0 0 0 + age abs(age - copper) log(trig) + 0 0 0 + shape_Srv_ftm_stts_cn + 0 + + + $m3b + $m3b$`Surv(futime, status != "censored")` + (Intercept) copper + 0 0 + sexfemale age + 0 0 + abs(age - copper) log(trig) + 0 0 + shape_Srv_ftm_stts_cn D_Srv_ftm_stts_cn_center[1,1] + 0 0 + + + +--- + + Code + lapply(models0, confint) + Output + $m0a + $m0a$`Surv(futime, status != "censored")` + 2.5% 97.5% + (Intercept) 0 0 + shape_Srv_ftm_stts_cn 0 0 + + + $m1a + $m1a$`Surv(futime, status != "censored")` + 2.5% 97.5% + (Intercept) 0 0 + age 0 0 + sexfemale 0 0 + shape_Srv_ftm_stts_cn 0 0 + + + $m1b + $m1b$`Surv(futime, I(status != "censored"))` + 2.5% 97.5% + (Intercept) 0 0 + age 0 0 + sexfemale 0 0 + shape_Srv_ftm_stts_cn 0 0 + + + $m2a + $m2a$`Surv(futime, status != "censored")` + 2.5% 97.5% + (Intercept) 0 0 + copper 0 0 + shape_Srv_ftm_stts_cn 0 0 + + + $m3a + $m3a$`Surv(futime, status != "censored")` + 2.5% 97.5% + (Intercept) 0 0 + copper 0 0 + sexfemale 0 0 + age 0 0 + abs(age - copper) 0 0 + log(trig) 0 0 + shape_Srv_ftm_stts_cn 0 0 + + + $m3b + $m3b$`Surv(futime, status != "censored")` + 2.5% 97.5% + (Intercept) 0 0 + copper 0 0 + sexfemale 0 0 + age 0 0 + abs(age - copper) 0 0 + log(trig) 0 0 + shape_Srv_ftm_stts_cn 0 0 + D_Srv_ftm_stts_cn_center[1,1] 0 0 + + + +--- + + Code + lapply(models0, summary) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + + Bayesian weibull survival model fitted with JointAI + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ 1, + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + Posterior summary of the shape of the Weibull distribution: + Mean SD 2.5% 97.5% GR-crit MCE/SD + shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m1a + + Bayesian weibull survival model fitted with JointAI + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ age + + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + + Posterior summary of the shape of the Weibull distribution: + Mean SD 2.5% 97.5% GR-crit MCE/SD + shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m1b + + Bayesian weibull survival model fitted with JointAI + + Call: + survreg_imp(formula = Surv(futime, I(status != "censored")) ~ + age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, + warn = FALSE, mess = FALSE) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + + Posterior summary of the shape of the Weibull distribution: + Mean SD 2.5% 97.5% GR-crit MCE/SD + shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m2a + + Bayesian weibull survival model fitted with JointAI + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper, + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + copper 0 0 0 0 0 NaN NaN + + Posterior summary of the shape of the Weibull distribution: + Mean SD 2.5% 97.5% GR-crit MCE/SD + shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m3a + + Bayesian weibull survival model fitted with JointAI + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5, + n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, + NA))) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + copper 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + abs(age - copper) 0 0 0 0 0 NaN NaN + log(trig) 0 0 0 0 0 NaN NaN + + Posterior summary of the shape of the Weibull distribution: + Mean SD 2.5% 97.5% GR-crit MCE/SD + shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + + $m3b + + Bayesian weibull survival model fitted with JointAI + + Call: + survreg_imp(formula = Surv(futime, status != "censored") ~ copper + + sex + age + abs(age - copper) + log(trig) + (1 | center), + data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, + mess = FALSE, trunc = list(trig = c(1e-04, NA))) + + + Number of events: 169 + + Posterior summary: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + copper 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + abs(age - copper) 0 0 0 0 0 NaN NaN + log(trig) 0 0 0 0 0 NaN NaN + + + Posterior summary of random effects covariance matrix: + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN + + + Posterior summary of the shape of the Weibull distribution: + Mean SD 2.5% 97.5% GR-crit MCE/SD + shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN + + + MCMC settings: + Iterations = 6:15 + Sample size per chain = 10 + Thinning interval = 1 + Number of chains = 3 + + Number of observations: 312 + Number of groups: + - center: 10 + + +--- + + Code + lapply(models0, function(x) coef(summary(x))) + Output + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + [1] "No variability observed in a component. Setting batch size to 1" + $m0a + $m0a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + + + $m1a + $m1a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + + + $m1b + $m1b$`Surv(futime, I(status != "censored"))` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + + + $m2a + $m2a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + copper 0 0 0 0 0 NaN NaN + + + $m3a + $m3a$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + copper 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + abs(age - copper) 0 0 0 0 0 NaN NaN + log(trig) 0 0 0 0 0 NaN NaN + + + $m3b + $m3b$`Surv(futime, status != "censored")` + Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD + (Intercept) 0 0 0 0 0 NaN NaN + copper 0 0 0 0 0 NaN NaN + sexfemale 0 0 0 0 0 NaN NaN + age 0 0 0 0 0 NaN NaN + abs(age - copper) 0 0 0 0 0 NaN NaN + log(trig) 0 0 0 0 0 NaN NaN + + + diff --git a/tests/testthat/test-clm.R b/tests/testthat/test-clm.R index 27e03b1b..ddf20c65 100644 --- a/tests/testthat/test-clm.R +++ b/tests/testthat/test-clm.R @@ -1,8 +1,7 @@ library("JointAI") skip_on_cran() - -Sys.setenv(IS_CHECK = "true") +# Sys.setenv(IS_CHECK = "true") run_clm_models <- function() { sink(tempfile()) @@ -124,19 +123,18 @@ test_that("MCMC samples can be plottet", { test_that("data_list remains the same", { # skip_on_cran() - print_output(lapply(models, "[[", "data_list"), type = "value", - context = "clm") + expect_snapshot(lapply(models, "[[", "data_list")) }) test_that("jagsmodel remains the same", { - print_output(lapply(models, "[[", "jagsmodel"), context = "clm") + expect_snapshot(lapply(models, "[[", "jagsmodel")) }) test_that("GRcrit and MCerror give same result", { # skip_on_cran() - print_output(lapply(models0, GR_crit, multivariate = FALSE), context = "clm") - print_output(lapply(models0, MC_error), context = "clm") + expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE)) + expect_snapshot(lapply(models0, MC_error)) }) @@ -144,32 +142,31 @@ test_that("summary output remained the same on Windows", { # skip_on_cran() skip_on_os(c("mac", "linux", "solaris")) - print_output(lapply(models0, print), context = "clm") - print_output(lapply(models0, coef), context = "clm") - print_output(lapply(models0, confint), context = "clm") - print_output(lapply(models0, summary), context = "clm") - print_output(lapply(models0, function(x) coef(summary(x))), context = "clm") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) test_that("summary output remained the same on non-Windows", { # skip_on_cran() skip_on_os(c("windows")) - print_output(lapply(models0, print), extra = "nonWin", context = "clm") - print_output(lapply(models0, coef), extra = "nonWin", context = "clm") - print_output(lapply(models0, confint), extra = "nonWin", context = "clm") - print_output(lapply(models0, summary), extra = "nonWin", context = "clm") - print_output(lapply(models0, function(x) coef(summary(x))), extra = "nonWin", - context = "clm") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) test_that("prediction works", { + expect_equal(class(predict(models$m4a, type = "lp", warn = FALSE)$fitted), + "array") + expect_equal(class(predict(models$m4a, type = "prob", warn = FALSE)$fitted), + "array") - local_edition(2) - expect_is(predict(models$m4a, type = "lp", warn = FALSE)$fitted, "array") - expect_is(predict(models$m4a, type = "prob", warn = FALSE)$fitted, "array") - local_edition(3) expect_s3_class(predict(models$m4a, type = "class", warn = FALSE)$fitted, "data.frame") expect_s3_class(predict(models$m4a, type = "response", warn = FALSE)$fitted, @@ -185,11 +182,11 @@ test_that("prediction works", { "data.frame") - local_edition(2) - expect_is(predict(models$m5d, type = "lp", warn = FALSE)$fitted, "array") - expect_is(predict(models$m5d, type = "prob", warn = FALSE)$fitted, "array") + expect_equal(class(predict(models$m5d, type = "lp", warn = FALSE)$fitted), + "array") + expect_equal(class(predict(models$m5d, type = "prob", warn = FALSE)$fitted), + "array") - local_edition(3) expect_s3_class(predict(models$m5d, type = "class", warn = FALSE)$fitted, "data.frame") expect_s3_class(predict(models$m5d, type = "response", warn = FALSE)$fitted, @@ -252,4 +249,4 @@ test_that("wrong models give errors", { expect_error(clm_imp(O2 ~ O1 + C1, data = wideDF, nonprop = list(O2 = ~ C2), warn = FALSE)) }) -Sys.setenv(IS_CHECK = "") +# Sys.setenv(IS_CHECK = "") diff --git a/tests/testthat/test-clmm.R b/tests/testthat/test-clmm.R index 46ffd5e4..a4513abb 100644 --- a/tests/testthat/test-clmm.R +++ b/tests/testthat/test-clmm.R @@ -1,7 +1,7 @@ library("JointAI") -Sys.setenv(IS_CHECK = "true") - +# Sys.setenv(IS_CHECK = "true") +skip_on_cran() if (identical(Sys.getenv("NOT_CRAN"), "true")) { run_clmm_models <- function() { @@ -179,63 +179,49 @@ test_that("MCMC samples can be plotted", { test_that("data_list remains the same", { skip_on_cran() - print_output(lapply(models, "[[", "data_list"), type = "value", - context = "clmm") + expect_snapshot(lapply(models, "[[", "data_list")) }) test_that("jagsmodel remains the same", { skip_on_cran() - print_output(lapply(models, "[[", "jagsmodel"), - context = "clmm") + expect_snapshot(lapply(models, "[[", "jagsmodel")) }) test_that("GRcrit and MCerror give same result", { skip_on_cran() - print_output(lapply(models0, GR_crit, multivariate = FALSE), - context = "clmm") - print_output(lapply(models0, MC_error), - context = "clmm") + expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE)) + expect_snapshot(lapply(models0, MC_error)) }) test_that("summary output remained the same on Windows", { skip_on_cran() skip_on_os(c("mac", "linux", "solaris")) - print_output(lapply(models0, print), - context = "clmm") - print_output(lapply(models0, coef), - context = "clmm") - print_output(lapply(models0, confint), - context = "clmm") - print_output(lapply(models0, summary), - context = "clmm") - print_output(lapply(models0, function(x) coef(summary(x))), - context = "clmm") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) test_that("summary output remained the same on non-Windows", { skip_on_cran() skip_on_os(c("windows")) - print_output(lapply(models0, print), extra = "nonWin", - context = "clmm") - print_output(lapply(models0, coef), extra = "nonWin", - context = "clmm") - print_output(lapply(models0, confint), extra = "nonWin", - context = "clmm") - print_output(lapply(models0, summary), extra = "nonWin", - context = "clmm") - print_output(lapply(models0, function(x) coef(summary(x))), - extra = "nonWin", context = "clmm") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) test_that("prediction works", { - local_edition(2) - expect_is(predict(models$m4a, type = "lp", warn = FALSE)$fitted, "array") - expect_is(predict(models$m4a, type = "prob", warn = FALSE)$fitted, "array") + expect_equal(class(predict(models$m4a, type = "lp", warn = FALSE)$fitted), + "array") + expect_equal(class(predict(models$m4a, type = "prob", warn = FALSE)$fitted), + "array") - local_edition(3) expect_s3_class(predict(models$m4a, type = "class", warn = FALSE)$fitted, "data.frame") expect_s3_class(predict(models$m4a, type = "response", warn = FALSE)$fitted, @@ -250,11 +236,11 @@ test_that("prediction works", { expect_s3_class(predict(models$m4a, type = "response", warn = FALSE)$newdata, "data.frame") - local_edition(2) - expect_is(predict(models$m5d, type = "lp", warn = FALSE)$fitted, "array") - expect_is(predict(models$m5d, type = "prob", warn = FALSE)$fitted, "array") + expect_equal(class(predict(models$m5d, type = "lp", warn = FALSE)$fitted), + "array") + expect_equal(class(predict(models$m5d, type = "prob", warn = FALSE)$fitted), + "array") - local_edition(3) expect_s3_class(predict(models$m5d, type = "class", warn = FALSE)$fitted, "data.frame") expect_s3_class(predict(models$m5d, type = "response", warn = FALSE)$fitted, @@ -326,4 +312,4 @@ test_that("model can be plottet", { }) } -Sys.setenv(IS_CHECK = "") +# Sys.setenv(IS_CHECK = "") diff --git a/tests/testthat/test-coxph.R b/tests/testthat/test-coxph.R index 1fc8d18e..c1f19212 100644 --- a/tests/testthat/test-coxph.R +++ b/tests/testthat/test-coxph.R @@ -1,6 +1,8 @@ library("JointAI") -Sys.setenv(IS_CHECK = "true") +# Sys.setenv(IS_CHECK = "true") + +skip_on_cran() if (identical(Sys.getenv("NOT_CRAN"), "true")) { @@ -140,31 +142,28 @@ if (identical(Sys.getenv("NOT_CRAN"), "true")) { # test_that("data_list remains the same", { # skip_on_cran() - # print_output(lapply(models, "[[", "data_list"), type = "value") + # expect_snapshot(lapply(models, "[[", "data_list")) # }) test_that("jagsmodel remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "jagsmodel"), context = "coxph") + expect_snapshot(lapply(models, "[[", "jagsmodel")) }) test_that("GRcrit and MCerror give same result", { - skip_on_cran() - print_output(lapply(models0, GR_crit, multivariate = FALSE), context = "coxph") - print_output(lapply(models0, MC_error), context = "coxph") + expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE)) + expect_snapshot(lapply(models0, MC_error)) }) test_that("summary output remained the same", { skip_on_cran() - print_output(lapply(models0, print), context = "coxph") - print_output(lapply(models0, coef), context = "coxph") - print_output(lapply(models0, confint), context = "coxph") - print_output(lapply(models0, summary), context = "coxph") - print_output(lapply(models0, function(x) coef(summary(x))), - context = "coxph") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) @@ -268,4 +267,4 @@ if (identical(Sys.getenv("NOT_CRAN"), "true")) { }) } -Sys.setenv(IS_CHECK = "") +# Sys.setenv(IS_CHECK = "") diff --git a/tests/testthat/test-glm.R b/tests/testthat/test-glm.R index 69ecc9d9..2b030f1e 100644 --- a/tests/testthat/test-glm.R +++ b/tests/testthat/test-glm.R @@ -1,7 +1,9 @@ library("JointAI") -Sys.setenv(IS_CHECK = "true") +# Sys.setenv(IS_CHECK = "true") +skip_on_cran() + if (identical(Sys.getenv("NOT_CRAN"), "true")) { set_seed(1234) wideDF <- JointAI::wideDF @@ -299,30 +301,28 @@ if (identical(Sys.getenv("NOT_CRAN"), "true")) { }) test_that("data_list remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "data_list"), type = "value", - context = "glm") + expect_snapshot(lapply(models, "[[", "data_list")) }) test_that("jagsmodel remains the same", { skip_on_cran() - print_output(lapply(models, "[[", "jagsmodel"), context = "glm") + expect_snapshot(lapply(models, "[[", "jagsmodel")) }) test_that("GRcrit and MCerror give same result", { skip_on_cran() - print_output(lapply(models0, GR_crit, multivariate = FALSE), context = "glm") - print_output(lapply(models0, MC_error), context = "glm") + expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE)) + expect_snapshot(lapply(models0, MC_error)) }) test_that("summary output remained the same", { skip_on_cran() - print_output(lapply(models0, print), context = "glm") - print_output(lapply(models0, coef), context = "glm") - print_output(lapply(models0, confint), context = "glm") - print_output(lapply(models0, summary, missinfo = TRUE), context = "glm") - print_output(lapply(models0, function(x) coef(summary(x))), context = "glm") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary, missinfo = TRUE)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) @@ -405,4 +405,4 @@ if (identical(Sys.getenv("NOT_CRAN"), "true")) { warn = FALSE, mess = FALSE)) }) } -Sys.setenv(IS_CHECK = "") +# Sys.setenv(IS_CHECK = "") diff --git a/tests/testthat/test-glmm.R b/tests/testthat/test-glmm.R index 8da0b18c..e8cca55b 100644 --- a/tests/testthat/test-glmm.R +++ b/tests/testthat/test-glmm.R @@ -1,6 +1,8 @@ library("JointAI") -Sys.setenv(IS_CHECK = "true") +# Sys.setenv(IS_CHECK = "true") +skip_on_cran() + if (identical(Sys.getenv("NOT_CRAN"), "true")) { set_seed(1234) @@ -337,31 +339,25 @@ if (identical(Sys.getenv("NOT_CRAN"), "true")) { test_that("data_list remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "data_list"), type = "value", - context = "glmm") + expect_snapshot(lapply(models, "[[", "data_list")) }) test_that("jagsmodel remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "jagsmodel"), context = "glmm") + expect_snapshot(lapply(models, "[[", "jagsmodel")) }) test_that("GRcrit and MCerror give same result", { - skip_on_cran() - print_output(lapply(models0, GR_crit, multivariate = FALSE), context = "glmm") - print_output(lapply(models0, MC_error), context = "glmm") + expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE)) + expect_snapshot(lapply(models0, MC_error)) }) test_that("summary output remained the same", { - skip_on_cran() - print_output(lapply(models0, print), context = "glmm") - print_output(lapply(models0, coef), context = "glmm") - print_output(lapply(models0, confint), context = "glmm") - print_output(lapply(models0, summary, missinfo = TRUE), context = "glmm") - print_output(lapply(models0, function(x) coef(summary(x))), - context = "glmm") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary, missinfo = TRUE)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) @@ -443,4 +439,4 @@ if (identical(Sys.getenv("NOT_CRAN"), "true")) { # models = c(Be2 = "betareg"))) # }) -Sys.setenv(IS_CHECK = "") +# Sys.setenv(IS_CHECK = "") diff --git a/tests/testthat/test-mlogit.R b/tests/testthat/test-mlogit.R index 1142a1a2..8a208d70 100644 --- a/tests/testthat/test-mlogit.R +++ b/tests/testthat/test-mlogit.R @@ -1,6 +1,6 @@ library("JointAI") -Sys.setenv(IS_CHECK = "true") +# Sys.setenv(IS_CHECK = "true") skip_on_cran() @@ -82,41 +82,36 @@ test_that("MCMC samples can be plottet", { test_that("data_list remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "data_list"), type = "value", - context = "mlogit") + expect_snapshot(lapply(models, "[[", "data_list")) }) test_that("jagsmodel remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "jagsmodel"), context = "mlogit") + expect_snapshot(lapply(models, "[[", "jagsmodel")) }) test_that("GRcrit and MCerror give same result", { - skip_on_cran() - print_output(lapply(models0, GR_crit, multivariate = FALSE), context = "mlogit") - print_output(lapply(models0, MC_error), context = "mlogit") + expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE)) + expect_snapshot(lapply(models0, MC_error)) }) test_that("summary output remained the same", { skip_on_cran() - print_output(lapply(models0, print), context = "mlogit") - print_output(lapply(models0, coef), context = "mlogit") - print_output(lapply(models0, confint), context = "mlogit") - print_output(lapply(models0, summary), context = "mlogit") - print_output(lapply(models0, function(x) coef(summary(x))), - context = "mlogit") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) test_that("prediction works", { - local_edition(2) - expect_is(predict(models$m4a, type = "lp", warn = FALSE)$fitted, "array") - expect_is(predict(models$m4a, type = "prob", warn = FALSE)$fitted, "array") + expect_equal(class(predict(models$m4a, type = "lp", warn = FALSE)$fitted), + "array") + expect_equal(class(predict(models$m4a, type = "prob", warn = FALSE)$fitted), + "array") - local_edition(3) expect_s3_class(predict(models$m4a, type = "class", warn = FALSE)$fitted, "data.frame") expect_s3_class(predict(models$m4a, type = "response", warn = FALSE)$fitted, @@ -131,11 +126,11 @@ test_that("prediction works", { expect_s3_class(predict(models$m4a, type = "response", warn = FALSE)$newdata, "data.frame") - local_edition(2) - expect_is(predict(models$m4b, type = "lp", warn = FALSE)$fitted, "array") - expect_is(predict(models$m4b, type = "prob", warn = FALSE)$fitted, "array") + expect_equal(class(predict(models$m4b, type = "lp", warn = FALSE)$fitted), + "array") + expect_equal(class(predict(models$m4b, type = "prob", warn = FALSE)$fitted), + "array") - local_edition(3) expect_s3_class(predict(models$m4b, type = "class", warn = FALSE)$fitted, "data.frame") expect_s3_class(predict(models$m4b, type = "response", warn = FALSE)$fitted, @@ -182,4 +177,4 @@ test_that("wrong models give errors", { expect_error(mlogit_imp(M1 ~ O1 + C1 + C2 + (1 | id), data = longDF)) expect_error(mlogit_imp(M1 ~ O1 + C1 + C2 + (1 | id), data = wideDF)) }) -Sys.setenv(IS_CHECK = "") +# Sys.setenv(IS_CHECK = "") diff --git a/tests/testthat/test-mlogitmm.R b/tests/testthat/test-mlogitmm.R index 4fd9881b..206ad52a 100644 --- a/tests/testthat/test-mlogitmm.R +++ b/tests/testthat/test-mlogitmm.R @@ -1,6 +1,6 @@ library("JointAI") -Sys.setenv(IS_CHECK = "true") +# Sys.setenv(IS_CHECK = "true") skip_on_cran() @@ -123,32 +123,25 @@ test_that("MCMC samples can be plottet", { }) test_that("data_list remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "data_list"), type = "value", - context = "mlogitmm") + expect_snapshot(lapply(models, "[[", "data_list")) }) test_that("jagsmodel remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "jagsmodel"), context = "mlogitmm") + expect_snapshot(lapply(models, "[[", "jagsmodel")) }) test_that("GRcrit and MCerror give same result", { - skip_on_cran() - print_output(lapply(models0, GR_crit, multivariate = FALSE), - context = "mlogitmm") - print_output(lapply(models0, MC_error), context = "mlogitmm") + expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE)) + expect_snapshot(lapply(models0, MC_error)) }) test_that("summary output remained the same", { - skip_on_cran() - print_output(lapply(models0, print), context = "mlogitmm") - print_output(lapply(models0, coef), context = "mlogitmm") - print_output(lapply(models0, confint), context = "mlogitmm") - print_output(lapply(models0, summary), context = "mlogitmm") - print_output(lapply(models0, function(x) coef(summary(x))), - context = "mlogitmm") + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) @@ -165,11 +158,11 @@ test_that("prediction works", { ) - local_edition(2) - expect_is(predict(models$m4a, type = "lp", warn = FALSE)$fitted, "array") - expect_is(predict(models$m4a, type = "prob", warn = FALSE)$fitted, "array") + expect_equal(class(predict(models$m4a, type = "lp", warn = FALSE)$fitted), + "array") + expect_equal(class(predict(models$m4a, type = "prob", warn = FALSE)$fitted), + "array") - local_edition(3) expect_s3_class(predict(models$m4a, type = "class", warn = FALSE)$fitted, "data.frame") expect_s3_class(predict(models$m4a, type = "response", warn = FALSE)$fitted, @@ -184,15 +177,15 @@ test_that("prediction works", { expect_s3_class(predict(models$m4a, type = "response", warn = FALSE)$newdata, "data.frame") - local_edition(2) - expect_is(predict(models$m4e, type = "lp", warn = FALSE)$fitted, "array") - expect_is(predict(models$m4e, type = "prob", warn = FALSE)$fitted, "array") - expect_is(predict(models$m4e, type = "class", warn = FALSE)$fitted, - "data.frame") - expect_is(predict(models$m4e, type = "response", warn = FALSE)$fitted, - "data.frame") + expect_equal(class(predict(models$m4e, type = "lp", warn = FALSE)$fitted), + "array") + expect_equal(class(predict(models$m4e, type = "prob", warn = FALSE)$fitted), + "array") + expect_s3_class(predict(models$m4e, type = "class", warn = FALSE)$fitted, + "data.frame") + expect_s3_class(predict(models$m4e, type = "response", warn = FALSE)$fitted, + "data.frame") - local_edition(3) expect_s3_class(predict(models$m4b, type = "lp", warn = FALSE)$newdata, "data.frame") expect_s3_class(predict(models$m4b, type = "prob", warn = FALSE)$newdata, @@ -241,4 +234,4 @@ test_that("wrong models give errors", { data = longDF), "JointAI_errored") }) -Sys.setenv(IS_CHECK = "") +# Sys.setenv(IS_CHECK = "") diff --git a/tests/testthat/test-survreg.R b/tests/testthat/test-survreg.R index 94f951a4..3f25ca19 100644 --- a/tests/testthat/test-survreg.R +++ b/tests/testthat/test-survreg.R @@ -1,6 +1,6 @@ library("JointAI") -Sys.setenv(IS_CHECK = "true") +# Sys.setenv(IS_CHECK = "true") skip_on_cran() @@ -91,33 +91,27 @@ test_that("MCMC samples can be plottet", { test_that("data_list remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "data_list"), type = "value", - context = "survreg") + expect_snapshot(lapply(models, "[[", "data_list")) }) test_that("jagsmodel remains the same", { - skip_on_cran() - print_output(lapply(models, "[[", "jagsmodel"), context = "survreg") + expect_snapshot(lapply(models, "[[", "jagsmodel")) }) test_that("GRcrit and MCerror give same result", { - skip_on_cran() - print_output(lapply(models0, GR_crit, multivariate = FALSE), - context = "survreg") - print_output(lapply(models0, MC_error), context = "survreg") + expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE)) + expect_snapshot(lapply(models0, MC_error)) }) test_that("summary output remained the same", { - skip_on_cran() - print_output(lapply(models0, print), context = "survreg") - print_output(lapply(models0, coef), context = "survreg") - print_output(lapply(models0, confint), context = "survreg") - print_output(lapply(models0, summary), context = "survreg") - print_output(lapply(models0, function(x) coef(summary(x))), - context = "survreg") + + expect_snapshot(lapply(models0, print)) + expect_snapshot(lapply(models0, coef)) + expect_snapshot(lapply(models0, confint)) + expect_snapshot(lapply(models0, summary)) + expect_snapshot(lapply(models0, function(x) coef(summary(x)))) }) @@ -191,4 +185,4 @@ test_that("wrong models give errors", { }) -Sys.setenv(IS_CHECK = "") +# Sys.setenv(IS_CHECK = "") From dad472e997455932d582ddea786c014b922370ab Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 16:30:52 +0200 Subject: [PATCH 114/176] empty lines --- .../testout/glm_lapply.models.jagsmodel..txt | 5118 +++++------ .../testout/glmm_lapply.models.jagsmodel..txt | 8056 ++++++++--------- .../mlogit_lapply.models.jagsmodel..txt | 987 +- .../mlogitmm_lapply.models.jagsmodel..txt | 2032 ++--- 4 files changed, 7946 insertions(+), 8247 deletions(-) diff --git a/tests/testthat/testout/glm_lapply.models.jagsmodel..txt b/tests/testthat/testout/glm_lapply.models.jagsmodel..txt index 28e1c96a..631d9ef0 100644 --- a/tests/testthat/testout/glm_lapply.models.jagsmodel..txt +++ b/tests/testthat/testout/glm_lapply.models.jagsmodel..txt @@ -1,2697 +1,2525 @@ $m0a1 -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } $m0a2 -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } $m0a3 -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - log(mu_y[i]) <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + log(mu_y[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } $m0a4 -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) - inv_mu_y[i] <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) + inv_mu_y[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } $m0b1 -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for B1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for B1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m0b2 -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - probit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for B1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + probit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for B1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m0b3 -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - log(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for B1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + log(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for B1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m0b4 -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - cloglog(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for B1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + cloglog(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for B1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m0c1 -model { - - - # Gamma model for L1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) - inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - -} +model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + } $m0c2 -model { - - - # Gamma model for L1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - log(mu_L1[i]) <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - -} +model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + log(mu_L1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + } $m0d1 -model { - - - # Poisson model for P1 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) - log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for P1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - -} +model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for P1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + } $m0d2 -model { - - - # Poisson model for P1 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) - mu_P1[i] <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for P1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - -} +model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + mu_P1[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for P1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + } $m0e1 -model { - - - # Log-normal model for L1 ------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) - mu_L1[i] <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1 <- sqrt(1/tau_L1) - - -} +model { + + # Log-normal model for L1 ------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + } $m0f1 -model { - - - # Beta model for Be1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) - - shape1_Be1[i] <- mu_Be1[i] * tau_Be1 - shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 - - logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1] - } - - # Priors for the model for Be1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - -} +model { + + # Beta model for Be1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1] + } + + # Priors for the model for Be1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + } $m1a -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - } - - # Priors for the model for y - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for y + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + } $m1b -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - } - - # Priors for the model for B1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for B1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m1c -model { - - - # Gamma model for L1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) - inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - } - - # Priors for the model for L1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - -} +model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for L1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + } $m1d -model { - - - # Poisson model for P1 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) - log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - } - - # Priors for the model for P1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - -} +model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for P1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + } $m1e -model { - - - # Log-normal model for L1 ------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) - mu_L1[i] <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - } - - # Priors for the model for L1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1 <- sqrt(1/tau_L1) - - -} +model { + + # Log-normal model for L1 ------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for L1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + } $m1f -model { - - - # Beta model for Be1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) - - shape1_Be1[i] <- mu_Be1[i] * tau_Be1 - shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 - - logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - } - - # Priors for the model for Be1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - -} +model { + + # Beta model for Be1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + } + + # Priors for the model for Be1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + } $m2a -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - } - - # Priors for the model for y - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for y + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2b -model { - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - } - - # Priors for the model for B2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for B2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2c -model { - - - # Gamma model for L1mis --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - } - - # Priors for the model for L1mis - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for L1mis + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2d -model { - - - # Poisson model for P2 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P2[i])) - log(mu_P2[i]) <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - } - - # Priors for the model for P2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P2[i])) + log(mu_P2[i]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for P2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2e -model { - - - # Log-normal model for L1mis ---------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) - mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - } - - # Priors for the model for L1mis - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1mis <- sqrt(1/tau_L1mis) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Log-normal model for L1mis ---------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for L1mis + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2f -model { - - - # Beta model for Be2 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - } - - # Priors for the model for Be2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Beta model for Be2 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + } + + # Priors for the model for Be2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3a -model { - - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 7] * beta[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + - M_lvlone[i, 8] * beta[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6] - } - - # Priors for the model for C1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5] - - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Poisson model for P2 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) - log(mu_P2[i]) <- M_lvlone[i, 7] * alpha[6] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] - } - - # Priors for the model for P2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - - - - # Gamma model for L1mis --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- M_lvlone[i, 7] * alpha[10] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12] - } - - # Priors for the model for L1mis - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - - - - # Beta model for Be2 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- M_lvlone[i, 7] * alpha[13] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14] - } - - # Priors for the model for Be2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 7] * alpha[15] - } - - # Priors for the model for C2 - for (k in 15:15) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 7] * beta[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + + M_lvlone[i, 8] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6] + } + + # Priors for the model for C1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5] + + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) + log(mu_P2[i]) <- M_lvlone[i, 7] * alpha[6] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] + } + + # Priors for the model for P2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- M_lvlone[i, 7] * alpha[10] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12] + } + + # Priors for the model for L1mis + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Beta model for Be2 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- M_lvlone[i, 7] * alpha[13] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14] + } + + # Priors for the model for Be2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 7] * alpha[15] + } + + # Priors for the model for C2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3b -model { - - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 6] * beta[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + - M_lvlone[i, 7] * beta[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] - } - - # Priors for the model for C1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - probit(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Poisson model for P2 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) - mu_P2[i] <- M_lvlone[i, 6] * alpha[5] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] - } - - # Priors for the model for P2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - - - - # Log-normal model for L1mis ---------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dlnorm(mu_L1mis[i], tau_L1mis) - mu_L1mis[i] <- M_lvlone[i, 6] * alpha[8] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] - } - - # Priors for the model for L1mis - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1mis <- sqrt(1/tau_L1mis) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- 1/max(1e-10, inv_mu_C2[i]) - inv_mu_C2[i] <- M_lvlone[i, 6] * alpha[10] - } - - # Priors for the model for C2 - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 6] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 7] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + } + + # Priors for the model for C1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + probit(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) + mu_P2[i] <- M_lvlone[i, 6] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] + } + + # Priors for the model for P2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Log-normal model for L1mis ---------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- M_lvlone[i, 6] * alpha[8] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] + } + + # Priors for the model for L1mis + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- 1/max(1e-10, inv_mu_C2[i]) + inv_mu_C2[i] <- M_lvlone[i, 6] * alpha[10] + } + + # Priors for the model for C2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3c -model { - - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 6] * beta[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + - M_lvlone[i, 7] * beta[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] - } - - # Priors for the model for C1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - log(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Poisson model for P2 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) - mu_P2[i] <- M_lvlone[i, 6] * alpha[5] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] - } - - # Priors for the model for P2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - - - - # Gamma model for L1mis --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - log(mu_L1mis[i]) <- M_lvlone[i, 6] * alpha[8] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] - } - - # Priors for the model for L1mis - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2) - log(mu_C2[i]) <- M_lvlone[i, 6] * alpha[10] - } - - # Priors for the model for C2 - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 6] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 7] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + } + + # Priors for the model for C1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + log(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) + mu_P2[i] <- M_lvlone[i, 6] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] + } + + # Priors for the model for P2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + log(mu_L1mis[i]) <- M_lvlone[i, 6] * alpha[8] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] + } + + # Priors for the model for L1mis + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2) + log(mu_C2[i]) <- M_lvlone[i, 6] * alpha[10] + } + + # Priors for the model for C2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3d -model { - - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 7] * beta[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + - M_lvlone[i, 8] * beta[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6] - } - - # Priors for the model for C1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - log(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5] - - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Poisson model for P2 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) - mu_P2[i] <- M_lvlone[i, 7] * alpha[6] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] - } - - # Priors for the model for P2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - - - - # Gamma model for L1mis --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - log(mu_L1mis[i]) <- M_lvlone[i, 7] * alpha[10] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12] - } - - # Priors for the model for L1mis - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - - - - # Normal model for Be2 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 5] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1) - mu_Be2[i] <- M_lvlone[i, 7] * alpha[13] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14] - } - - # Priors for the model for Be2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_Be2 <- sqrt(1/tau_Be2) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2) - log(mu_C2[i]) <- M_lvlone[i, 7] * alpha[15] - } - - # Priors for the model for C2 - for (k in 15:15) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 7] * beta[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + + M_lvlone[i, 8] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6] + } + + # Priors for the model for C1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + log(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5] + + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Poisson model for P2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i])) + mu_P2[i] <- M_lvlone[i, 7] * alpha[6] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9] + } + + # Priors for the model for P2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + log(mu_L1mis[i]) <- M_lvlone[i, 7] * alpha[10] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12] + } + + # Priors for the model for L1mis + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Normal model for Be2 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 5] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1) + mu_Be2[i] <- M_lvlone[i, 7] * alpha[13] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14] + } + + # Priors for the model for Be2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_Be2 <- sqrt(1/tau_Be2) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2) + log(mu_C2[i]) <- M_lvlone[i, 7] * alpha[15] + } + + # Priors for the model for C2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m4a -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + - M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + - M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + - M_lvlone[i, 11] * beta[7] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] - } - - # Priors for the model for y - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] - - M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] - M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] - M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] - } - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] + } + + # Priors for the model for y + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } $m4b -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - logit(mu_B1[i]) <- M_lvlone[i, 5] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] - } - - # Priors for the model for B1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Gamma model for L1mis --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[2] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] - } - - # Priors for the model for L1mis - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - - - - # Beta model for Be2 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- M_lvlone[i, 5] * alpha[5] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[6] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] - - M_lvlone[i, 7] <- log(M_lvlone[i, 3]) - - - } - - # Priors for the model for Be2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dnorm(mu_C2[i], tau_C2) - log(mu_C2[i]) <- M_lvlone[i, 5] * alpha[8] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[9] - - M_lvlone[i, 6] <- abs(M_lvlone[i, 8] - M_lvlone[i, 4]) - - - } - - # Priors for the model for C2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 5] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + } + + # Priors for the model for B1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Gamma model for L1mis --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- M_lvlone[i, 5] * alpha[1] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[2] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] + } + + # Priors for the model for L1mis + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + + + # Beta model for Be2 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- M_lvlone[i, 5] * alpha[5] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[6] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7] + + M_lvlone[i, 7] <- log(M_lvlone[i, 3]) + + + } + + # Priors for the model for Be2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dnorm(mu_C2[i], tau_C2) + log(mu_C2[i]) <- M_lvlone[i, 5] * alpha[8] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[9] + + M_lvlone[i, 6] <- abs(M_lvlone[i, 8] - M_lvlone[i, 4]) + + + } + + # Priors for the model for C2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5a1 -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5a2 -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - log(mu_y[i]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + log(mu_y[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5a3 -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) - inv_mu_y[i] <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) + inv_mu_y[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5b1 -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - logit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for B1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + - M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + - M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for B1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + + M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5b2 -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - probit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for B1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + - M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + - M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + probit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for B1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + + M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5b3 -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - log(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for B1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + - M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + - M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + log(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for B1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + + M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5b4 -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - cloglog(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for B1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + - M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + - M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + cloglog(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for B1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] + + M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] + + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5c1 -model { - - - # Gamma model for L1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) - inv_mu_L1[i] <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for L1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for L1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5c2 -model { - - - # Gamma model for L1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - log(mu_L1[i]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for L1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Gamma model for L1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + log(mu_L1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for L1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5d1 -model { - - - # Poisson model for P1 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) - log(mu_P1[i]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for P1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + log(mu_P1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for P1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5d2 -model { - - - # Poisson model for P1 ---------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) - mu_P1[i] <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for P1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Poisson model for P1 ---------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i])) + mu_P1[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for P1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5e1 -model { - - - # Log-normal model for L1 ------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) - mu_L1[i] <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for L1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1 <- sqrt(1/tau_L1) - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Log-normal model for L1 ------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for L1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m5f1 -model { - - - # Beta model for Be1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) - - shape1_Be1[i] <- mu_Be1[i] * tau_Be1 - shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 - - logit(mu_Be1[i]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + - M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + - M_lvlone[i, 9] * beta[7] - } - - # Priors for the model for Be1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - - - - # Binomial model for B2 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) - logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + - M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 1:6) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + - M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + - M_lvlone[i, 9] * alpha[11] - } - - # Priors for the model for C2 - for (k in 7:11) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Beta model for Be1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + + M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] + + M_lvlone[i, 9] * beta[7] + } + + # Priors for the model for Be1 + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + + + # Binomial model for B2 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i]))) + logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] + + M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 1:6) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] + + M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] + + M_lvlone[i, 9] * alpha[11] + } + + # Priors for the model for C2 + for (k in 7:11) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m6a -model { - - - # Normal model for y ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + - M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + - M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + - M_lvlone[i, 11] * beta[7] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] - } - - # Priors for the model for y - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] - - M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] - M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] - M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] - } - - -} +model { + + # Normal model for y ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] + } + + # Priors for the model for y + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } $m6b -model { - - - # Binomial model for B1 --------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) - logit(mu_B1[i]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + - M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + - M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + - M_lvlone[i, 11] * beta[7] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] - } - - # Priors for the model for B1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] - - M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] - M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] - M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] - } - - -} +model { + + # Binomial model for B1 --------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i]))) + logit(mu_B1[i]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] + } + + # Priors for the model for B1 + for (k in 1:12) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } $m6c -model { - - - # Gamma model for C1 ------------------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dgamma(shape_C1[i], rate_C1[i]) - - shape_C1[i] <- pow(mu_C1[i], 2) / pow(sigma_C1, 2) - rate_C1[i] <- mu_C1[i] / pow(sigma_C1, 2) - - log(mu_C1[i]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + - M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + - M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + - M_lvlone[i, 11] * beta[7] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] - } - - # Priors for the model for C1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_C1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_C1 <- sqrt(1/tau_C1) - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[8] - - M_lvlone[i, 12] <- abs(M_lvlone[i, 16] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[23] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 9] * M_lvlone[i, 12] - M_lvlone[i, 14] <- M_lvlone[i, 10] * M_lvlone[i, 12] - M_lvlone[i, 15] <- M_lvlone[i, 11] * M_lvlone[i, 12] - } - - -} +model { + + # Gamma model for C1 ------------------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dgamma(shape_C1[i], rate_C1[i]) + + shape_C1[i] <- pow(mu_C1[i], 2) / pow(sigma_C1, 2) + rate_C1[i] <- mu_C1[i] / pow(sigma_C1, 2) + + log(mu_C1[i]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + } + + # Priors for the model for C1 + for (k in 1:11) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_C1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 16] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 13] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 14] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } $m6d -model { - - - # Normal model for SBP ---------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) - mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + - M_lvlone[i, 6] * beta[3] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] - } - - # Priors for the model for SBP - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_SBP <- sqrt(1/tau_SBP) - - - - - # Normal model for bili --------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili)T(1e-05, 1e+10) - mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] - - M_lvlone[i, 7] <- log(M_lvlone[i, 2]) - - - } - - # Priors for the model for bili - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_bili <- sqrt(1/tau_bili) - - - - - # Normal model for creat -------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) - mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + - M_lvlone[i, 6] * alpha[7] - - M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) - - - } - - # Priors for the model for creat - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_creat <- sqrt(1/tau_creat) - - -} +model { + + # Normal model for SBP ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) + mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 6] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + } + + # Priors for the model for SBP + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_SBP <- sqrt(1/tau_SBP) + + + + # Normal model for bili --------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili)T(1e-05, 1e+10) + mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] + + M_lvlone[i, 7] <- log(M_lvlone[i, 2]) + + + } + + # Priors for the model for bili + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_bili <- sqrt(1/tau_bili) + + + + # Normal model for creat -------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) + mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + M_lvlone[i, 6] * alpha[7] + + M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) + + + } + + # Priors for the model for creat + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_creat <- sqrt(1/tau_creat) + + } $m6e -model { - - - # Normal model for SBP ---------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) - mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + - M_lvlone[i, 6] * beta[3] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] - } - - # Priors for the model for SBP - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_SBP <- sqrt(1/tau_SBP) - - - - - # Log-normal model for bili ----------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 2] ~ dlnorm(mu_bili[i], tau_bili) - mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] - - M_lvlone[i, 7] <- log(M_lvlone[i, 2]) - - - } - - # Priors for the model for bili - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_bili <- sqrt(1/tau_bili) - - - - - # Normal model for creat -------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) - mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + - M_lvlone[i, 6] * alpha[7] - - M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) - - - } - - # Priors for the model for creat - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_creat <- sqrt(1/tau_creat) - - -} +model { + + # Normal model for SBP ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) + mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 6] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + } + + # Priors for the model for SBP + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_SBP <- sqrt(1/tau_SBP) + + + + # Log-normal model for bili ----------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 2] ~ dlnorm(mu_bili[i], tau_bili) + mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] + + M_lvlone[i, 7] <- log(M_lvlone[i, 2]) + + + } + + # Priors for the model for bili + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_bili <- sqrt(1/tau_bili) + + + + # Normal model for creat -------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) + mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + M_lvlone[i, 6] * alpha[7] + + M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) + + + } + + # Priors for the model for creat + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_creat <- sqrt(1/tau_creat) + + } $m6f -model { - - - # Normal model for SBP ---------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) - mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + - M_lvlone[i, 6] * beta[3] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] - } - - # Priors for the model for SBP - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_SBP <- sqrt(1/tau_SBP) - - - - - # Gamma model for bili ---------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 2] ~ dgamma(shape_bili[i], rate_bili[i]) - - shape_bili[i] <- pow(mu_bili[i], 2) / pow(sigma_bili, 2) - rate_bili[i] <- mu_bili[i] / pow(sigma_bili, 2) - - mu_bili[i] <- 1/max(1e-10, inv_mu_bili[i]) - inv_mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] - - M_lvlone[i, 7] <- log(M_lvlone[i, 2]) - - - } - - # Priors for the model for bili - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_bili ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_bili <- sqrt(1/tau_bili) - - - - - # Normal model for creat -------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) - mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + - M_lvlone[i, 6] * alpha[7] - - M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) - - - } - - # Priors for the model for creat - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_creat <- sqrt(1/tau_creat) - - -} +model { + + # Normal model for SBP ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) + mu_SBP[i] <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + M_lvlone[i, 6] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + } + + # Priors for the model for SBP + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_SBP <- sqrt(1/tau_SBP) + + + + # Gamma model for bili ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 2] ~ dgamma(shape_bili[i], rate_bili[i]) + + shape_bili[i] <- pow(mu_bili[i], 2) / pow(sigma_bili, 2) + rate_bili[i] <- mu_bili[i] / pow(sigma_bili, 2) + + mu_bili[i] <- 1/max(1e-10, inv_mu_bili[i]) + inv_mu_bili[i] <- M_lvlone[i, 4] * alpha[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4] + + M_lvlone[i, 7] <- log(M_lvlone[i, 2]) + + + } + + # Priors for the model for bili + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_bili ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_bili <- sqrt(1/tau_bili) + + + + # Normal model for creat -------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat) + mu_creat[i] <- M_lvlone[i, 4] * alpha[5] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] + + M_lvlone[i, 6] * alpha[7] + + M_lvlone[i, 8] <- exp(M_lvlone[i, 3]) + + + } + + # Priors for the model for creat + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_creat <- sqrt(1/tau_creat) + + } $mod7a -model { - - - # Normal model for SBP ---------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) - mu_SBP[i] <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[3] + - M_lvlone[i, 6] * beta[4] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[5] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[6] - } - - # Priors for the model for SBP - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_SBP <- sqrt(1/tau_SBP) - - - - - # Normal model for bili --------------------------------------------------------- - for (i in 1:186) { - M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili) - mu_bili[i] <- M_lvlone[i, 3] * alpha[1] + - (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] - - M_lvlone[i, 7] <- M_lvlone[i, 2]^2 - M_lvlone[i, 8] <- M_lvlone[i, 2]^3 - - - } - - # Priors for the model for bili - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_bili <- sqrt(1/tau_bili) - - -} +model { + + # Normal model for SBP ---------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP) + mu_SBP[i] <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[3] + + M_lvlone[i, 6] * beta[4] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[5] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[6] + } + + # Priors for the model for SBP + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_SBP <- sqrt(1/tau_SBP) + + + + # Normal model for bili --------------------------------------------------------- + for (i in 1:186) { + M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili) + mu_bili[i] <- M_lvlone[i, 3] * alpha[1] + + (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * alpha[2] + + M_lvlone[i, 6] * alpha[3] + + M_lvlone[i, 7] <- M_lvlone[i, 2]^2 + M_lvlone[i, 8] <- M_lvlone[i, 2]^3 + + + } + + # Priors for the model for bili + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_bili <- sqrt(1/tau_bili) + + } diff --git a/tests/testthat/testout/glmm_lapply.models.jagsmodel..txt b/tests/testthat/testout/glmm_lapply.models.jagsmodel..txt index 0f08e644..84fbbc4e 100644 --- a/tests/testthat/testout/glmm_lapply.models.jagsmodel..txt +++ b/tests/testthat/testout/glmm_lapply.models.jagsmodel..txt @@ -1,4136 +1,4046 @@ $m0a1 -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } $m0a2 -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } $m0a3 -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - log(mu_y[i]) <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + log(mu_y[i]) <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } $m0a4 -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) - inv_mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) + inv_mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } $m0b1 -model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - -} +model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } $m0b2 -model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - probit(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - -} +model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + probit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } $m0b3 -model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - log(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - -} +model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + log(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } $m0b4 -model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - log(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - -} +model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + log(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } $m0c1 -model { - - # Gamma mixed effects model for L1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) - inv_mu_L1[i] <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - -} +model { + + # Gamma mixed effects model for L1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } $m0c2 -model { - - # Gamma mixed effects model for L1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - log(mu_L1[i]) <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - -} +model { + + # Gamma mixed effects model for L1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + log(mu_L1[i]) <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } $m0d1 -model { - - # Poisson mixed effects model for p1 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) - log(mu_p1[i]) <- b_p1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) - mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for p1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) - -} +model { + + # Poisson mixed effects model for p1 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) + log(mu_p1[i]) <- b_p1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) + mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for p1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) + } $m0d2 -model { - - # Poisson mixed effects model for p1 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) - mu_p1[i] <- b_p1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) - mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for p1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) - -} +model { + + # Poisson mixed effects model for p1 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) + mu_p1[i] <- b_p1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) + mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for p1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) + } $m0e1 -model { - - # Log-normal mixed effects model for L1 ----------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) - mu_L1[i] <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - -} +model { + + # Log-normal mixed effects model for L1 ----------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } $m0f1 -model { - - # Beta mixed effects model for Be1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) - - shape1_Be1[i] <- mu_Be1[i] * tau_Be1 - shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 - - logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ]) - mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for Be1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1]) - -} +model { + + # Beta mixed effects model for Be1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ]) + mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for Be1 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1]) + } $m1a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for y - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for y + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + } $m1b -model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for b1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - -} +model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for b1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + } $m1c -model { - - # Gamma mixed effects model for L1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) - inv_mu_L1[i] <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for L1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - -} +model { + + # Gamma mixed effects model for L1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) + + shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) + rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) + + mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) + inv_mu_L1[i] <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for L1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } $m1d -model { - - # Poisson mixed effects model for p1 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) - log(mu_p1[i]) <- b_p1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) - mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for p1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) - -} +model { + + # Poisson mixed effects model for p1 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) + log(mu_p1[i]) <- b_p1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) + mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for p1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) + } $m1e -model { - - # Log-normal mixed effects model for L1 ----------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) - mu_L1[i] <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for L1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - -} +model { + + # Log-normal mixed effects model for L1 ----------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) + mu_L1[i] <- b_L1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) + mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for L1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1 <- sqrt(1/tau_L1) + + invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) + } $m1f -model { - - # Beta mixed effects model for Be1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) - - shape1_Be1[i] <- mu_Be1[i] * tau_Be1 - shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 - - logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ]) - mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for Be1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1]) - -} +model { + + # Beta mixed effects model for Be1 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) + + shape1_Be1[i] <- mu_Be1[i] * tau_Be1 + shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 + + logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ]) + mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + } + + # Priors for the model for Be1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1]) + } $m2a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for y + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m2b -model { - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for b2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m2c -model { - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1mis - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1mis + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m2d -model { - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i])) - log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for p2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i])) + log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for p2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m2e -model { - - # Log-normal mixed effects model for L1mis -------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) - mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1mis - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Log-normal mixed effects model for L1mis -------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for L1mis + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m2f -model { - - # Beta mixed effects model for Be2 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for Be2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Beta mixed effects model for Be2 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for Be2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m3a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for y + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3b -model { - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for b2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for b2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3c -model { - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for L1mis - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for L1mis + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3d -model { - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i])) - log(mu_p2[i]) <- b_p2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for p2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i])) + log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for p2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3e -model { - - # Log-normal mixed effects model for L1mis -------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) - mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for L1mis - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Log-normal mixed effects model for L1mis -------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for L1mis + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3f -model { - - # Beta mixed effects model for Be2 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for Be2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Beta mixed effects model for Be2 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] + } + + # Priors for the model for Be2 + for (k in 1:1) { + beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m4a -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] - } - - # Priors for the model for c1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) - log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] - } - - # Priors for the model for p2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + M_id[ii, 3] * alpha[7] - } - - # Priors for the model for c2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + - alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[10] + M_id[ii, 3] * alpha[11] - } - - # Priors for the model for L1mis - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Beta mixed effects model for Be2 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[13] + M_id[ii, 3] * alpha[14] - } - - # Priors for the model for Be2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[15] - - M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 15:15) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + } + + # Priors for the model for c1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) + log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + } + + # Priors for the model for p2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + M_id[ii, 3] * alpha[7] + } + + # Priors for the model for c2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + + alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[10] + M_id[ii, 3] * alpha[11] + } + + # Priors for the model for L1mis + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Beta mixed effects model for Be2 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[13] + M_id[ii, 3] * alpha[14] + } + + # Priors for the model for Be2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[15] + + M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m4b -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[3] * M_lvlone[i, 6] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) - mu_p2[i] <- b_p2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[3] * M_lvlone[i, 6] + - alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for p2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - probit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5] - } - - # Priors for the model for b2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- 1/max(1e-10, inv_mu_c2[i]) - inv_mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] - } - - # Priors for the model for c2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Log-normal mixed effects model for L1mis -------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dlnorm(mu_L1mis[i], tau_L1mis) - mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10] - } - - # Priors for the model for L1mis - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - -} +model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[3] * M_lvlone[i, 6] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) + mu_p2[i] <- b_p2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[3] * M_lvlone[i, 6] + + alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for p2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + probit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5] + } + + # Priors for the model for b2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- 1/max(1e-10, inv_mu_c2[i]) + inv_mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] + } + + # Priors for the model for c2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Log-normal mixed effects model for L1mis -------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dlnorm(mu_L1mis[i], tau_L1mis) + mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10] + } + + # Priors for the model for L1mis + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + } $m4c -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[3] * M_lvlone[i, 6] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) - mu_p2[i] <- b_p2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[3] * M_lvlone[i, 6] + - alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for p2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - log(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5] - } - - # Priors for the model for b2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) - log(mu_c2[i]) <- b_c2_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] - } - - # Priors for the model for c2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10] - } - - # Priors for the model for L1mis - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - -} +model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[3] * M_lvlone[i, 6] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) + mu_p2[i] <- b_p2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[3] * M_lvlone[i, 6] + + alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for p2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + log(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5] + } + + # Priors for the model for b2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) + log(mu_c2[i]) <- b_c2_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] + } + + # Priors for the model for c2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10] + } + + # Priors for the model for L1mis + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + } $m4d -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[3] * M_lvlone[i, 7] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_beta[k]) - tau_reg_norm_ridge_beta[k] ~ dgamma(0.01, 0.01) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) - mu_p2[i] <- b_p2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[3] * M_lvlone[i, 7] + - alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - alpha[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for p2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson_ridge_alpha[k]) - tau_reg_poisson_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - log(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[8] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - alpha[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[6] - } - - # Priors for the model for b2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_alpha[k]) - tau_reg_binom_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) - log(mu_c2[i]) <- b_c2_id[group_id[i], 1] + - alpha[11] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - alpha[12] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[10] - } - - # Priors for the model for c2 - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] + - alpha[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[13] - } - - # Priors for the model for L1mis - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k]) - tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Normal mixed effects model for Be2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 6] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1) - mu_Be2[i] <- b_Be2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * alpha[15] - } - - # Priors for the model for Be2 - for (k in 15:15) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_Be2 <- sqrt(1/tau_Be2) - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - -} +model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[3] * M_lvlone[i, 7] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_beta[k]) + tau_reg_norm_ridge_beta[k] ~ dgamma(0.01, 0.01) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Poisson mixed effects model for p2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) + mu_p2[i] <- b_p2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[3] * M_lvlone[i, 7] + + alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + alpha[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) + mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for p2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson_ridge_alpha[k]) + tau_reg_poisson_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + + invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + log(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[8] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + alpha[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[6] + } + + # Priors for the model for b2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_alpha[k]) + tau_reg_binom_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) + log(mu_c2[i]) <- b_c2_id[group_id[i], 1] + + alpha[11] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + alpha[12] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[10] + } + + # Priors for the model for c2 + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] + + alpha[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[13] + } + + # Priors for the model for L1mis + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k]) + tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Normal mixed effects model for Be2 -------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 6] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1) + mu_Be2[i] <- b_Be2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * alpha[15] + } + + # Priors for the model for Be2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_Be2 <- sqrt(1/tau_Be2) + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + } $m5a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[6] * M_lvlone[i, 5] + beta[7] * M_lvlone[i, 6] + - beta[8] * M_lvlone[i, 7] + - beta[9] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + - beta[11] * (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] + - beta[12] * (M_lvlone[i, 10] - spM_lvlone[10, 1])/spM_lvlone[10, 2] + - beta[13] * (M_lvlone[i, 11] - spM_lvlone[11, 1])/spM_lvlone[11, 2] + - beta[14] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + - M_id[ii, 4] * beta[3] + M_id[ii, 5] * beta[4] + - (M_id[ii, 6] - spM_id[6, 1])/spM_id[6, 2] * beta[5] - mu_b_y_id[ii, 2] <- beta[10] - } - - # Priors for the model for y - for (k in 1:14) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[6] * M_lvlone[i, 5] + - alpha[7] * M_lvlone[i, 6] + alpha[8] * M_lvlone[i, 7] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - - M_lvlone[i, 8] <- abs(M_id[group_id[i], 7] - M_lvlone[i, 2]) - - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + - M_id[ii, 4] * alpha[3] + M_id[ii, 5] * alpha[4] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[5] - } - - # Priors for the model for c2 - for (k in 1:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] + - alpha[14] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- M_id[ii, 3] * alpha[10] + M_id[ii, 4] * alpha[11] + - M_id[ii, 5] * alpha[12] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[13] - } - - - - # Priors for the model for o2 - for (k in 10:14) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[15] + M_id[ii, 3] * alpha[16] + - M_id[ii, 4] * alpha[17] + M_id[ii, 5] * alpha[18] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[19] - } - - # Priors for the model for time - for (k in 15:19) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 2] * alpha[20] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[21] - log(phi_M2[ii, 3]) <- M_id[ii, 2] * alpha[22] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[23] - log(phi_M2[ii, 4]) <- M_id[ii, 2] * alpha[24] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[25] - - M_id[ii, 3] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 20:25) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 10] <- M_lvlone[i, 5] * M_lvlone[i, 8] - M_lvlone[i, 11] <- M_lvlone[i, 6] * M_lvlone[i, 8] - M_lvlone[i, 12] <- M_lvlone[i, 7] * M_lvlone[i, 8] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[6] * M_lvlone[i, 5] + beta[7] * M_lvlone[i, 6] + + beta[8] * M_lvlone[i, 7] + + beta[9] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + beta[11] * (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] + + beta[12] * (M_lvlone[i, 10] - spM_lvlone[10, 1])/spM_lvlone[10, 2] + + beta[13] * (M_lvlone[i, 11] - spM_lvlone[11, 1])/spM_lvlone[11, 2] + + beta[14] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + + M_id[ii, 4] * beta[3] + M_id[ii, 5] * beta[4] + + (M_id[ii, 6] - spM_id[6, 1])/spM_id[6, 2] * beta[5] + mu_b_y_id[ii, 2] <- beta[10] + } + + # Priors for the model for y + for (k in 1:14) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[6] * M_lvlone[i, 5] + + alpha[7] * M_lvlone[i, 6] + alpha[8] * M_lvlone[i, 7] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + + M_lvlone[i, 8] <- abs(M_id[group_id[i], 7] - M_lvlone[i, 2]) + + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + + M_id[ii, 4] * alpha[3] + M_id[ii, 5] * alpha[4] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[5] + } + + # Priors for the model for c2 + for (k in 1:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Cumulative logit mixed effects model for o2 ----------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dcat(p_o2[i, 1:4]) + eta_o2[i] <- b_o2_id[group_id[i], 1] + + alpha[14] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) + p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) + p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) + p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) + + logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] + logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] + logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + for (ii in 1:100) { + b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) + mu_b_o2_id[ii, 1] <- M_id[ii, 3] * alpha[10] + M_id[ii, 4] * alpha[11] + + M_id[ii, 5] * alpha[12] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[13] + } + + + + # Priors for the model for o2 + for (k in 10:14) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) + gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) + + invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[15] + M_id[ii, 3] * alpha[16] + + M_id[ii, 4] * alpha[17] + M_id[ii, 5] * alpha[18] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[19] + } + + # Priors for the model for time + for (k in 15:19) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 2] * alpha[20] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[21] + log(phi_M2[ii, 3]) <- M_id[ii, 2] * alpha[22] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[23] + log(phi_M2[ii, 4]) <- M_id[ii, 2] * alpha[24] + + (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[25] + + M_id[ii, 3] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 20:25) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 10] <- M_lvlone[i, 5] * M_lvlone[i, 8] + M_lvlone[i, 11] <- M_lvlone[i, 6] * M_lvlone[i, 8] + M_lvlone[i, 12] <- M_lvlone[i, 7] * M_lvlone[i, 8] + } + + } $m5b -model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + - b_b1_id[group_id[i], 2] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - b_b1_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:3] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 2] * beta[1] - mu_b_b1_id[ii, 2] <- beta[5] - mu_b_b1_id[ii, 3] <- 0 - } - - # Priors for the model for b1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k]) - tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01) - } - - for (k in 1:3) { - RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_b1_id[1:3, 1:3] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id) - D_b1_id[1:3, 1:3] <- inverse(invD_b1_id[ , ]) - - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] - } - - # Priors for the model for L1mis - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k]) - tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Beta mixed effects model for Be2 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + - alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - - - M_lvlone[i, 7] <- log(M_lvlone[i, 3]) - - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] - } - - # Priors for the model for Be2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta_ridge_alpha[k]) - tau_reg_beta_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - - - M_lvlone[i, 6] <- abs(M_lvlone[i, 4] - M_id[group_id[i], 1]) - - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[11] - } - - # Priors for the model for c1 - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[14] - } - - # Priors for the model for time - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - log(mu_C2[ii]) <- M_id[ii, 2] * alpha[15] - - - - } - - # Priors for the model for C2 - for (k in 15:15) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + + b_b1_id[group_id[i], 2] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + b_b1_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:3] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 2] * beta[1] + mu_b_b1_id[ii, 2] <- beta[5] + mu_b_b1_id[ii, 3] <- 0 + } + + # Priors for the model for b1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k]) + tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01) + } + + for (k in 1:3) { + RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_b1_id[1:3, 1:3] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id) + D_b1_id[1:3, 1:3] <- inverse(invD_b1_id[ , ]) + + + # Gamma mixed effects model for L1mis ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) + + shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) + rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) + + mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) + inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) + mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] + } + + # Priors for the model for L1mis + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k]) + tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) + sigma_L1mis <- sqrt(1/tau_L1mis) + + invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) + + + # Beta mixed effects model for Be2 ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) + + shape1_Be2[i] <- mu_Be2[i] * tau_Be2 + shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 + + logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + + alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + + M_lvlone[i, 7] <- log(M_lvlone[i, 3]) + + } + + for (ii in 1:100) { + b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) + mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] + } + + # Priors for the model for Be2 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta_ridge_alpha[k]) + tau_reg_beta_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) + + + invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + + M_lvlone[i, 6] <- abs(M_lvlone[i, 4] - M_id[group_id[i], 1]) + + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[11] + } + + # Priors for the model for c1 + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 5] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[14] + } + + # Priors for the model for time + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + log(mu_C2[ii]) <- M_id[ii, 2] * alpha[15] + + + + } + + # Priors for the model for C2 + for (k in 15:15) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m6a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[3] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[4] * M_lvlone[i, 3] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- beta[5] - } - - # Priors for the model for y - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] - } - - # Priors for the model for b2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] - } - - # Priors for the model for C2 - for (k in 5:6) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[3] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[4] * M_lvlone[i, 3] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- beta[5] + } + + # Priors for the model for y + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + } + + # Priors for the model for b2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + } + + # Priors for the model for C2 + for (k in 5:6) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m6b -model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_b1_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[3] * M_id[group_id[i], 3] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:2] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- beta[5] - mu_b_b1_id[ii, 2] <- 0 - } - - # Priors for the model for b1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k]) - tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01) - } - - for (k in 1:2) { - RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_b1_id[1:2, 1:2] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id) - D_b1_id[1:2, 1:2] <- inverse(invD_b1_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] + - M_id[ii, 3] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[6] + - M_id[ii, 3] * alpha[7] - } - - # Priors for the model for time - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9] - } - - # Priors for the model for C2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_b1_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + beta[3] * M_id[group_id[i], 3] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:2] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- beta[5] + mu_b_b1_id[ii, 2] <- 0 + } + + # Priors for the model for b1 + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k]) + tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01) + } + + for (k in 1:2) { + RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_b1_id[1:2, 1:2] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id) + D_b1_id[1:2, 1:2] <- inverse(invD_b1_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] + + M_id[ii, 3] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[6] + + M_id[ii, 3] * alpha[7] + } + + # Priors for the model for time + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9] + } + + # Priors for the model for C2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) + tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m7a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[2] - mu_b_y_id[ii, 3] <- beta[3] - } - - # Priors for the model for y - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[2] + mu_b_y_id[ii, 3] <- beta[3] + } + + # Priors for the model for y + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + } $m7b -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[2] - mu_b_y_id[ii, 3] <- beta[3] - mu_b_y_id[ii, 4] <- beta[4] - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:4) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[2] + mu_b_y_id[ii, 3] <- beta[3] + mu_b_y_id[ii, 4] <- beta[4] + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:4) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) + } $m7c -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - mu_b_y_id[ii, 2] <- beta[4] - mu_b_y_id[ii, 3] <- beta[5] - mu_b_y_id[ii, 4] <- beta[6] - } - - # Priors for the model for y - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:4) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] + mu_b_y_id[ii, 2] <- beta[4] + mu_b_y_id[ii, 3] <- beta[5] + mu_b_y_id[ii, 4] <- beta[6] + } + + # Priors for the model for y + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:4) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) + } $m7d -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] - mu_b_y_id[ii, 2] <- 0 - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] - } - - # Priors for the model for time - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - } - - # Priors for the model for C2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + mu_b_y_id[ii, 2] <- 0 + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] + } + + # Priors for the model for time + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + } + + # Priors for the model for C2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m7e -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] - mu_b_y_id[ii, 2] <- beta[5] - mu_b_y_id[ii, 3] <- beta[6] - mu_b_y_id[ii, 4] <- beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:4) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] - } - - # Priors for the model for C2 - for (k in 5:6) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + mu_b_y_id[ii, 2] <- beta[5] + mu_b_y_id[ii, 3] <- beta[6] + mu_b_y_id[ii, 4] <- beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:4) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + } + + # Priors for the model for C2 + for (k in 5:6) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m7f -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] - mu_b_y_id[ii, 2] <- 0 - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] - } - - # Priors for the model for time - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - } - - # Priors for the model for C2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + mu_b_y_id[ii, 2] <- 0 + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] + } + + # Priors for the model for time + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + } + + # Priors for the model for C2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m8a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[4] - mu_b_y_id[ii, 3] <- beta[3] - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[4] + mu_b_y_id[ii, 3] <- beta[3] + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m8b -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[4] - mu_b_y_id[ii, 3] <- beta[3] - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[4] + mu_b_y_id[ii, 3] <- beta[3] + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m8c -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] - mu_b_y_id[ii, 2] <- beta[5] - mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6] - } - - # Priors for the model for y - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] - } - - # Priors for the model for c2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6] - } - - # Priors for the model for c1 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] - - M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 8:8) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + mu_b_y_id[ii, 2] <- beta[5] + mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6] + } + + # Priors for the model for y + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + } + + # Priors for the model for c2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6] + } + + # Priors for the model for c1 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + + M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 8:8) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3] + } + + } $m8d -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] - mu_b_y_id[ii, 2] <- beta[5] - mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6] - } - - # Priors for the model for y - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] - } - - # Priors for the model for c2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6] - } - - # Priors for the model for c1 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9] - } - - # Priors for the model for time - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] - - M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + mu_b_y_id[ii, 2] <- beta[5] + mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6] + } + + # Priors for the model for y + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + } + + # Priors for the model for c2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6] + } + + # Priors for the model for c1 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9] + } + + # Priors for the model for time + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + + M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 10:10) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3] + } + + } $m8e -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for time - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for time + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] + } + + } $m8f -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 10:11) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 10:11) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] + } + + } $m8g -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 6:7) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 6:7) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] + } + + } $m8h -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for time - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for time + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + } + + } $m8i -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 10:11) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[5] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 10:11) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + } + + } $m8j -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for time - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for time + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + } + + } $m8k -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for time - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[6] + mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c2 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for c1 + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for time + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + } + + } $m8l -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[5] + M_id[ii, 4] * beta[7] - mu_b_y_id[ii, 3] <- 0 - } - - # Priors for the model for y - for (k in 1:9) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - M_id[ii, 4] * alpha[7] - } - - # Priors for the model for time - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 4] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] - M_lvlone[i, 7] <- M_id[group_id[i], 4] * M_lvlone[i, 2] * M_lvlone[i, 3] - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[5] + M_id[ii, 4] * beta[7] + mu_b_y_id[ii, 3] <- 0 + } + + # Priors for the model for y + for (k in 1:9) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:3) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + M_id[ii, 4] * alpha[7] + } + + # Priors for the model for time + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 4] <- M_id[group_id[i], 4] * M_lvlone[i, 2] + M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] + M_lvlone[i, 7] <- M_id[group_id[i], 4] * M_lvlone[i, 2] * M_lvlone[i, 3] + } + + } $m8m -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + b_y_id[group_id[i], 2] * M_lvlone[i, 3] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[4] * M_lvlone[i, 4] + beta[5] * M_lvlone[i, 5] + - beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[3] - } - - # Priors for the model for y - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + b_y_id[group_id[i], 2] * M_lvlone[i, 3] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[4] * M_lvlone[i, 4] + beta[5] * M_lvlone[i, 5] + + beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + mu_b_y_id[ii, 2] <- beta[3] + } + + # Priors for the model for y + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + } $m8n -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[6] * M_lvlone[i, 5] - } - - for (ii in 1:100) { - b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[2] - mu_b_y_id[ii, 3] <- beta[5] - mu_b_y_id[ii, 4] <- beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:4) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * M_lvlone[i, 5] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for time - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for b1 - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_y_id[group_id[i], 4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[6] * M_lvlone[i, 5] + } + + for (ii in 1:100) { + b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 4] * beta[3] + mu_b_y_id[ii, 2] <- beta[2] + mu_b_y_id[ii, 3] <- beta[5] + mu_b_y_id[ii, 4] <- beta[7] + } + + # Priors for the model for y + for (k in 1:7) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:4) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[5] * M_lvlone[i, 5] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for c1 + for (k in 1:5) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[6] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + + M_id[ii, 4] * alpha[8] + } + + # Priors for the model for time + for (k in 6:9) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Binomial mixed effects model for b1 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 4] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) + logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) + mu_b_b1_id[ii, 1] <- M_id[ii, 2] * alpha[10] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + + M_id[ii, 4] * alpha[12] + } + + # Priors for the model for b1 + for (k in 10:12) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 13:14) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m9a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + b_y_o1[group_o1[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[3] * M_lvlone[i, 3] + - beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - for (iii in 1:3) { - b_y_o1[iii, 1:1] ~ dnorm(mu_b_y_o1[iii, ], invD_y_o1[ , ]) - mu_b_y_o1[iii, 1] <- 0 - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - invD_y_o1[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_o1[1, 1] <- 1 / (invD_y_o1[1, 1]) - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + b_y_o1[group_o1[i], 1] + + beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[3] * M_lvlone[i, 3] + + beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + for (iii in 1:3) { + b_y_o1[iii, 1:1] ~ dnorm(mu_b_y_o1[iii, ], invD_y_o1[ , ]) + mu_b_y_o1[iii, 1] <- 0 + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + invD_y_o1[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_o1[1, 1] <- 1 / (invD_y_o1[1, 1]) + } $m9b -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + - M_id[ii, 4] * beta[4] - mu_b_y_id[ii, 2] <- beta[5] - } - - # Priors for the model for y - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + - M_id[ii, 4] * alpha[4] - } - - # Priors for the model for time - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - M_id[ii, 4] * alpha[7] - } - - # Priors for the model for C2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + + b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + + M_id[ii, 4] * beta[4] + mu_b_y_id[ii, 2] <- beta[5] + } + + # Priors for the model for y + for (k in 1:5) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + for (k in 1:2) { + RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) + D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + + M_id[ii, 4] * alpha[4] + } + + # Priors for the model for time + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + M_id[ii, 4] * alpha[7] + } + + # Priors for the model for C2 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m9c -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + - M_id[ii, 4] * beta[4] - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for C2 - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Normal mixed effects model for y ---------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) + mu_y[i] <- b_y_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) + mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + + (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + + M_id[ii, 4] * beta[4] + } + + # Priors for the model for y + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_y <- sqrt(1/tau_y) + + invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for C2 + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } diff --git a/tests/testthat/testout/mlogit_lapply.models.jagsmodel..txt b/tests/testthat/testout/mlogit_lapply.models.jagsmodel..txt index 1db41eb4..44b415f7 100644 --- a/tests/testthat/testout/mlogit_lapply.models.jagsmodel..txt +++ b/tests/testthat/testout/mlogit_lapply.models.jagsmodel..txt @@ -1,512 +1,495 @@ $m0a -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] - log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[2] - log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[3] - } - - # Priors for the model for M1 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - -} +model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] + log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[2] + log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[3] + } + + # Priors for the model for M1 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } $m0b -model { - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 2] * beta[1] - log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[2] - log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[3] - } - - # Priors for the model for M2 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - -} +model { + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 2] * beta[1] + log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[2] + log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[3] + } + + # Priors for the model for M2 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } $m1a -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] - log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[5] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6] - } - - # Priors for the model for M1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - -} +model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[5] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6] + } + + # Priors for the model for M1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } $m1b -model { - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] - log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[5] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6] - } - - # Priors for the model for M2 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - -} +model { + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 2] * beta[1] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] + log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[3] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] + log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[5] + + (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6] + } + + # Priors for the model for M2 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } $m2a -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - log(phi_M1[i, 3]) <- M_lvlone[i, 3] * beta[3] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] - log(phi_M1[i, 4]) <- M_lvlone[i, 3] * beta[5] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] - } - - # Priors for the model for M1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + log(phi_M1[i, 3]) <- M_lvlone[i, 3] * beta[3] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + log(phi_M1[i, 4]) <- M_lvlone[i, 3] * beta[5] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] + } + + # Priors for the model for M1 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2b -model { - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - log(phi_M2[i, 3]) <- M_lvlone[i, 3] * beta[3] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] - log(phi_M2[i, 4]) <- M_lvlone[i, 3] * beta[5] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] - } - - # Priors for the model for M2 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 3] * beta[1] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] + log(phi_M2[i, 3]) <- M_lvlone[i, 3] * beta[3] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + log(phi_M2[i, 4]) <- M_lvlone[i, 3] * beta[5] + + (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] + } + + # Priors for the model for M2 + for (k in 1:6) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 3] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m3a -model { - - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] + - M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4] - } - - # Priors for the model for C1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - -} +model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] + + M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4] + } + + # Priors for the model for C1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + } $m3b -model { - - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] - } - - # Priors for the model for C1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 2] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 3] * alpha[1] - log(phi_M2[i, 3]) <- M_lvlone[i, 3] * alpha[2] - log(phi_M2[i, 4]) <- M_lvlone[i, 3] * alpha[3] - - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - -} +model { + + # Normal model for C1 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) + mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] + + M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] + } + + # Priors for the model for C1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C1 <- sqrt(1/tau_C1) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 2] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 3] * alpha[1] + log(phi_M2[i, 3]) <- M_lvlone[i, 3] * alpha[2] + log(phi_M2[i, 4]) <- M_lvlone[i, 3] * alpha[3] + + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 1:3) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + } $m4a -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + - M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + - M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + - M_lvlone[i, 11] * beta[7] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] - log(phi_M1[i, 3]) <- M_lvlone[i, 5] * beta[13] + M_lvlone[i, 6] * beta[14] + - M_lvlone[i, 7] * beta[15] + M_lvlone[i, 8] * beta[16] + - M_lvlone[i, 9] * beta[17] + M_lvlone[i, 10] * beta[18] + - M_lvlone[i, 11] * beta[19] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[20] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[21] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[22] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[23] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[24] - log(phi_M1[i, 4]) <- M_lvlone[i, 5] * beta[25] + M_lvlone[i, 6] * beta[26] + - M_lvlone[i, 7] * beta[27] + M_lvlone[i, 8] * beta[28] + - M_lvlone[i, 9] * beta[29] + M_lvlone[i, 10] * beta[30] + - M_lvlone[i, 11] * beta[31] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[32] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[33] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[34] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[35] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[36] - } - - # Priors for the model for M1 - for (k in 1:36) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] - - M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] - M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] - M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] - } - - -} +model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + + M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + + M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + + M_lvlone[i, 11] * beta[7] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] + log(phi_M1[i, 3]) <- M_lvlone[i, 5] * beta[13] + M_lvlone[i, 6] * beta[14] + + M_lvlone[i, 7] * beta[15] + M_lvlone[i, 8] * beta[16] + + M_lvlone[i, 9] * beta[17] + M_lvlone[i, 10] * beta[18] + + M_lvlone[i, 11] * beta[19] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[20] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[21] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[22] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[23] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[24] + log(phi_M1[i, 4]) <- M_lvlone[i, 5] * beta[25] + M_lvlone[i, 6] * beta[26] + + M_lvlone[i, 7] * beta[27] + M_lvlone[i, 8] * beta[28] + + M_lvlone[i, 9] * beta[29] + M_lvlone[i, 10] * beta[30] + + M_lvlone[i, 11] * beta[31] + + (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[32] + + (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[33] + + (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[34] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[35] + + (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[36] + } + + # Priors for the model for M1 + for (k in 1:36) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + + M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + + M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + + M_lvlone[i, 11] * alpha[7] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] + + M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + + M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + + M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + + M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + + (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] + + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Cumulative logit model for O2 ------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) + eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] + + p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) + p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) + p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) + p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) + + logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] + logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] + logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) + } + + # Priors for the model for O2 + for (k in 24:24) { + alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) + } + + delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + + gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) + gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) + gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] + M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] + M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] + } + + } $m4b -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] - log(phi_M1[i, 3]) <- M_lvlone[i, 4] * beta[6] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[7] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[9] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[10] - log(phi_M1[i, 4]) <- M_lvlone[i, 4] * beta[11] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[12] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[14] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[15] - } - - # Priors for the model for M1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] + - M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] + - M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] + - M_lvlone[i, 14] * alpha[7] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8] - - M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 4] * alpha[9] + M_lvlone[i, 12] * alpha[10] + - M_lvlone[i, 13] * alpha[11] + M_lvlone[i, 14] * alpha[12] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 4] * alpha[14] + M_lvlone[i, 12] * alpha[15] + - M_lvlone[i, 13] * alpha[16] + M_lvlone[i, 14] * alpha[17] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 4] * alpha[19] + M_lvlone[i, 12] * alpha[20] + - M_lvlone[i, 13] * alpha[21] + M_lvlone[i, 14] * alpha[22] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[23] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - - - M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] - } - - -} +model { + + # Multinomial logit model for M1 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) + + p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) + p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) + p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) + p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) + + log(phi_M1[i, 1]) <- 0 + log(phi_M1[i, 2]) <- M_lvlone[i, 4] * beta[1] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] + log(phi_M1[i, 3]) <- M_lvlone[i, 4] * beta[6] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[7] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[9] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[10] + log(phi_M1[i, 4]) <- M_lvlone[i, 4] * beta[11] + + (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[12] + + (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + + (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[14] + + (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[15] + } + + # Priors for the model for M1 + for (k in 1:15) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (i in 1:100) { + M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) + mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] + + M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] + + M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] + + M_lvlone[i, 14] * alpha[7] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8] + + M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2]) + + + } + + # Priors for the model for C2 + for (k in 1:8) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + + # Multinomial logit model for M2 ------------------------------------------------ + for (i in 1:100) { + M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) + + p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) + p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) + p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) + p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) + + log(phi_M2[i, 1]) <- 0 + log(phi_M2[i, 2]) <- M_lvlone[i, 4] * alpha[9] + M_lvlone[i, 12] * alpha[10] + + M_lvlone[i, 13] * alpha[11] + M_lvlone[i, 14] * alpha[12] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[13] + log(phi_M2[i, 3]) <- M_lvlone[i, 4] * alpha[14] + M_lvlone[i, 12] * alpha[15] + + M_lvlone[i, 13] * alpha[16] + M_lvlone[i, 14] * alpha[17] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[18] + log(phi_M2[i, 4]) <- M_lvlone[i, 4] * alpha[19] + M_lvlone[i, 12] * alpha[20] + + M_lvlone[i, 13] * alpha[21] + M_lvlone[i, 14] * alpha[22] + + (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[23] + + M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) + M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) + M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) + + + + M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0) + + } + + # Priors for the model for M2 + for (k in 9:23) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + # Re-calculate interaction terms + for (i in 1:100) { + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] + } + + } diff --git a/tests/testthat/testout/mlogitmm_lapply.models.jagsmodel..txt b/tests/testthat/testout/mlogitmm_lapply.models.jagsmodel..txt index 384367ab..9a3b0fbc 100644 --- a/tests/testthat/testout/mlogitmm_lapply.models.jagsmodel..txt +++ b/tests/testthat/testout/mlogitmm_lapply.models.jagsmodel..txt @@ -1,1044 +1,1022 @@ $m0a -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + } $m0b -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:2) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + } $m1a -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 1] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 1] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + } $m1b -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 1] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 1] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + } $m1c -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + } $m1d -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + } $m2a -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 2] + - beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 2] + + beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2b -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 2] + - beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - -} +model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 2] + + beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[1] + } + + # Priors for the model for C2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + } $m2c -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m2d -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + beta[2] * M_id[group_id[i], 1] + + beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:4) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + + + # Normal mixed effects model for c2 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) + mu_c2[i] <- b_c2_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) + mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + } + + # Priors for the model for c2 + for (k in 1:1) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c2 <- sqrt(1/tau_c2) + + invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) + } $m3a -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 2] + - beta[3] * M_lvlone[i, 3] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - -} +model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 2] + + beta[3] * M_lvlone[i, 3] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + } $m3b -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 3] + - beta[3] * M_lvlone[i, 4] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - alpha[1] * M_id[group_id[i], 1] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - alpha[2] * M_id[group_id[i], 1] - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:2) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - -} +model { + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 3] + + beta[3] * M_lvlone[i, 4] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] + } + + # Priors for the model for c1 + for (k in 1:3) { + beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + alpha[1] * M_id[group_id[i], 1] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + alpha[2] * M_id[group_id[i], 1] + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:2) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + } $m4a -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 3] + - beta[2] * M_id[group_id[i], 4] + - beta[3] * M_id[group_id[i], 5] + - beta[4] * M_id[group_id[i], 6] + - beta[5] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] + - beta[6] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + - beta[13] * M_lvlone[i, 3] + beta[14] * M_lvlone[i, 4] + - beta[15] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[16] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[7] * M_id[group_id[i], 3] + - beta[8] * M_id[group_id[i], 4] + - beta[9] * M_id[group_id[i], 5] + - beta[10] * M_id[group_id[i], 6] + - beta[11] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] + - beta[12] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + - beta[17] * M_lvlone[i, 3] + beta[18] * M_lvlone[i, 4] + - beta[19] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[20] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:20) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - alpha[1] * M_id[group_id[i], 3] + - alpha[2] * M_id[group_id[i], 4] + - alpha[3] * M_id[group_id[i], 5] + - alpha[4] * M_id[group_id[i], 6] + - alpha[5] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] + - alpha[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - alpha[7] * M_id[group_id[i], 3] + - alpha[8] * M_id[group_id[i], 4] + - alpha[9] * M_id[group_id[i], 5] + - alpha[10] * M_id[group_id[i], 6] + - alpha[11] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] + - alpha[12] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:12) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 3] * alpha[13] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[14] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[15] - log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[16] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[17] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[18] - log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[19] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[20] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[21] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 13:21) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 3] * alpha[22] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[23] - - M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2]) - - - } - - # Priors for the model for C2 - for (k in 22:23) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_lvlone[i, 3] * M_id[group_id[i], 7] - M_lvlone[i, 6] <- M_lvlone[i, 4] * M_id[group_id[i], 7] - } - - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 3] + + beta[2] * M_id[group_id[i], 4] + + beta[3] * M_id[group_id[i], 5] + + beta[4] * M_id[group_id[i], 6] + + beta[5] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] + + beta[6] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + + beta[13] * M_lvlone[i, 3] + beta[14] * M_lvlone[i, 4] + + beta[15] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[16] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[7] * M_id[group_id[i], 3] + + beta[8] * M_id[group_id[i], 4] + + beta[9] * M_id[group_id[i], 5] + + beta[10] * M_id[group_id[i], 6] + + beta[11] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] + + beta[12] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + + beta[17] * M_lvlone[i, 3] + beta[18] * M_lvlone[i, 4] + + beta[19] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + + beta[20] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:20) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + alpha[1] * M_id[group_id[i], 3] + + alpha[2] * M_id[group_id[i], 4] + + alpha[3] * M_id[group_id[i], 5] + + alpha[4] * M_id[group_id[i], 6] + + alpha[5] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] + + alpha[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + alpha[7] * M_id[group_id[i], 3] + + alpha[8] * M_id[group_id[i], 4] + + alpha[9] * M_id[group_id[i], 5] + + alpha[10] * M_id[group_id[i], 6] + + alpha[11] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] + + alpha[12] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:12) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + + + # Multinomial logit model for M2 ------------------------------------------------ + for (ii in 1:100) { + M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) + + p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) + p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) + p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) + p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) + + log(phi_M2[ii, 1]) <- 0 + log(phi_M2[ii, 2]) <- M_id[ii, 3] * alpha[13] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[14] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[15] + log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[16] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[17] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[18] + log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[19] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[20] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[21] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0) + M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0) + M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0) + + } + + # Priors for the model for M2 + for (k in 13:21) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 3] * alpha[22] + + (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[23] + + M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2]) + + + } + + # Priors for the model for C2 + for (k in 22:23) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 5] <- M_lvlone[i, 3] * M_id[group_id[i], 7] + M_lvlone[i, 6] <- M_lvlone[i, 4] * M_id[group_id[i], 7] + } + + } $m4b -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[4] * M_id[group_id[i], 2] + - beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[6] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - alpha[1] * M_id[group_id[i], 2] + - alpha[2] * M_id[group_id[i], 5] + - alpha[3] * M_id[group_id[i], 6] + - alpha[4] * M_id[group_id[i], 7] + - alpha[5] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + - alpha[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - alpha[7] * M_id[group_id[i], 2] + - alpha[8] * M_id[group_id[i], 5] + - alpha[9] * M_id[group_id[i], 6] + - alpha[10] * M_id[group_id[i], 7] + - alpha[11] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + - alpha[12] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - - - - M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0) - - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:12) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[13] + M_id[ii, 5] * alpha[14] + - M_id[ii, 6] * alpha[15] + M_id[ii, 7] * alpha[16] + - (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[17] - - M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1]) - - - } - - # Priors for the model for C2 - for (k in 13:17) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3] - } - - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[4] * M_id[group_id[i], 2] + + beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[6] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + + beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + + + # Multinomial logit mixed model for m2 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) + + p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) + p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) + p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) + + log(phi_m2[i, 1]) <- 0 + log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + + alpha[1] * M_id[group_id[i], 2] + + alpha[2] * M_id[group_id[i], 5] + + alpha[3] * M_id[group_id[i], 6] + + alpha[4] * M_id[group_id[i], 7] + + alpha[5] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + + alpha[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + + alpha[7] * M_id[group_id[i], 2] + + alpha[8] * M_id[group_id[i], 5] + + alpha[9] * M_id[group_id[i], 6] + + alpha[10] * M_id[group_id[i], 7] + + alpha[11] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + + alpha[12] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) + M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) + + + + M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0) + + } + + for (ii in 1:100) { + b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) + mu_b_m2_id[ii, 1] <- 0 + } + + + + # Priors for the model for m2 + for (k in 1:12) { + alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) + + + # Normal model for C2 ----------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) + mu_C2[ii] <- M_id[ii, 2] * alpha[13] + M_id[ii, 5] * alpha[14] + + M_id[ii, 6] * alpha[15] + M_id[ii, 7] * alpha[16] + + (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[17] + + M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1]) + + + } + + # Priors for the model for C2 + for (k in 13:17) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_C2 <- sqrt(1/tau_C2) + + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3] + } + + } $m4c -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[3] * M_id[group_id[i], 4] + - beta[7] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[4] * M_id[group_id[i], 2] + - beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[6] * M_id[group_id[i], 4] + - beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[10] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:4] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - mu_b_m1_id[ii, 2] <- 0 - mu_b_m1_id[ii, 3] <- 0 - mu_b_m1_id[ii, 4] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - for (k in 1:4) { - RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_m1_id[1:4, 1:4] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id) - D_m1_id[1:4, 1:4] <- inverse(invD_m1_id[ , ]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for time - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - M_id[ii, 4] * alpha[7] - } - - # Priors for the model for c1 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[1] * M_id[group_id[i], 2] + + beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[3] * M_id[group_id[i], 4] + + beta[7] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[4] * M_id[group_id[i], 2] + + beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + + beta[6] * M_id[group_id[i], 4] + + beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[10] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:4] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + mu_b_m1_id[ii, 2] <- 0 + mu_b_m1_id[ii, 3] <- 0 + mu_b_m1_id[ii, 4] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + for (k in 1:4) { + RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_m1_id[1:4, 1:4] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id) + D_m1_id[1:4, 1:4] <- inverse(invD_m1_id[ , ]) + + + # Normal mixed effects model for time ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) + mu_time[i] <- b_time_id[group_id[i], 1] + + alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + } + + for (ii in 1:100) { + b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) + mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + + M_id[ii, 4] * alpha[3] + } + + # Priors for the model for time + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_time <- sqrt(1/tau_time) + + invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) + + + # Normal mixed effects model for c1 --------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) + mu_c1[i] <- b_c1_id[group_id[i], 1] + } + + for (ii in 1:100) { + b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) + mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + + M_id[ii, 4] * alpha[7] + } + + # Priors for the model for c1 + for (k in 5:7) { + alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) + } + tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) + sigma_c1 <- sqrt(1/tau_c1) + + invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) + + + # Binomial model for B2 --------------------------------------------------------- + for (ii in 1:100) { + M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) + logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + + (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] + + M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) + + } + + # Priors for the model for B2 + for (k in 8:9) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + } $m4d -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[1] * M_id[group_id[i], 1] + - beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[7] * M_lvlone[i, 5] + - beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + - beta[10] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[3] * M_id[group_id[i], 1] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[12] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[13] * M_lvlone[i, 5] + - beta[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[15] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + - beta[16] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:2] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - mu_b_m1_id[ii, 2] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:16) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - for (k in 1:2) { - RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_m1_id[1:2, 1:2] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id) - D_m1_id[1:2, 1:2] <- inverse(invD_m1_id[ , ]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] - } - - # Priors for the model for b2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] - } - - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[1] * M_id[group_id[i], 1] + + beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[7] * M_lvlone[i, 5] + + beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + + beta[10] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[3] * M_id[group_id[i], 1] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[12] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + + beta[13] * M_lvlone[i, 5] + + beta[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + beta[15] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + + beta[16] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:2] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + mu_b_m1_id[ii, 2] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:16) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) + } + + for (k in 1:2) { + RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) + } + invD_m1_id[1:2, 1:2] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id) + D_m1_id[1:2, 1:2] <- inverse(invD_m1_id[ , ]) + + + # Binomial mixed effects model for b2 ------------------------------------------- + for (i in 1:329) { + M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) + logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + + alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + + + M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) + } + + for (ii in 1:100) { + b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) + mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + } + + # Priors for the model for b2 + for (k in 1:4) { + alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) + } + + invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) + + # Re-calculate interaction terms + for (i in 1:329) { + M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] + } + + } $m4e -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[5] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[6] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 1] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial_ridge_beta[k]) - tau_reg_multinomial_ridge_beta[k] ~ dgamma(0.01, 0.01) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - -} +model { + + # Multinomial logit mixed model for m1 ------------------------------------------ + for (i in 1:329) { + M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) + + p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) + p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) + p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) + + log(phi_m1[i, 1]) <- 0 + log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + + beta[1] * M_id[group_id[i], 1] + + beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[5] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[6] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + + beta[3] * M_id[group_id[i], 1] + + beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + + beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + + beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + + beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + } + + for (ii in 1:100) { + b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) + mu_b_m1_id[ii, 1] <- 0 + } + + + + # Priors for the model for m1 + for (k in 1:10) { + beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial_ridge_beta[k]) + tau_reg_multinomial_ridge_beta[k] ~ dgamma(0.01, 0.01) + } + + invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) + D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) + } From 606cb00d01f66e0d3cdc4a79ab34f842bccb0ce3 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 16:31:56 +0200 Subject: [PATCH 115/176] use \code{} instead of rmarkdown version to see if this removes the warning when running the documentation --- R/plots.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/plots.R b/R/plots.R index 2baee73e..05c831bc 100644 --- a/R/plots.R +++ b/R/plots.R @@ -121,10 +121,10 @@ traceplot.JointAI <- function(object, start = NULL, end = NULL, thin = NULL, #' the MCMC sample of an object of class "JointAI". #' @inheritParams traceplot #' @param vlines list, where each element is a named list of parameters that -#' can be passed to `graphics::abline()` to create +#' can be passed to \code{graphics::abline()} to create #' vertical lines. #' Each of the list elements needs to contain at least -#' `v = `, where is a vector of the +#' \code{v = } where is a vector of the #' same length as the number of plots (see examples). #' @param joined logical; should the chains be combined before plotting? #' @param ... additional parameters passed to \code{plot()} From 3aa036f7e23def513bfb8a920dde8c8f469ecbaa Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 16:32:16 +0200 Subject: [PATCH 116/176] update link to codecov --- README.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.Rmd b/README.Rmd index e71ea2ef..7f897a4a 100644 --- a/README.Rmd +++ b/README.Rmd @@ -21,7 +21,7 @@ knitr::opts_chunk$set( [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/JointAI)](https://CRAN.R-project.org/package=JointAI) [![](https://cranlogs.r-pkg.org/badges/grand-total/JointAI)](https://CRAN.R-project.org/package=JointAI) [![Download counter](https://cranlogs.r-pkg.org/badges/JointAI)](https://cran.r-project.org/package=JointAI) -[![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://codecov.io/gh/NErler/JointAI) +[![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://app.codecov.io/gh/NErler/JointAI) [![Travis-CI Build Status](https://travis-ci.org/NErler/JointAI.svg?branch=master)](https://travis-ci.org/NErler/JointAI) [![R build status](https://github.com/NErler/JointAI/workflows/R-CMD-check/badge.svg)](https://github.com/NErler/JointAI/actions) From 3480287126be771f621593ac755dc704752456bf Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 16:33:29 +0200 Subject: [PATCH 117/176] update cran comments --- cran-comments.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/cran-comments.md b/cran-comments.md index 7fef5077..8944d802 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,3 +1,24 @@ + +# JointAI (version 1.0.4) + + +## Round 1 + +### Test environments +* local Windows 10, R 4.2.1 +* windows server x64 (via github actions), R 4.1.2 +* ubuntu 20.04.3 LTS (via github actions), R 4.0.5, R 4.1.2, devel +* win-builder (oldrelease, devel and release) + + + +### R CMD check results + +0 errors | 0 warnings | 1 note + + +--- + # JointAI (version 1.0.3) From de6dbba1a00fc5b043db1e2a49422ff5de8c6ece Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 17:12:26 +0200 Subject: [PATCH 118/176] tentative update github actions CMD check --- .github/workflows/R-CMD-check.yaml | 151 +++++++++++++++-------------- 1 file changed, 79 insertions(+), 72 deletions(-) diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 53d47773..6d116383 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -1,12 +1,8 @@ on: push: - branches: - - master - - JMdevel - - rd_vcov + branches: [main, master] pull_request: - branches: - - master + branches: [main, master] name: R-CMD-check @@ -23,50 +19,44 @@ jobs: config: - {os: macOS-latest, r: 'release'} - {os: windows-latest, r: 'release'} - - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - - {os: ubuntu-latest, r: 'release'} - - {os: ubuntu-latest, r: 'oldrel-1'} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} + - {os: ubuntu-latest, r: 'release'} + - {os: ubuntu-latest, r: 'oldrel-1'} env: R_REMOTES_NO_ERRORS_FROM_WARNINGS: true RSPM: ${{ matrix.config.rspm }} GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + R_KEEP_PKG_SOURCE: yes + steps: - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-r@v1 + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} http-user-agent: ${{ matrix.config.http-user-agent }} use-public-rspm: true - - uses: r-lib/actions/setup-pandoc@v1 - - - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} - - - name: Cache R packages - if: runner.os != 'Windows' - uses: actions/cache@v1 - with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - - name: Install system dependencies - if: runner.os == 'Linux' - run: | - while read -r cmd - do - eval sudo $cmd - done < <(Rscript -e 'cat(remotes::system_requirements("ubuntu", "16.04"), sep = "\n")') + # - name: Query dependencies + # run: | + # install.packages('remotes') + # saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) + # writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") + # shell: Rscript {0} + # + # - name: Cache R packages + # if: runner.os != 'Windows' + # uses: actions/cache@v1 + # with: + # path: ${{ env.R_LIBS_USER }} + # key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} + # restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - name: Install JAGS macOS if: runner.os == 'macOS' @@ -76,50 +66,67 @@ jobs: - name: Download JAGS Windows if: runner.os == 'Windows' - run: (New-Object System.Net.WebClient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.0.exe', 'C:\JAGS-4.3.0.exe') + run: (New-Object System.Net.WebClient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe', 'C:\JAGS-4.3.1.exe') shell: powershell - name: Install JAGS Windows if: runner.os == 'Windows' - run: C:\JAGS-4.3.0.exe /S + run: C:\JAGS-4.3.1.exe /S shell: cmd - - - name: Install dependencies + - name: Install system dependencies + if: runner.os == 'Linux' run: | - remotes::install_deps(dependencies = TRUE) - remotes::install_cran("rcmdcheck") - shell: Rscript {0} + while read -r cmd + do + eval sudo $cmd + done < <(Rscript -e 'cat(remotes::system_requirements("ubuntu", "20.04"), sep = "\n")') - - name: Session info - run: | - options(width = 100) - pkgs <- installed.packages()[, "Package"] - sessioninfo::session_info(pkgs, include_base = TRUE) - shell: Rscript {0} - - - name: Check - env: - _R_CHECK_CRAN_INCOMING_: false - _R_CHECK_FORCE_SUGGESTS_: false - _R_CHECK_SYSTEM_CLOCK_: false - IS_CHECK: true - run: | - os = .Platform$OS.type - if (os == "windows" & R.version$major == 3) {remotes::install_version("rjags", version = "4-10")} - jagshome = if (os == "windows") {readRegistry("SOFTWARE\\JAGS", maxdepth = 2, view = "32-bit")} - if (os == "windows"){Sys.setenv(JAGS_HOME = try(jagshome[["JAGS-4.3.0"]][["InstallDir"]]))} - rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), build_args = "--no-manual", error_on = "warning", check_dir = "check") - shell: Rscript {0} - - - name: Show testthat output - if: always() - run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true - shell: bash - - - name: Upload check results - if: failure() - uses: actions/upload-artifact@main + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::rcmdcheck + needs: check + + - uses: r-lib/actions/check-r-package@v2 with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check + upload-snapshots: true + + # - name: Install dependencies + # run: | + # remotes::install_deps(dependencies = TRUE) + # remotes::install_cran("rcmdcheck") + # shell: Rscript {0} + + # - name: Session info + # run: | + # options(width = 100) + # pkgs <- installed.packages()[, "Package"] + # sessioninfo::session_info(pkgs, include_base = TRUE) + # shell: Rscript {0} + + # - name: Check + # env: + # _R_CHECK_CRAN_INCOMING_: false + # _R_CHECK_FORCE_SUGGESTS_: false + # _R_CHECK_SYSTEM_CLOCK_: false + # IS_CHECK: true + # run: | + # os = .Platform$OS.type + # if (os == "windows" & R.version$major == 3) {remotes::install_version("rjags", version = "4-10")} + # jagshome = if (os == "windows") {readRegistry("SOFTWARE\\JAGS", maxdepth = 2, view = "32-bit")} + # if (os == "windows"){Sys.setenv(JAGS_HOME = try(jagshome[["JAGS-4.3.0"]][["InstallDir"]]))} + # rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), build_args = "--no-manual", error_on = "warning", check_dir = "check") + # shell: Rscript {0} + + # - name: Show testthat output + # if: always() + # run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true + # shell: bash + # + # - name: Upload check results + # if: failure() + # uses: actions/upload-artifact@main + # with: + # name: ${{ runner.os }}-r${{ matrix.config.r }}-results + # path: check From 01024810b7b831dac2c1ffe804b4644663fbe93b Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 17:18:24 +0200 Subject: [PATCH 119/176] update jags version in coverage action --- .github/workflows/test-coverage.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml index a012f35c..3fe4e293 100644 --- a/.github/workflows/test-coverage.yaml +++ b/.github/workflows/test-coverage.yaml @@ -54,12 +54,12 @@ jobs: - name: Download JAGS Windows if: runner.os == 'Windows' - run: (New-Object System.Net.WebClient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.0.exe', 'C:\JAGS-4.3.0.exe') + run: (New-Object System.Net.WebClient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe', 'C:\JAGS-4.3.1.exe') shell: powershell - name: Install JAGS Windows if: runner.os == 'Windows' - run: C:\JAGS-4.3.0.exe /S + run: C:\JAGS-4.3.1.exe /S shell: cmd - name: Install dependencies @@ -74,6 +74,6 @@ jobs: IS_CHECK: true run: | jagshome = readRegistry("SOFTWARE\\JAGS", maxdepth = 2, view = "32-bit") - Sys.setenv(JAGS_HOME = jagshome[["JAGS-4.3.0"]][["InstallDir"]]) + Sys.setenv(JAGS_HOME = jagshome[["JAGS-4.3.1"]][["InstallDir"]]) covr::codecov(quiet = FALSE, type = "all") shell: Rscript {0} From 8eec5e88284d3c14ec279b28a65adcadd627facc Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 17:18:39 +0200 Subject: [PATCH 120/176] update cran comments --- cran-comments.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/cran-comments.md b/cran-comments.md index 8944d802..17724adf 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -7,7 +7,8 @@ ### Test environments * local Windows 10, R 4.2.1 * windows server x64 (via github actions), R 4.1.2 -* ubuntu 20.04.3 LTS (via github actions), R 4.0.5, R 4.1.2, devel +* ubuntu 20.04.4 LTS (via github actions), R 4.1.3, R 4.2.1, devel +* mac-OS 10.16 (via github actions), R 4.2.1 * win-builder (oldrelease, devel and release) From 22e3ef575922c1d362e45b12d914e9e08b81d17a Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 17:41:36 +0200 Subject: [PATCH 121/176] remove unnecessary syntax --- .github/workflows/test-coverage.yaml | 18 +----------------- 1 file changed, 1 insertion(+), 17 deletions(-) diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml index 3fe4e293..53df6db4 100644 --- a/.github/workflows/test-coverage.yaml +++ b/.github/workflows/test-coverage.yaml @@ -37,21 +37,6 @@ jobs: key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - - name: Install system dependencies - if: runner.os == 'Linux' - env: - RHUB_PLATFORM: linux-x86_64-ubuntu-gcc - run: | - Rscript -e "remotes::install_github('r-hub/sysreqs')" - sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))") - sudo -s eval "$sysreqs" - - - name: Install JAGS macOS - if: runner.os == 'macOS' - run: | - rm '/usr/local/bin/gfortran' - brew install jags - - name: Download JAGS Windows if: runner.os == 'Windows' run: (New-Object System.Net.WebClient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe', 'C:\JAGS-4.3.1.exe') @@ -62,6 +47,7 @@ jobs: run: C:\JAGS-4.3.1.exe /S shell: cmd + - name: Install dependencies run: | install.packages(c("remotes")) @@ -73,7 +59,5 @@ jobs: env: IS_CHECK: true run: | - jagshome = readRegistry("SOFTWARE\\JAGS", maxdepth = 2, view = "32-bit") - Sys.setenv(JAGS_HOME = jagshome[["JAGS-4.3.1"]][["InstallDir"]]) covr::codecov(quiet = FALSE, type = "all") shell: Rscript {0} From 7e14f59333e562edca93c369cf57f50f4744a1e5 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 18:20:18 +0200 Subject: [PATCH 122/176] update snapshots --- tests/testthat/_snaps/clmm.md | 512 ++++----- tests/testthat/_snaps/glm.md | 1830 ++++++++++++++++----------------- tests/testthat/_snaps/glmm.md | 666 ++++++------ 3 files changed, 1504 insertions(+), 1504 deletions(-) diff --git a/tests/testthat/_snaps/clmm.md b/tests/testthat/_snaps/clmm.md index 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-7.850462e-17 -0.8164966 NA NA NA NA NA NA - 90.1 -11.7036651 2 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 90.2 -5.3838521 2 3 7.071068e-01 0.4082483 NA NA NA NA NA NA - 90.3 -4.1636999 4 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 88.2 -2.5095281 2 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 88.3 -16.3345673 NA 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 89 -11.0459647 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 90 -4.5610239 2 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA + 90.1 -11.7036651 2 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA + 90.2 -5.3838521 2 1 7.071068e-01 0.4082483 NA NA NA NA NA NA + 90.3 -4.1636999 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA 91 -7.1462503 2 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 91.1 -12.8374475 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA + 91.1 -12.8374475 NA NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA 91.2 -18.2576707 3 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 92 -6.4119222 2 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA - 93 5.2122168 3 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 93.1 3.1211725 2 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA - 93.2 -3.6841177 3 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 93.3 2.6223542 2 2 7.071068e-01 0.4082483 NA NA NA NA NA NA - 93.4 -11.1877696 4 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 94 -6.9602492 NA 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA + 92 -6.4119222 2 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA + 93 5.2122168 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 93.1 3.1211725 2 1 7.071068e-01 0.4082483 NA NA NA NA NA NA + 93.2 -3.6841177 3 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 93.3 2.6223542 2 1 7.071068e-01 0.4082483 NA NA NA NA NA NA + 93.4 -11.1877696 4 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 94 -6.9602492 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA 94.1 -7.4318416 2 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA - 94.2 -4.3498045 NA NA -7.071068e-01 0.4082483 NA NA NA NA NA NA - 94.3 -11.6340088 3 4 7.071068e-01 0.4082483 NA NA NA NA NA NA + 94.2 -4.3498045 NA 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 94.3 -11.6340088 3 2 7.071068e-01 0.4082483 NA NA NA NA NA NA 94.4 -12.9357964 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA 94.5 -14.7648530 3 1 7.071068e-01 0.4082483 NA NA NA NA NA NA - 95 -12.8849309 NA 4 7.071068e-01 0.4082483 NA NA NA NA NA NA - 95.1 -9.7451502 2 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA - 95.2 -0.8535063 3 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA + 95 -12.8849309 NA 3 7.071068e-01 0.4082483 NA NA NA NA NA NA + 95.1 -9.7451502 2 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 95.2 -0.8535063 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA 96 -4.9139832 3 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA 96.1 -3.9582653 NA 3 7.071068e-01 0.4082483 NA NA NA NA NA NA - 96.2 -9.6555492 4 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA - 96.3 -11.8690793 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 96.4 -11.0224373 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA - 96.5 -10.9530403 1 1 7.071068e-01 0.4082483 NA NA NA NA NA NA - 97 -9.8540471 2 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA - 97.1 -19.2262840 1 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA + 96.2 -9.6555492 4 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 96.3 -11.8690793 3 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 96.4 -11.0224373 NA 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA + 96.5 -10.9530403 1 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA + 97 -9.8540471 2 1 7.071068e-01 0.4082483 NA NA NA NA NA NA + 97.1 -19.2262840 1 2 7.071068e-01 0.4082483 NA NA NA NA NA NA 98 -11.9651231 2 2 7.071068e-01 0.4082483 NA NA NA NA NA NA - 98.1 -2.6515128 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 98.2 -12.2606382 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 98.1 -2.6515128 1 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 98.2 -12.2606382 3 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA 99 -11.4720500 NA 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA 99.1 -14.0596866 NA 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA - 99.2 -17.3939469 4 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 100 1.1005874 1 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 100.1 -3.8226248 NA 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA - 100.2 -0.9123182 1 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA - 100.3 -15.8389474 4 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA - 100.4 -12.8093826 1 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA + 99.2 -17.3939469 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 100 1.1005874 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 100.1 -3.8226248 NA 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA + 100.2 -0.9123182 1 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA + 100.3 -15.8389474 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA + 100.4 -12.8093826 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA time 1 0.5090421822 1.1 0.6666076288 diff --git a/tests/testthat/_snaps/glm.md b/tests/testthat/_snaps/glm.md index 460597df..2e299c3a 100644 --- a/tests/testthat/_snaps/glm.md +++ b/tests/testthat/_snaps/glm.md @@ -1491,106 +1491,106 @@ $m0f1 $m0f1$M_lvlone Be1 (Intercept) - 1 0.45229748 1 - 2 0.31390911 1 - 3 0.88676975 1 - 4 0.67928795 1 - 5 0.86107032 1 - 6 0.52000423 1 - 7 0.34719105 1 - 8 0.18045201 1 - 9 0.34612871 1 - 10 0.55633517 1 - 11 0.73374173 1 - 12 0.56285329 1 - 13 0.23650300 1 - 14 0.66121522 1 - 15 0.16109531 1 - 16 0.40954364 1 - 17 0.42162203 1 - 18 0.12474473 1 - 19 0.71505383 1 - 20 0.34912863 1 - 21 0.41725877 1 - 22 0.80141280 1 - 23 0.65400132 1 - 24 0.47291860 1 - 25 0.62568814 1 - 26 0.59851269 1 - 27 0.84062107 1 - 28 0.56853915 1 - 29 0.62393228 1 - 30 0.58602381 1 - 31 0.40679332 1 - 32 0.52438550 1 - 33 0.83736405 1 - 34 0.29409196 1 - 35 0.53040245 1 - 36 0.79610577 1 - 37 0.44161232 1 - 38 0.25857604 1 - 39 0.29529933 1 - 40 0.40368672 1 - 41 0.29998904 1 - 42 0.43520654 1 - 43 0.39785283 1 - 44 0.45436466 1 - 45 0.60038178 1 - 46 0.65127155 1 - 47 0.84266680 1 - 48 0.48283334 1 - 49 0.42047107 1 - 50 0.81321020 1 - 51 0.84609933 1 - 52 0.51080932 1 - 53 0.41759420 1 - 54 0.13916586 1 - 55 0.80396498 1 - 56 0.30204420 1 - 57 0.24531399 1 - 58 0.95451262 1 - 59 0.55848637 1 - 60 0.49168611 1 - 61 0.06109871 1 - 62 0.47507304 1 - 63 0.72632012 1 - 64 0.60201521 1 - 65 0.71347511 1 - 66 0.87907845 1 - 67 0.76298384 1 - 68 0.37548965 1 - 69 0.66911392 1 - 70 0.45055803 1 - 71 0.36863648 1 - 72 0.05438441 1 - 73 0.31224450 1 - 74 0.61959148 1 - 75 0.89734187 1 - 76 0.62262255 1 - 77 0.65026635 1 - 78 0.27569783 1 - 79 0.54057426 1 - 80 0.11123008 1 - 81 0.61898135 1 - 82 0.66747098 1 - 83 0.54624592 1 - 84 0.66835038 1 - 85 0.57729553 1 - 86 0.68145411 1 - 87 0.86323982 1 - 88 0.75256936 1 - 89 0.50816526 1 - 90 0.24704239 1 - 91 0.60301841 1 - 92 0.40125582 1 - 93 0.81660199 1 - 94 0.16699428 1 - 95 0.39753376 1 - 96 0.60396374 1 - 97 0.46213848 1 - 98 0.35293768 1 - 99 0.42440379 1 - 100 0.65238427 1 + 1 0.69649948 1 + 2 0.56085128 1 + 3 0.35796663 1 + 4 0.53961336 1 + 5 0.06191042 1 + 6 0.51256785 1 + 7 0.13154723 1 + 8 0.35032766 1 + 9 0.21796890 1 + 10 0.10476230 1 + 11 0.66083800 1 + 12 0.66884267 1 + 13 0.69840279 1 + 14 0.50398472 1 + 15 0.52807655 1 + 16 0.40135087 1 + 17 0.45554802 1 + 18 0.68717635 1 + 19 0.35880655 1 + 20 0.36341035 1 + 21 0.71468563 1 + 22 0.44558172 1 + 23 0.33262526 1 + 24 0.66812751 1 + 25 0.23180310 1 + 26 0.37786624 1 + 27 0.88834598 1 + 28 0.46487057 1 + 29 0.47018802 1 + 30 0.91617346 1 + 31 0.67589111 1 + 32 0.61623852 1 + 33 0.44182889 1 + 34 0.29868153 1 + 35 0.44235110 1 + 36 0.72557250 1 + 37 0.74809277 1 + 38 0.26452559 1 + 39 0.41597215 1 + 40 0.29080530 1 + 41 0.80342568 1 + 42 0.76614332 1 + 43 0.29734466 1 + 44 0.42809509 1 + 45 0.12861202 1 + 46 0.44369392 1 + 47 0.35290028 1 + 48 0.88288407 1 + 49 0.37880332 1 + 50 0.60663793 1 + 51 0.15505292 1 + 52 0.65796074 1 + 53 0.63416487 1 + 54 0.83040459 1 + 55 0.64947589 1 + 56 0.67541381 1 + 57 0.53637356 1 + 58 0.39157422 1 + 59 0.88168026 1 + 60 0.32582606 1 + 61 0.64492753 1 + 62 0.34804110 1 + 63 0.49241010 1 + 64 0.43387493 1 + 65 0.21806182 1 + 66 0.60021691 1 + 67 0.30567313 1 + 68 0.22476988 1 + 69 0.23155216 1 + 70 0.29610794 1 + 71 0.83435168 1 + 72 0.65543408 1 + 73 0.59684715 1 + 74 0.80640183 1 + 75 0.52288624 1 + 76 0.41546840 1 + 77 0.44756212 1 + 78 0.68093413 1 + 79 0.29261828 1 + 80 0.21008516 1 + 81 0.44710869 1 + 82 0.70470991 1 + 83 0.31300581 1 + 84 0.44774544 1 + 85 0.68031201 1 + 86 0.44456865 1 + 87 0.79031803 1 + 88 0.22231438 1 + 89 0.30114327 1 + 90 0.45339193 1 + 91 0.35526875 1 + 92 0.68684691 1 + 93 0.81430167 1 + 94 0.60104343 1 + 95 0.82012448 1 + 96 0.55669948 1 + 97 0.76622465 1 + 98 0.50112270 1 + 99 0.53468983 1 + 100 0.58249327 1 $m0f1$mu_reg_beta [1] 0 @@ -2211,112 +2211,112 @@ $m1f $m1f$M_lvlone Be1 (Intercept) C1 - 1 0.45229748 1 1.410531 - 2 0.31390911 1 1.434183 - 3 0.88676975 1 1.430994 - 4 0.67928795 1 1.453096 - 5 0.86107032 1 1.438344 - 6 0.52000423 1 1.453207 - 7 0.34719105 1 1.425176 - 8 0.18045201 1 1.437908 - 9 0.34612871 1 1.416911 - 10 0.55633517 1 1.448638 - 11 0.73374173 1 1.428375 - 12 0.56285329 1 1.450130 - 13 0.23650300 1 1.420545 - 14 0.66121522 1 1.423005 - 15 0.16109531 1 1.435902 - 16 0.40954364 1 1.423901 - 17 0.42162203 1 1.457208 - 18 0.12474473 1 1.414280 - 19 0.71505383 1 1.443383 - 20 0.34912863 1 1.434954 - 21 0.41725877 1 1.429499 - 22 0.80141280 1 1.441897 - 23 0.65400132 1 1.423713 - 24 0.47291860 1 1.435395 - 25 0.62568814 1 1.425944 - 26 0.59851269 1 1.437115 - 27 0.84062107 1 1.441326 - 28 0.56853915 1 1.422953 - 29 0.62393228 1 1.437797 - 30 0.58602381 1 1.472121 - 31 0.40679332 1 1.421782 - 32 0.52438550 1 1.457672 - 33 0.83736405 1 1.430842 - 34 0.29409196 1 1.431523 - 35 0.53040245 1 1.421395 - 36 0.79610577 1 1.434496 - 37 0.44161232 1 1.425383 - 38 0.25857604 1 1.421802 - 39 0.29529933 1 1.430094 - 40 0.40368672 1 1.447621 - 41 0.29998904 1 1.434797 - 42 0.43520654 1 1.446091 - 43 0.39785283 1 1.445306 - 44 0.45436466 1 1.448783 - 45 0.60038178 1 1.450617 - 46 0.65127155 1 1.415055 - 47 0.84266680 1 1.436590 - 48 0.48283334 1 1.433938 - 49 0.42047107 1 1.414941 - 50 0.81321020 1 1.421807 - 51 0.84609933 1 1.453203 - 52 0.51080932 1 1.452129 - 53 0.41759420 1 1.431510 - 54 0.13916586 1 1.430082 - 55 0.80396498 1 1.443492 - 56 0.30204420 1 1.436460 - 57 0.24531399 1 1.418119 - 58 0.95451262 1 1.434971 - 59 0.55848637 1 1.445599 - 60 0.49168611 1 1.437097 - 61 0.06109871 1 1.428360 - 62 0.47507304 1 1.440550 - 63 0.72632012 1 1.443014 - 64 0.60201521 1 1.424298 - 65 0.71347511 1 1.448823 - 66 0.87907845 1 1.425834 - 67 0.76298384 1 1.427102 - 68 0.37548965 1 1.414240 - 69 0.66911392 1 1.456218 - 70 0.45055803 1 1.470594 - 71 0.36863648 1 1.425058 - 72 0.05438441 1 1.432371 - 73 0.31224450 1 1.441656 - 74 0.61959148 1 1.434952 - 75 0.89734187 1 1.402860 - 76 0.62262255 1 1.453363 - 77 0.65026635 1 1.432909 - 78 0.27569783 1 1.435103 - 79 0.54057426 1 1.434462 - 80 0.11123008 1 1.434661 - 81 0.61898135 1 1.445881 - 82 0.66747098 1 1.442548 - 83 0.54624592 1 1.430097 - 84 0.66835038 1 1.430119 - 85 0.57729553 1 1.430315 - 86 0.68145411 1 1.437584 - 87 0.86323982 1 1.409738 - 88 0.75256936 1 1.422388 - 89 0.50816526 1 1.422509 - 90 0.24704239 1 1.439432 - 91 0.60301841 1 1.430175 - 92 0.40125582 1 1.418002 - 93 0.81660199 1 1.423812 - 94 0.16699428 1 1.423473 - 95 0.39753376 1 1.434412 - 96 0.60396374 1 1.450844 - 97 0.46213848 1 1.433371 - 98 0.35293768 1 1.444378 - 99 0.42440379 1 1.422523 - 100 0.65238427 1 1.410394 + 1 0.69649948 1 1.410531 + 2 0.56085128 1 1.434183 + 3 0.35796663 1 1.430994 + 4 0.53961336 1 1.453096 + 5 0.06191042 1 1.438344 + 6 0.51256785 1 1.453207 + 7 0.13154723 1 1.425176 + 8 0.35032766 1 1.437908 + 9 0.21796890 1 1.416911 + 10 0.10476230 1 1.448638 + 11 0.66083800 1 1.428375 + 12 0.66884267 1 1.450130 + 13 0.69840279 1 1.420545 + 14 0.50398472 1 1.423005 + 15 0.52807655 1 1.435902 + 16 0.40135087 1 1.423901 + 17 0.45554802 1 1.457208 + 18 0.68717635 1 1.414280 + 19 0.35880655 1 1.443383 + 20 0.36341035 1 1.434954 + 21 0.71468563 1 1.429499 + 22 0.44558172 1 1.441897 + 23 0.33262526 1 1.423713 + 24 0.66812751 1 1.435395 + 25 0.23180310 1 1.425944 + 26 0.37786624 1 1.437115 + 27 0.88834598 1 1.441326 + 28 0.46487057 1 1.422953 + 29 0.47018802 1 1.437797 + 30 0.91617346 1 1.472121 + 31 0.67589111 1 1.421782 + 32 0.61623852 1 1.457672 + 33 0.44182889 1 1.430842 + 34 0.29868153 1 1.431523 + 35 0.44235110 1 1.421395 + 36 0.72557250 1 1.434496 + 37 0.74809277 1 1.425383 + 38 0.26452559 1 1.421802 + 39 0.41597215 1 1.430094 + 40 0.29080530 1 1.447621 + 41 0.80342568 1 1.434797 + 42 0.76614332 1 1.446091 + 43 0.29734466 1 1.445306 + 44 0.42809509 1 1.448783 + 45 0.12861202 1 1.450617 + 46 0.44369392 1 1.415055 + 47 0.35290028 1 1.436590 + 48 0.88288407 1 1.433938 + 49 0.37880332 1 1.414941 + 50 0.60663793 1 1.421807 + 51 0.15505292 1 1.453203 + 52 0.65796074 1 1.452129 + 53 0.63416487 1 1.431510 + 54 0.83040459 1 1.430082 + 55 0.64947589 1 1.443492 + 56 0.67541381 1 1.436460 + 57 0.53637356 1 1.418119 + 58 0.39157422 1 1.434971 + 59 0.88168026 1 1.445599 + 60 0.32582606 1 1.437097 + 61 0.64492753 1 1.428360 + 62 0.34804110 1 1.440550 + 63 0.49241010 1 1.443014 + 64 0.43387493 1 1.424298 + 65 0.21806182 1 1.448823 + 66 0.60021691 1 1.425834 + 67 0.30567313 1 1.427102 + 68 0.22476988 1 1.414240 + 69 0.23155216 1 1.456218 + 70 0.29610794 1 1.470594 + 71 0.83435168 1 1.425058 + 72 0.65543408 1 1.432371 + 73 0.59684715 1 1.441656 + 74 0.80640183 1 1.434952 + 75 0.52288624 1 1.402860 + 76 0.41546840 1 1.453363 + 77 0.44756212 1 1.432909 + 78 0.68093413 1 1.435103 + 79 0.29261828 1 1.434462 + 80 0.21008516 1 1.434661 + 81 0.44710869 1 1.445881 + 82 0.70470991 1 1.442548 + 83 0.31300581 1 1.430097 + 84 0.44774544 1 1.430119 + 85 0.68031201 1 1.430315 + 86 0.44456865 1 1.437584 + 87 0.79031803 1 1.409738 + 88 0.22231438 1 1.422388 + 89 0.30114327 1 1.422509 + 90 0.45339193 1 1.439432 + 91 0.35526875 1 1.430175 + 92 0.68684691 1 1.418002 + 93 0.81430167 1 1.423812 + 94 0.60104343 1 1.423473 + 95 0.82012448 1 1.434412 + 96 0.55669948 1 1.450844 + 97 0.76622465 1 1.433371 + 98 0.50112270 1 1.444378 + 99 0.53468983 1 1.422523 + 100 0.58249327 1 1.410394 $m1f$spM_lvlone - center scale - Be1 0.5234394 0.21256350 - (Intercept) NA NA - C1 1.4341005 0.01299651 + center scale + Be1 0.503988 0.20498987 + (Intercept) NA NA + C1 1.434101 0.01299651 $m1f$mu_reg_beta [1] 0 @@ -2589,54 +2589,54 @@ 1 0.9364352 0.144065882 1 2 0.8943541 0.032778478 1 3 0.2868460 0.343008492 1 - 4 NA -0.361887858 1 + 4 0.9068418 -0.361887858 1 5 0.7621346 -0.389600647 1 - 6 0.5858621 -0.205306841 1 - 7 0.7194403 0.079434830 1 + 6 NA -0.205306841 1 + 7 NA 0.079434830 1 8 0.7593154 -0.331246757 1 9 0.5863705 -0.329638800 1 - 10 NA 0.167597533 1 + 10 0.7342586 0.167597533 1 11 0.7218028 0.860207989 1 - 12 0.7241254 0.022730640 1 - 13 NA 0.217171172 1 + 12 NA 0.022730640 1 + 13 0.7200126 0.217171172 1 14 0.5289014 -0.403002412 1 15 0.7322482 0.087369742 1 16 0.7462471 -0.183870429 1 17 0.9119922 -0.194577002 1 - 18 0.6262513 -0.349718516 1 + 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0.8079308 NA 1 - 80 0.5214822 -0.002032440 1 + 80 NA -0.002032440 1 81 NA -0.154246160 1 - 82 0.8332107 0.140201825 1 + 82 NA 0.140201825 1 83 0.4544158 -0.141417121 1 84 0.6482660 NA 1 85 0.7272109 -0.021285339 1 - 86 0.7302426 -0.010196306 1 + 86 NA -0.010196306 1 87 0.6768061 -0.089747520 1 88 0.8115758 -0.083699898 1 - 89 0.9775567 -0.044061996 1 + 89 NA -0.044061996 1 90 0.6408465 -0.209291697 1 91 0.5917453 0.639036426 1 92 0.7224845 0.094698299 1 @@ -2683,13 +2683,13 @@ 95 0.7305821 0.125295503 1 96 0.9696445 0.213084904 1 97 0.7087457 -0.161914659 1 - 98 NA -0.034767685 1 - 99 0.9084899 -0.320681689 1 + 98 0.9964080 -0.034767685 1 + 99 NA -0.320681689 1 100 0.9296776 0.058192962 1 $m2c$spM_lvlone center scale - L1mis 0.72626070 0.1536447 + L1mis 0.72862466 0.1577261 C2 -0.06490582 0.3331735 (Intercept) NA NA @@ -2722,7 +2722,7 @@ $m2d$M_lvlone P2 C2 (Intercept) 1 0 0.144065882 1 - 2 2 0.032778478 1 + 2 NA 0.032778478 1 3 1 0.343008492 1 4 1 -0.361887858 1 5 0 -0.389600647 1 @@ -2730,7 +2730,7 @@ 7 1 0.079434830 1 8 0 -0.331246757 1 9 2 -0.329638800 1 - 10 0 0.167597533 1 + 10 NA 0.167597533 1 11 3 0.860207989 1 12 0 0.022730640 1 13 5 0.217171172 1 @@ -2742,23 +2742,23 @@ 19 NA -0.508781244 1 20 3 0.494883111 1 21 3 0.258041067 1 - 22 4 -0.922621989 1 + 22 NA -0.922621989 1 23 6 0.431254949 1 24 4 -0.294218881 1 25 NA -0.425548895 1 26 1 0.057176054 1 27 1 0.289090158 1 28 2 -0.473079489 1 - 29 NA -0.385664863 1 - 30 1 -0.154780107 1 + 29 2 -0.385664863 1 + 30 NA -0.154780107 1 31 5 0.100536296 1 - 32 NA 0.634791958 1 + 32 2 0.634791958 1 33 0 -0.387252617 1 34 2 -0.181741088 1 - 35 4 -0.311562695 1 + 35 NA -0.311562695 1 36 2 -0.044115907 1 37 4 -0.657409991 1 - 38 NA 0.159577214 1 + 38 2 0.159577214 1 39 2 -0.460416933 1 40 NA NA 1 41 2 -0.248909867 1 @@ -2767,56 +2767,56 @@ 44 2 0.066648592 1 45 1 -0.276108719 1 46 2 -0.179737577 1 - 47 3 0.181190937 1 + 47 NA 0.181190937 1 48 2 -0.453871693 1 49 NA 0.448629602 1 50 2 -0.529811821 1 - 51 NA -0.028304571 1 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0.7328840 0.205092215 1 75 0.7946099 -0.136596858 1 - 76 NA -0.500179043 1 + 76 0.7734810 -0.500179043 1 77 0.5296147 0.527352086 1 78 0.7723288 0.022742250 1 79 0.8079308 NA 1 - 80 0.5214822 -0.002032440 1 + 80 NA -0.002032440 1 81 NA -0.154246160 1 - 82 0.8332107 0.140201825 1 + 82 NA 0.140201825 1 83 0.4544158 -0.141417121 1 84 0.6482660 NA 1 85 0.7272109 -0.021285339 1 - 86 0.7302426 -0.010196306 1 + 86 NA -0.010196306 1 87 0.6768061 -0.089747520 1 88 0.8115758 -0.083699898 1 - 89 0.9775567 -0.044061996 1 + 89 NA -0.044061996 1 90 0.6408465 -0.209291697 1 91 0.5917453 0.639036426 1 92 0.7224845 0.094698299 1 @@ -2947,13 +2947,13 @@ 95 0.7305821 0.125295503 1 96 0.9696445 0.213084904 1 97 0.7087457 -0.161914659 1 - 98 NA -0.034767685 1 - 99 0.9084899 -0.320681689 1 + 98 0.9964080 -0.034767685 1 + 99 NA -0.320681689 1 100 0.9296776 0.058192962 1 $m2e$spM_lvlone center scale - L1mis 0.72626070 0.1536447 + L1mis 0.72862466 0.1577261 C2 -0.06490582 0.3331735 (Intercept) NA NA @@ -2973,110 +2973,110 @@ $m2f $m2f$M_lvlone Be2 C2 (Intercept) - 1 0.70995633 0.144065882 1 - 2 0.65930815 0.032778478 1 - 3 NA 0.343008492 1 - 4 0.76377664 -0.361887858 1 - 5 0.57143534 -0.389600647 1 - 6 NA -0.205306841 1 - 7 0.94878453 0.079434830 1 - 8 0.66316162 -0.331246757 1 - 9 0.33529773 -0.329638800 1 - 10 0.54648836 0.167597533 1 - 11 0.41960544 0.860207989 1 - 12 0.43175258 0.022730640 1 - 13 0.28221450 0.217171172 1 - 14 0.52917815 -0.403002412 1 - 15 NA 0.087369742 1 - 16 0.25397143 -0.183870429 1 - 17 0.03793703 -0.194577002 1 - 18 0.43662405 -0.349718516 1 - 19 NA -0.508781244 1 - 20 0.64434515 0.494883111 1 - 21 0.51477949 0.258041067 1 + 1 0.13821330 0.144065882 1 + 2 NA 0.032778478 1 + 3 0.85221266 0.343008492 1 + 4 0.61517266 -0.361887858 1 + 5 0.56718424 -0.389600647 1 + 6 0.16127199 -0.205306841 1 + 7 NA 0.079434830 1 + 8 0.51062047 -0.331246757 1 + 9 0.29560086 -0.329638800 1 + 10 0.43261394 0.167597533 1 + 11 0.54537238 0.860207989 1 + 12 0.36458613 0.022730640 1 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0.639036426 1 NA 92 1.418002 NA 6 0.7224845 0.094698299 1 NA 93 1.423812 1 0 0.4501596 -0.055510622 1 NA 94 1.423473 1 4 0.5190455 -0.421318463 1 NA 95 1.434412 1 3 0.7305821 0.125295503 1 NA - 96 1.450844 1 NA 0.9696445 0.213084904 1 NA + 96 1.450844 1 3 0.9696445 0.213084904 1 NA 97 1.433371 NA 3 0.7087457 -0.161914659 1 NA - 98 1.444378 1 3 NA -0.034767685 1 NA - 99 1.422523 0 5 0.9084899 -0.320681689 1 NA + 98 1.444378 1 3 0.9964080 -0.034767685 1 NA + 99 1.422523 0 5 NA -0.320681689 1 NA 100 1.410394 NA 2 0.9296776 0.058192962 1 NA $m3c$spM_lvlone center scale C1 1.43410054 0.01299651 B2 NA NA - P2 2.17500000 1.68969325 - L1mis 0.72626070 0.15364470 + P2 2.15000000 1.64663062 + L1mis 0.72862466 0.15772614 C2 -0.06490582 0.33317347 (Intercept) NA NA B21 NA NA @@ -3562,114 +3562,114 @@ $m3d $m3d$M_lvlone C1 B2 P2 L1mis Be2 C2 (Intercept) B21 - 1 1.410531 1 0 0.9364352 0.70995633 0.144065882 1 NA - 2 1.434183 1 2 0.8943541 0.65930815 0.032778478 1 NA - 3 1.430994 1 1 0.2868460 NA 0.343008492 1 NA - 4 1.453096 1 1 NA 0.76377664 -0.361887858 1 NA - 5 1.438344 1 0 0.7621346 0.57143534 -0.389600647 1 NA - 6 1.453207 NA 1 0.5858621 NA -0.205306841 1 NA - 7 1.425176 1 1 0.7194403 0.94878453 0.079434830 1 NA - 8 1.437908 1 0 0.7593154 0.66316162 -0.331246757 1 NA - 9 1.416911 1 2 0.5863705 0.33529773 -0.329638800 1 NA - 10 1.448638 NA 0 NA 0.54648836 0.167597533 1 NA - 11 1.428375 1 3 0.7218028 0.41960544 0.860207989 1 NA - 12 1.450130 1 0 0.7241254 0.43175258 0.022730640 1 NA - 13 1.420545 1 5 NA 0.28221450 0.217171172 1 NA - 14 1.423005 1 0 0.5289014 0.52917815 -0.403002412 1 NA - 15 1.435902 1 1 0.7322482 NA 0.087369742 1 NA - 16 1.423901 1 4 0.7462471 0.25397143 -0.183870429 1 NA - 17 1.457208 1 NA 0.9119922 0.03793703 -0.194577002 1 NA - 18 1.414280 1 1 0.6262513 0.43662405 -0.349718516 1 NA - 19 1.443383 NA NA NA NA -0.508781244 1 NA - 20 1.434954 NA 3 0.7173364 0.64434515 0.494883111 1 NA - 21 1.429499 1 3 0.7288999 0.51477949 0.258041067 1 NA - 22 1.441897 NA 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0.22231438 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204 + 89 0.30114327 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204 + 90 0.45339193 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204 + 91 0.35526875 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068 + 92 0.68684691 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068 + 93 0.81430167 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068 + 94 0.60104343 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068 + 95 0.82012448 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068 + 96 0.55669948 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204 + 97 0.76622465 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068 + 98 0.50112270 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204 + 99 0.53468983 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204 + 100 0.58249327 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204 $m5f1$spM_lvlone center scale - Be1 0.52343941 0.2125635 + Be1 0.50398804 0.2049899 B2 NA NA C2 -0.06490582 0.3331735 (Intercept) NA NA @@ -15560,7 +15560,7 @@ Number and proportion of complete cases: # % - lvlone 77 77 + lvlone 78 78 Number and proportion of missing values: # NA % NA @@ -15680,7 +15680,7 @@ Number and proportion of complete cases: # % - lvlone 36 36 + lvlone 46 46 Number and proportion of missing values: # NA % NA @@ -15727,7 +15727,7 @@ Number and proportion of complete cases: # % - lvlone 48 48 + lvlone 55 55 Number and proportion of missing values: # NA % NA @@ -15773,7 +15773,7 @@ Number and proportion of complete cases: # % - lvlone 48 48 + lvlone 55 55 Number and proportion of missing values: # NA % NA @@ -15821,7 +15821,7 @@ Number and proportion of complete cases: # % - lvlone 36 36 + lvlone 46 46 Number and proportion of missing values: # NA % NA diff --git a/tests/testthat/_snaps/glmm.md b/tests/testthat/_snaps/glmm.md index bf6b7f07..472cdff2 100644 --- a/tests/testthat/_snaps/glmm.md +++ b/tests/testthat/_snaps/glmm.md @@ -10583,7 +10583,7 @@ 1.1 0.79402906 -0.08061445 1.2 0.53603334 -0.26523782 1.3 0.24129804 -0.30260393 - 2 0.36962247 -0.33443795 + 2 NA -0.33443795 2.1 0.31668065 -0.11819800 2.2 0.37114414 -0.31532280 3 0.54680608 -0.12920657 @@ -10606,14 +10606,14 @@ 8.2 0.30550233 NA 8.3 0.88029778 -0.35148972 8.4 0.20200392 0.03661023 - 8.5 0.90954076 -0.08424534 + 8.5 NA -0.08424534 9 1.12218535 NA 9.1 0.57911079 -0.43509340 9.2 0.81350994 -0.22527490 10 0.32744766 NA 10.1 0.62912282 NA 11 0.92140073 -0.08587475 - 11.1 NA -0.06157340 + 11.1 0.16012129 -0.06157340 11.2 0.16166775 -0.12436018 11.3 0.14979756 -0.21377934 11.4 0.46855190 -0.32208329 @@ -10623,7 +10623,7 @@ 14 0.80630788 -0.28992072 14.1 0.35697552 NA 14.2 0.21330192 NA - 14.3 0.18519862 -0.21979936 + 14.3 NA -0.21979936 15 0.30769119 NA 15.1 0.28349746 -0.29092263 15.2 0.64618365 -0.19392239 @@ -10644,8 +10644,8 @@ 19.1 0.13338947 -0.15815241 19.2 0.41662218 -0.14717437 19.3 0.53670855 -0.21271374 - 20 NA -0.22087628 - 20.1 1.67304138 NA + 20 0.41688567 -0.22087628 + 20.1 NA NA 20.2 0.81634101 -0.30127439 20.3 0.39232496 -0.11782590 20.4 0.57925554 -0.19857957 @@ -10653,24 +10653,24 @@ 21 0.24759801 -0.31407992 21.1 0.34052205 -0.12424941 21.2 0.03905058 -0.27672716 - 22 NA -0.23790593 + 22 0.48605351 -0.23790593 22.1 0.43761071 -0.15996535 23 0.47599712 -0.18236682 23.1 0.47680301 -0.20823302 24 0.51696505 -0.29026416 25 0.59392591 -0.36139273 25.1 0.74010330 -0.19571118 - 25.2 1.31733398 -0.21379355 + 25.2 NA -0.21379355 25.3 0.73081722 -0.33876012 25.4 0.29274286 NA 25.5 0.74425342 -0.04068446 26 0.20974346 -0.16846716 - 26.1 0.55999618 -0.10440642 + 26.1 NA -0.10440642 26.2 0.22908815 -0.26884827 26.3 0.41880799 NA 27 0.10097167 -0.19520794 - 27.1 0.49169944 -0.17622638 - 28 0.27459710 -0.32164962 + 27.1 NA -0.17622638 + 28 NA -0.32164962 28.1 0.56052750 -0.27003852 28.2 0.15301800 -0.07235801 28.3 0.27802542 -0.13462982 @@ -10693,21 +10693,21 @@ 34.2 0.19103604 -0.24278671 34.3 NA -0.19971833 35 0.66303137 NA - 35.1 0.71422833 -0.24165780 - 35.2 0.52721825 NA - 36 NA -0.49062180 - 36.1 0.31373962 -0.25651700 + 35.1 NA -0.24165780 + 35.2 NA NA + 36 0.93843318 -0.49062180 + 36.1 NA -0.25651700 36.2 0.29886676 NA 36.3 0.22616598 -0.30401274 36.4 0.53849566 NA 37 1.68107300 -0.15276529 37.1 1.13777638 -0.30016169 37.2 0.26931933 0.06809545 - 38 0.68769326 -0.11218486 + 38 NA -0.11218486 39 0.14395367 -0.38072211 39.1 0.36454923 -0.32094428 39.2 1.03700002 NA - 39.3 NA -0.40173480 + 39.3 0.41320585 -0.40173480 39.4 0.20901554 -0.20041848 39.5 0.51603848 -0.26027990 40 0.33912363 -0.19751956 @@ -10717,7 +10717,7 @@ 41 0.25193679 -0.26096953 41.1 0.28760510 -0.23953874 41.2 0.45553197 -0.03079344 - 41.3 NA NA + 41.3 0.79237611 NA 41.4 0.12582175 NA 42 0.50079604 -0.16084527 42.1 0.61140760 -0.13812521 @@ -10726,7 +10726,7 @@ 43.2 0.15152473 -0.29253959 44 0.38806434 -0.22697597 44.1 1.11140786 NA - 44.2 NA NA + 44.2 0.39132534 NA 44.3 0.40934909 -0.40544012 45 0.68587067 -0.19274788 45.1 0.34530800 -0.34860483 @@ -10746,38 +10746,38 @@ 52 0.19007135 -0.07438732 52.1 0.75662940 -0.37537080 52.2 1.66104719 -0.24222066 - 52.3 0.12505840 -0.31520603 + 52.3 NA -0.31520603 52.4 0.18369708 -0.44619160 52.5 0.48689343 -0.11011682 53 0.31983157 -0.23278716 53.1 0.61569501 -0.28317264 - 53.2 0.43461540 -0.19517481 + 53.2 NA -0.19517481 54 1.90522418 -0.10122856 54.1 0.59484889 -0.28325504 54.2 1.47174857 -0.16753120 - 54.3 NA -0.22217672 - 54.4 NA -0.34609328 + 54.3 0.27307143 -0.22217672 + 54.4 0.81272938 -0.34609328 55 0.22735476 -0.32428190 55.1 0.54683512 -0.24235382 - 55.2 NA -0.24065814 + 55.2 1.03503777 -0.24065814 55.3 0.30169529 -0.23665476 55.4 0.36008059 NA 56 0.14193566 NA 56.1 0.65073539 -0.30357450 56.2 0.11338262 -0.51301630 56.3 0.16820103 -0.23743117 - 56.4 NA -0.17264917 + 56.4 0.27419110 -0.17264917 56.5 0.57110215 -0.39188329 57 0.85104054 -0.18501692 57.1 0.34733833 -0.27274841 57.2 1.44438762 NA 57.3 0.31836125 -0.09898509 - 58 NA -0.29901358 + 58 0.37456898 -0.29901358 58.1 0.22120158 -0.35390896 58.2 0.78885210 -0.16687336 58.3 0.10114937 -0.11784506 58.4 0.13385114 -0.05321983 - 58.5 1.00316675 -0.54457568 + 58.5 NA -0.54457568 59 0.13202156 -0.27255364 59.1 0.33371896 NA 60 0.35096579 NA @@ -10820,7 +10820,7 @@ 72.3 0.48937486 -0.09809866 72.4 0.64173822 -0.14240019 72.5 0.57609943 -0.14794204 - 73 NA -0.23509343 + 73 0.17393402 -0.23509343 74 0.23990575 -0.27963171 75 0.28469861 -0.12905034 76 0.71988630 0.04775562 @@ -10843,13 +10843,13 @@ 82.2 0.49936304 -0.20971187 83 0.21138572 -0.34228255 83.1 0.26403568 -0.34075730 - 83.2 NA -0.32503954 + 83.2 0.20311133 -0.32503954 83.3 1.16864671 NA 84 1.99179346 -0.20676741 84.1 1.52199460 -0.20310458 - 85 0.43196882 -0.12107593 + 85 NA -0.12107593 85.1 0.61458995 NA - 85.2 NA -0.32509207 + 85.2 0.07871196 -0.32509207 85.3 1.42315283 NA 85.4 0.97986129 -0.30730810 85.5 0.91792195 NA @@ -10859,7 +10859,7 @@ 86.3 0.09360826 -0.24454702 86.4 0.58301186 -0.12338992 86.5 0.39146055 -0.23976984 - 87 0.50923566 NA + 87 NA NA 87.1 0.66043624 -0.34366972 87.2 0.13267613 NA 88 0.10696344 -0.31563888 @@ -10867,7 +10867,7 @@ 88.2 0.48037889 -0.40311895 88.3 0.97755681 -0.12308715 89 0.70242369 -0.18527715 - 90 NA -0.25029126 + 90 0.40042977 -0.25029126 90.1 0.63975731 -0.26974303 90.2 0.33412775 -0.28804531 90.3 0.38399003 -0.19180615 @@ -10897,21 +10897,21 @@ 96.5 0.83843412 -0.28658893 97 0.47151154 -0.34484656 97.1 0.15596614 -0.35658805 - 98 NA -0.36913003 - 98.1 NA NA + 98 0.05179545 -0.36913003 + 98.1 0.47332096 NA 98.2 0.19706341 -0.17154225 99 0.22574556 -0.24753132 99.1 1.00732330 -0.27947829 99.2 0.09749127 -0.09033035 100 0.22857989 -0.17326698 100.1 0.39548654 NA - 100.2 0.25111467 -0.12072016 + 100.2 NA -0.12072016 100.3 0.32695372 -0.27657520 100.4 0.10043925 -0.14631556 $m2c$spM_lvlone center scale - L1mis 0.4916532 0.3533843 + L1mis 0.4818481 0.3462447 c2 -0.2237158 0.1059527 $m2c$mu_reg_norm @@ -11563,7 +11563,7 @@ 1.1 0.79402906 -0.08061445 1.2 0.53603334 -0.26523782 1.3 0.24129804 -0.30260393 - 2 0.36962247 -0.33443795 + 2 NA -0.33443795 2.1 0.31668065 -0.11819800 2.2 0.37114414 -0.31532280 3 0.54680608 -0.12920657 @@ -11586,14 +11586,14 @@ 8.2 0.30550233 NA 8.3 0.88029778 -0.35148972 8.4 0.20200392 0.03661023 - 8.5 0.90954076 -0.08424534 + 8.5 NA -0.08424534 9 1.12218535 NA 9.1 0.57911079 -0.43509340 9.2 0.81350994 -0.22527490 10 0.32744766 NA 10.1 0.62912282 NA 11 0.92140073 -0.08587475 - 11.1 NA -0.06157340 + 11.1 0.16012129 -0.06157340 11.2 0.16166775 -0.12436018 11.3 0.14979756 -0.21377934 11.4 0.46855190 -0.32208329 @@ -11603,7 +11603,7 @@ 14 0.80630788 -0.28992072 14.1 0.35697552 NA 14.2 0.21330192 NA - 14.3 0.18519862 -0.21979936 + 14.3 NA -0.21979936 15 0.30769119 NA 15.1 0.28349746 -0.29092263 15.2 0.64618365 -0.19392239 @@ -11624,8 +11624,8 @@ 19.1 0.13338947 -0.15815241 19.2 0.41662218 -0.14717437 19.3 0.53670855 -0.21271374 - 20 NA -0.22087628 - 20.1 1.67304138 NA + 20 0.41688567 -0.22087628 + 20.1 NA NA 20.2 0.81634101 -0.30127439 20.3 0.39232496 -0.11782590 20.4 0.57925554 -0.19857957 @@ -11633,24 +11633,24 @@ 21 0.24759801 -0.31407992 21.1 0.34052205 -0.12424941 21.2 0.03905058 -0.27672716 - 22 NA -0.23790593 + 22 0.48605351 -0.23790593 22.1 0.43761071 -0.15996535 23 0.47599712 -0.18236682 23.1 0.47680301 -0.20823302 24 0.51696505 -0.29026416 25 0.59392591 -0.36139273 25.1 0.74010330 -0.19571118 - 25.2 1.31733398 -0.21379355 + 25.2 NA -0.21379355 25.3 0.73081722 -0.33876012 25.4 0.29274286 NA 25.5 0.74425342 -0.04068446 26 0.20974346 -0.16846716 - 26.1 0.55999618 -0.10440642 + 26.1 NA -0.10440642 26.2 0.22908815 -0.26884827 26.3 0.41880799 NA 27 0.10097167 -0.19520794 - 27.1 0.49169944 -0.17622638 - 28 0.27459710 -0.32164962 + 27.1 NA -0.17622638 + 28 NA -0.32164962 28.1 0.56052750 -0.27003852 28.2 0.15301800 -0.07235801 28.3 0.27802542 -0.13462982 @@ -11673,21 +11673,21 @@ 34.2 0.19103604 -0.24278671 34.3 NA -0.19971833 35 0.66303137 NA - 35.1 0.71422833 -0.24165780 - 35.2 0.52721825 NA - 36 NA -0.49062180 - 36.1 0.31373962 -0.25651700 + 35.1 NA -0.24165780 + 35.2 NA NA + 36 0.93843318 -0.49062180 + 36.1 NA -0.25651700 36.2 0.29886676 NA 36.3 0.22616598 -0.30401274 36.4 0.53849566 NA 37 1.68107300 -0.15276529 37.1 1.13777638 -0.30016169 37.2 0.26931933 0.06809545 - 38 0.68769326 -0.11218486 + 38 NA -0.11218486 39 0.14395367 -0.38072211 39.1 0.36454923 -0.32094428 39.2 1.03700002 NA - 39.3 NA -0.40173480 + 39.3 0.41320585 -0.40173480 39.4 0.20901554 -0.20041848 39.5 0.51603848 -0.26027990 40 0.33912363 -0.19751956 @@ -11697,7 +11697,7 @@ 41 0.25193679 -0.26096953 41.1 0.28760510 -0.23953874 41.2 0.45553197 -0.03079344 - 41.3 NA NA + 41.3 0.79237611 NA 41.4 0.12582175 NA 42 0.50079604 -0.16084527 42.1 0.61140760 -0.13812521 @@ -11706,7 +11706,7 @@ 43.2 0.15152473 -0.29253959 44 0.38806434 -0.22697597 44.1 1.11140786 NA - 44.2 NA NA + 44.2 0.39132534 NA 44.3 0.40934909 -0.40544012 45 0.68587067 -0.19274788 45.1 0.34530800 -0.34860483 @@ -11726,38 +11726,38 @@ 52 0.19007135 -0.07438732 52.1 0.75662940 -0.37537080 52.2 1.66104719 -0.24222066 - 52.3 0.12505840 -0.31520603 + 52.3 NA -0.31520603 52.4 0.18369708 -0.44619160 52.5 0.48689343 -0.11011682 53 0.31983157 -0.23278716 53.1 0.61569501 -0.28317264 - 53.2 0.43461540 -0.19517481 + 53.2 NA -0.19517481 54 1.90522418 -0.10122856 54.1 0.59484889 -0.28325504 54.2 1.47174857 -0.16753120 - 54.3 NA -0.22217672 - 54.4 NA -0.34609328 + 54.3 0.27307143 -0.22217672 + 54.4 0.81272938 -0.34609328 55 0.22735476 -0.32428190 55.1 0.54683512 -0.24235382 - 55.2 NA -0.24065814 + 55.2 1.03503777 -0.24065814 55.3 0.30169529 -0.23665476 55.4 0.36008059 NA 56 0.14193566 NA 56.1 0.65073539 -0.30357450 56.2 0.11338262 -0.51301630 56.3 0.16820103 -0.23743117 - 56.4 NA -0.17264917 + 56.4 0.27419110 -0.17264917 56.5 0.57110215 -0.39188329 57 0.85104054 -0.18501692 57.1 0.34733833 -0.27274841 57.2 1.44438762 NA 57.3 0.31836125 -0.09898509 - 58 NA -0.29901358 + 58 0.37456898 -0.29901358 58.1 0.22120158 -0.35390896 58.2 0.78885210 -0.16687336 58.3 0.10114937 -0.11784506 58.4 0.13385114 -0.05321983 - 58.5 1.00316675 -0.54457568 + 58.5 NA 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0.97755681 -0.12308715 89 0.70242369 -0.18527715 - 90 NA -0.25029126 + 90 0.40042977 -0.25029126 90.1 0.63975731 -0.26974303 90.2 0.33412775 -0.28804531 90.3 0.38399003 -0.19180615 @@ -11877,21 +11877,21 @@ 96.5 0.83843412 -0.28658893 97 0.47151154 -0.34484656 97.1 0.15596614 -0.35658805 - 98 NA -0.36913003 - 98.1 NA NA + 98 0.05179545 -0.36913003 + 98.1 0.47332096 NA 98.2 0.19706341 -0.17154225 99 0.22574556 -0.24753132 99.1 1.00732330 -0.27947829 99.2 0.09749127 -0.09033035 100 0.22857989 -0.17326698 100.1 0.39548654 NA - 100.2 0.25111467 -0.12072016 + 100.2 NA -0.12072016 100.3 0.32695372 -0.27657520 100.4 0.10043925 -0.14631556 $m2e$spM_lvlone center scale - L1mis 0.4916532 0.3533843 + L1mis 0.4818481 0.3462447 c2 -0.2237158 0.1059527 $m2e$mu_reg_norm @@ -13505,7 +13505,7 @@ 1.1 0.79402906 1.2 0.53603334 1.3 0.24129804 - 2 0.36962247 + 2 NA 2.1 0.31668065 2.2 0.37114414 3 0.54680608 @@ -13528,14 +13528,14 @@ 8.2 0.30550233 8.3 0.88029778 8.4 0.20200392 - 8.5 0.90954076 + 8.5 NA 9 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2.394334e-06 1.2268695927 2.4451737676 NA 26.2 1 0.22908815 4.510972e-08 0.3678294939 3.5988757887 NA 26.3 0 0.41880799 3.657318e-11 0.5948516018 4.1822362854 NA 27 1 0.10097167 NA -0.3342844147 3.6955824879 NA - 27.1 1 0.49169944 8.874134e-06 -0.4835141229 4.2451434687 NA - 28 1 0.27459710 3.673907e-06 -0.7145915499 0.5746519344 NA + 27.1 1 NA 8.874134e-06 -0.4835141229 4.2451434687 NA + 28 1 NA 3.673907e-06 -0.7145915499 0.5746519344 NA 28.1 0 0.56052750 4.541426e-04 0.5063671955 2.7943964268 NA 28.2 1 0.15301800 2.697966e-12 -0.2067413142 4.2108539480 NA 28.3 1 0.27802542 NA 0.1196789973 4.4705521734 NA @@ -18463,21 +18463,21 @@ 34.2 1 0.19103604 7.883166e-09 -0.0897224473 3.9072251692 NA 34.3 1 NA 3.055823e-07 0.4163395571 3.9582124643 NA 35 1 0.66303137 1.287796e-07 -1.4693520528 1.3294299203 NA - 35.1 0 0.71422833 1.762232e-06 -0.3031734330 1.5276966314 NA - 35.2 1 0.52721825 5.355159e-08 -0.6045512101 4.5025920868 NA - 36 0 NA 7.250797e-06 0.9823048960 0.7123168337 NA - 36.1 0 0.31373962 2.370652e-06 1.4466051416 1.7972493160 NA + 35.1 0 NA 1.762232e-06 -0.3031734330 1.5276966314 NA + 35.2 1 NA 5.355159e-08 -0.6045512101 4.5025920868 NA + 36 0 0.93843318 7.250797e-06 0.9823048960 0.7123168337 NA + 36.1 0 NA 2.370652e-06 1.4466051416 1.7972493160 NA 36.2 1 0.29886676 1.537090e-05 1.1606752905 1.8262697803 NA 36.3 0 0.22616598 6.993214e-07 0.8373091576 4.2840119381 NA 36.4 1 0.53849566 4.950009e-05 0.2640591685 4.6194464504 NA 37 1 1.68107300 2.755165e-07 0.1177313455 2.0018732361 NA 37.1 0 1.13777638 3.400517e-07 -0.1415483779 3.6656836793 NA 37.2 0 0.26931933 2.489007e-09 0.0054610124 3.9663937816 NA - 38 1 0.68769326 1.302651e-01 0.8078948077 0.9826511063 NA + 38 1 NA 1.302651e-01 0.8078948077 0.9826511063 NA 39 1 0.14395367 4.343746e-04 0.9876451040 0.6921808305 NA 39.1 0 0.36454923 6.653143e-05 -0.3431222274 0.9027792048 NA 39.2 0 1.03700002 1.940204e-09 -1.7909380751 1.3055654289 NA - 39.3 0 NA 8.300468e-07 -0.1798746191 1.5412842878 NA + 39.3 0 0.41320585 8.300468e-07 -0.1798746191 1.5412842878 NA 39.4 1 0.20901554 7.464169e-08 -0.1850961689 3.1834997435 NA 39.5 1 0.51603848 5.765597e-10 0.4544226146 4.1394166439 NA 40 0 0.33912363 9.140572e-01 0.5350190436 1.1330395646 NA @@ -18487,7 +18487,7 @@ 41 1 0.25193679 3.700067e-02 -0.1035047421 1.9337158254 NA 41.1 1 0.28760510 5.798225e-06 -0.4684202411 3.1956304458 NA 41.2 0 0.45553197 1.086252e-08 0.5972615368 3.2846923557 NA - 41.3 1 NA 3.088732e-07 0.9885613862 3.3813529415 NA + 41.3 1 0.79237611 3.088732e-07 0.9885613862 3.3813529415 NA 41.4 1 0.12582175 4.549537e-05 -0.3908036794 3.5482964432 NA 42 1 0.50079604 5.220968e-03 -0.0338893961 0.4859252973 NA 42.1 1 0.61140760 7.264286e-08 -0.4498363172 4.3293134298 NA @@ -18496,7 +18496,7 @@ 43.2 1 0.15152473 8.151771e-07 0.1804894429 2.6131797966 NA 44 1 0.38806434 1.032476e-03 1.3221409285 0.7662644819 NA 44.1 0 1.11140786 3.120174e-09 0.3416426284 2.6490291790 NA - 44.2 0 NA 2.571257e-10 0.5706610068 3.3371910988 NA + 44.2 0 0.39132534 2.571257e-10 0.5706610068 3.3371910988 NA 44.3 1 0.40934909 2.227416e-09 1.2679497430 4.1154200875 NA 45 1 0.68587067 3.948036e-01 0.1414983160 0.1957449992 NA 45.1 0 0.34530800 1.066310e-03 0.7220892521 1.9963831536 NA @@ -18516,38 +18516,38 @@ 52 1 0.19007135 1.632022e-02 0.3758235358 2.1266646020 NA 52.1 1 0.75662940 2.653038e-02 0.7138067080 3.1000545993 NA 52.2 0 1.66104719 2.262881e-03 0.8872895233 3.1268477370 NA - 52.3 0 0.12505840 6.572647e-10 -0.9664587437 3.5711459327 NA + 52.3 0 NA 6.572647e-10 -0.9664587437 3.5711459327 NA 52.4 1 0.18369708 1.393737e-04 0.0254566848 4.7983659909 NA 52.5 1 0.48689343 5.069462e-03 0.4155259424 4.9818264414 NA 53 1 0.31983157 5.848890e-05 0.5675736897 0.4965799209 NA 53.1 1 0.61569501 1.878509e-04 -0.3154088781 3.5505357443 NA - 53.2 1 0.43461540 1.293417e-04 0.2162315769 4.5790420019 NA + 53.2 1 NA 1.293417e-04 0.2162315769 4.5790420019 NA 54 0 1.90522418 1.818441e-03 -0.0880802382 1.4034724841 NA 54.1 1 0.59484889 2.251839e-07 0.4129127672 1.8812377600 NA 54.2 0 1.47174857 5.638172e-06 1.0119546775 2.5107589352 NA - 54.3 1 NA 5.320676e-03 -0.1112901990 2.7848406672 NA - 54.4 0 NA 1.491367e-07 0.8587727145 4.0143877396 NA + 54.3 1 0.27307143 5.320676e-03 -0.1112901990 2.7848406672 NA + 54.4 0 0.81272938 1.491367e-07 0.8587727145 4.0143877396 NA 55 1 0.22735476 3.183775e-03 -0.0116453589 0.6118522980 NA 55.1 1 0.54683512 1.183380e-03 0.5835528661 0.7463747414 NA - 55.2 1 NA 1.817077e-06 -1.0010857254 2.8201208171 NA + 55.2 1 1.03503777 1.817077e-06 -1.0010857254 2.8201208171 NA 55.3 0 0.30169529 1.424370e-06 -0.4796526070 3.1326431572 NA 55.4 1 0.36008059 3.119967e-07 -0.1202746964 3.2218102901 NA 56 0 0.14193566 1.169667e-06 0.5176377612 1.2231332215 NA 56.1 1 0.65073539 1.182293e-06 -1.1136932588 2.3573202139 NA 56.2 1 0.11338262 2.087533e-04 -0.0168103281 2.5674936292 NA 56.3 0 0.16820103 5.728251e-06 0.3933023606 2.9507164378 NA - 56.4 0 NA 4.087596e-08 0.3714625139 3.2272730360 NA + 56.4 0 0.27419110 4.087596e-08 0.3714625139 3.2272730360 NA 56.5 1 0.57110215 8.040370e-07 0.7811448179 3.4175522043 NA 57 1 0.85104054 1.438387e-02 -1.0868304872 0.2370331448 NA 57.1 1 0.34733833 3.202179e-05 0.8018626997 0.2481445030 NA 57.2 0 1.44438762 1.486318e-03 -0.1159517011 1.1405586067 NA 57.3 0 0.31836125 1.718412e-04 0.6785562445 2.1153886721 NA - 58 1 NA 3.114123e-05 1.6476207996 1.2210099772 NA + 58 1 0.37456898 3.114123e-05 1.6476207996 1.2210099772 NA 58.1 1 0.22120158 1.403881e-04 0.3402652711 1.6334245703 NA 58.2 1 0.78885210 2.111006e-01 -0.1111300753 1.6791862890 NA 58.3 1 0.10114937 9.586985e-06 -0.5409234285 2.6320121693 NA 58.4 1 0.13385114 4.073162e-03 -0.1271327672 2.8477731440 NA - 58.5 1 1.00316675 9.285307e-04 0.8713264822 3.5715569824 NA + 58.5 1 NA 9.285307e-04 0.8713264822 3.5715569824 NA 59 0 0.13202156 2.711478e-06 0.4766421367 1.9023998594 NA 59.1 1 0.33371896 1.173472e-04 1.0028089765 4.9736620474 NA 60 0 0.35096579 7.579680e-09 0.5231452932 2.8854503250 NA @@ -18590,7 +18590,7 @@ 72.3 0 0.48937486 2.878783e-12 0.7107266765 3.4853593935 NA 72.4 0 0.64173822 1.014404e-09 0.1451969143 3.6884259700 NA 72.5 1 0.57609943 1.281231e-05 1.6298050306 4.0854155901 NA - 73 1 NA 6.661564e-02 -0.0307469467 4.6019889915 NA + 73 1 0.17393402 6.661564e-02 -0.0307469467 4.6019889915 NA 74 1 0.23990575 3.683842e-04 0.3730017941 1.4626806753 NA 75 0 0.28469861 2.274469e-06 -0.4908003566 3.2524286874 NA 76 1 0.71988630 9.155636e-04 -0.9888876620 1.8074807397 NA @@ -18613,13 +18613,13 @@ 82.2 0 0.49936304 2.001426e-08 0.4958140634 2.6233131927 NA 83 1 0.21138572 5.690020e-06 0.5104724530 0.0537394290 NA 83.1 0 0.26403568 5.980615e-06 -0.0513309106 2.9061570496 NA - 83.2 0 NA 1.880816e-05 -0.2067792494 3.1189457362 NA + 83.2 0 0.20311133 1.880816e-05 -0.2067792494 3.1189457362 NA 83.3 1 1.16864671 4.048910e-09 -0.0534169155 4.7663642222 NA 84 1 1.99179346 6.552173e-02 -0.0255753653 2.7254060237 NA 84.1 0 1.52199460 8.829278e-06 -1.8234189877 3.3364784659 NA - 85 0 0.43196882 4.118253e-06 -0.0114038622 0.2977756259 NA + 85 0 NA 4.118253e-06 -0.0114038622 0.2977756259 NA 85.1 0 0.61458995 2.311994e-06 -0.0577615939 1.7394116637 NA - 85.2 1 NA 5.182892e-05 -0.2241856342 2.6846330194 NA + 85.2 1 0.07871196 5.182892e-05 -0.2241856342 2.6846330194 NA 85.3 1 1.42315283 1.689467e-03 -0.0520175929 3.1608762743 NA 85.4 1 0.97986129 1.168017e-03 0.2892733846 3.9452053758 NA 85.5 1 0.91792195 7.945131e-07 -0.3740417009 4.5092553482 NA @@ -18629,7 +18629,7 @@ 86.3 0 0.09360826 2.272397e-06 0.3305413955 2.1870554925 NA 86.4 1 0.58301186 4.467006e-06 2.6003411822 2.4532935000 NA 86.5 0 0.39146055 1.693940e-08 -0.1420690052 3.8206058508 NA - 87 0 0.50923566 6.396865e-05 1.0457427869 2.7069531474 NA + 87 0 NA 6.396865e-05 1.0457427869 2.7069531474 NA 87.1 1 0.66043624 1.264093e-10 -0.2973007190 3.4462517721 NA 87.2 0 0.13267613 4.933807e-07 0.4396872616 4.5241666853 NA 88 0 0.10696344 9.223531e-02 -0.0601928334 0.0005892443 NA @@ -18637,7 +18637,7 @@ 88.2 0 0.48037889 1.260399e-01 0.5730917016 2.4952722900 NA 88.3 0 0.97755681 8.029866e-08 -0.0029455332 3.2995816297 NA 89 1 0.70242369 7.489307e-05 1.5465903721 0.6462086167 NA - 90 0 NA 1.100491e-02 0.0626760573 0.1696030737 NA + 90 0 0.40042977 1.100491e-02 0.0626760573 0.1696030737 NA 90.1 1 0.63975731 2.715349e-05 1.1896872985 2.5980385230 NA 90.2 1 0.33412775 5.916576e-03 0.2597888783 2.6651392167 NA 90.3 0 0.38399003 2.920657e-02 0.6599799887 3.1242690247 NA @@ -18667,15 +18667,15 @@ 96.5 1 0.83843412 1.479105e-05 -0.1684332749 4.2377650598 NA 97 0 0.47151154 2.082560e-04 1.3775130083 1.1955102731 NA 97.1 0 0.15596614 7.903013e-10 -1.7323228619 4.9603108643 NA - 98 0 NA 1.795949e-06 -1.2648518889 0.2041732438 NA - 98.1 0 NA 2.776600e-02 -0.9042716241 0.4309578973 NA + 98 0 0.05179545 1.795949e-06 -1.2648518889 0.2041732438 NA + 98.1 0 0.47332096 2.776600e-02 -0.9042716241 0.4309578973 NA 98.2 0 0.19706341 4.050457e-06 -0.1560385207 3.5172611906 NA 99 1 0.22574556 2.316802e-05 0.7993356425 0.3531786101 NA 99.1 1 1.00732330 2.206426e-06 1.0355522332 4.6789444226 NA 99.2 1 0.09749127 2.488411e-08 -0.1150895843 4.9927084171 NA 100 0 0.22857989 7.572193e-01 0.0369067906 1.0691387602 NA 100.1 0 0.39548654 9.794641e-02 1.6023713093 1.5109344281 NA - 100.2 1 0.25111467 4.934595e-01 0.8861545820 2.1502332564 NA + 100.2 1 NA 4.934595e-01 0.8861545820 2.1502332564 NA 100.3 1 0.32695372 1.502083e-07 0.1277046316 3.8745574222 NA 100.4 1 0.10043925 2.515993e-06 -0.0834577654 4.6567608765 NA log(Be2) I(time^2) @@ -19017,7 +19017,7 @@ $m5b$spM_lvlone center scale b1 NA NA - L1mis 0.49165317 0.3533843 + L1mis 0.48184811 0.3462447 Be2 0.04274145 0.1563798 c1 0.25599956 0.6718095 time 2.53394028 1.3818094 @@ -46082,7 +46082,7 @@ Number and proportion of complete cases: level # % id id 100 100.0 - lvlone lvlone 96 29.2 + lvlone lvlone 98 29.8 Number and proportion of missing values: level # NA % NA @@ -46141,7 +46141,7 @@ Number and proportion of complete cases: level # % id id 100 100.0 - lvlone lvlone 96 29.2 + lvlone lvlone 98 29.8 Number and proportion of missing values: level # NA % NA From c6649fe41921aa8885bf8d765e0110320d920fce Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 18:20:50 +0200 Subject: [PATCH 123/176] cat on cran to see if github things it is CRAN --- tests/testthat/test-clmm.R | 1 - tests/testthat/test-glm.R | 3 ++- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test-clmm.R b/tests/testthat/test-clmm.R index a4513abb..1adf775a 100644 --- a/tests/testthat/test-clmm.R +++ b/tests/testthat/test-clmm.R @@ -1,6 +1,5 @@ library("JointAI") -# Sys.setenv(IS_CHECK = "true") skip_on_cran() if (identical(Sys.getenv("NOT_CRAN"), "true")) { diff --git a/tests/testthat/test-glm.R b/tests/testthat/test-glm.R index 2b030f1e..d696037e 100644 --- a/tests/testthat/test-glm.R +++ b/tests/testthat/test-glm.R @@ -1,9 +1,10 @@ library("JointAI") -# Sys.setenv(IS_CHECK = "true") skip_on_cran() +cat("ON CRAN:", testthat:::on_cran(), "\n") + if (identical(Sys.getenv("NOT_CRAN"), "true")) { set_seed(1234) wideDF <- JointAI::wideDF From aebc0e6882237470e1c1ecc5edc9b224eb456564 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Thu, 1 Sep 2022 18:27:30 +0200 Subject: [PATCH 124/176] try use windows for readme action --- .github/workflows/render-readme.yaml | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/.github/workflows/render-readme.yaml b/.github/workflows/render-readme.yaml index 439edc74..6a9c2187 100644 --- a/.github/workflows/render-readme.yaml +++ b/.github/workflows/render-readme.yaml @@ -8,14 +8,21 @@ name: Render README jobs: render: name: Render README - runs-on: ubuntu-latest + runs-on: windows-latest steps: - uses: actions/checkout@v2 - uses: r-lib/actions/setup-r@v1 - uses: r-lib/actions/setup-pandoc@v1 - - name: Install JAGS - run: sudo apt-get install jags + - name: Download JAGS Windows + if: runner.os == 'Windows' + run: (New-Object System.Net.WebClient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe', 'C:\JAGS-4.3.1.exe') + shell: powershell + + - name: Install JAGS Windows + if: runner.os == 'Windows' + run: C:\JAGS-4.3.1.exe /S + shell: cmd - name: Install rmarkdown, remotes, and the local package run: | From 4faa6813208500ff17ca5335feaff68ce2d4fde5 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 2 Sep 2022 09:05:29 +0200 Subject: [PATCH 125/176] update snapshot --- tests/testthat/_snaps/mlogitmm.md | 8132 ++++++++++++++--------------- tests/testthat/test-mlogitmm.R | 3 + 2 files changed, 4069 insertions(+), 4066 deletions(-) diff --git a/tests/testthat/_snaps/mlogitmm.md b/tests/testthat/_snaps/mlogitmm.md index c639806d..47f81edf 100644 --- a/tests/testthat/_snaps/mlogitmm.md +++ b/tests/testthat/_snaps/mlogitmm.md @@ -109,335 +109,335 @@ $m0a$M_lvlone m1 - 1 2 + 1 3 1.1 2 - 1.2 2 - 1.3 2 - 2 1 - 2.1 1 + 1.2 1 + 1.3 1 + 2 2 + 2.1 2 2.2 1 - 3 2 - 3.1 1 + 3 1 + 3.1 2 3.2 2 - 4 3 - 4.1 3 - 4.2 3 - 4.3 2 + 4 2 + 4.1 1 + 4.2 2 + 4.3 3 5 2 - 5.1 2 - 5.2 3 + 5.1 1 + 5.2 2 5.3 2 6 2 - 7 1 - 7.1 1 - 7.2 1 - 8 3 - 8.1 3 + 7 3 + 7.1 2 + 7.2 3 + 8 2 + 8.1 1 8.2 3 - 8.3 1 + 8.3 2 8.4 2 - 8.5 3 - 9 2 + 8.5 2 + 9 3 9.1 2 9.2 3 - 10 1 - 10.1 3 - 11 3 + 10 3 + 10.1 1 + 11 1 11.1 1 11.2 2 - 11.3 1 - 11.4 3 - 12 2 - 13 1 + 11.3 3 + 11.4 1 + 12 1 + 13 2 13.1 3 14 1 - 14.1 3 - 14.2 3 - 14.3 2 - 15 2 - 15.1 2 + 14.1 1 + 14.2 1 + 14.3 3 + 15 1 + 15.1 1 15.2 3 - 15.3 3 - 16 1 + 15.3 2 + 16 2 16.1 2 16.2 1 16.3 3 16.4 2 - 16.5 3 - 17 1 - 17.1 2 + 16.5 1 + 17 2 + 17.1 3 17.2 1 - 17.3 2 + 17.3 1 17.4 2 18 1 19 2 - 19.1 1 + 19.1 3 19.2 2 19.3 3 20 2 - 20.1 3 - 20.2 2 - 20.3 2 - 20.4 1 - 20.5 2 - 21 3 + 20.1 2 + 20.2 1 + 20.3 3 + 20.4 2 + 20.5 3 + 21 1 21.1 2 - 21.2 2 + 21.2 3 22 2 - 22.1 1 - 23 1 + 22.1 2 + 23 2 23.1 1 - 24 3 - 25 3 + 24 1 + 25 1 25.1 3 - 25.2 1 - 25.3 1 - 25.4 2 - 25.5 2 - 26 1 + 25.2 2 + 25.3 2 + 25.4 1 + 25.5 1 + 26 2 26.1 1 - 26.2 2 - 26.3 1 + 26.2 1 + 26.3 2 27 1 - 27.1 2 - 28 3 - 28.1 2 - 28.2 3 - 28.3 2 - 29 1 + 27.1 3 + 28 1 + 28.1 3 + 28.2 1 + 28.3 1 + 29 3 29.1 3 - 29.2 1 - 29.3 3 - 30 2 - 30.1 2 - 30.2 1 - 31 3 + 29.2 3 + 29.3 2 + 30 1 + 30.1 3 + 30.2 3 + 31 1 32 3 - 32.1 1 - 32.2 1 - 32.3 3 + 32.1 3 + 32.2 2 + 32.3 1 33 3 - 33.1 2 - 34 3 - 34.1 2 - 34.2 1 + 33.1 1 + 34 1 + 34.1 1 + 34.2 2 34.3 2 - 35 3 + 35 1 35.1 1 - 35.2 2 - 36 1 - 36.1 2 + 35.2 1 + 36 2 + 36.1 3 36.2 3 - 36.3 2 + 36.3 3 36.4 3 37 1 - 37.1 2 - 37.2 3 - 38 1 - 39 3 + 37.1 3 + 37.2 1 + 38 2 + 39 2 39.1 3 - 39.2 3 - 39.3 3 - 39.4 2 - 39.5 2 - 40 2 + 39.2 1 + 39.3 2 + 39.4 3 + 39.5 3 + 40 3 40.1 3 - 40.2 2 - 40.3 2 - 41 1 - 41.1 2 - 41.2 2 - 41.3 2 + 40.2 1 + 40.3 3 + 41 3 + 41.1 3 + 41.2 1 + 41.3 1 41.4 1 - 42 3 - 42.1 2 + 42 1 + 42.1 1 43 3 43.1 3 - 43.2 1 + 43.2 2 44 2 44.1 2 - 44.2 3 + 44.2 1 44.3 1 - 45 1 - 45.1 1 - 46 2 - 46.1 1 + 45 2 + 45.1 3 + 46 3 + 46.1 2 46.2 3 47 1 - 47.1 1 + 47.1 2 47.2 2 - 47.3 3 + 47.3 2 47.4 2 48 3 - 48.1 3 - 49 1 - 50 2 - 51 2 + 48.1 1 + 49 3 + 50 1 + 51 3 52 3 - 52.1 3 - 52.2 2 - 52.3 2 - 52.4 1 - 52.5 2 - 53 3 - 53.1 2 - 53.2 1 - 54 1 + 52.1 2 + 52.2 1 + 52.3 3 + 52.4 3 + 52.5 3 + 53 1 + 53.1 3 + 53.2 2 + 54 3 54.1 3 - 54.2 1 - 54.3 3 + 54.2 3 + 54.3 1 54.4 1 - 55 3 + 55 1 55.1 3 - 55.2 1 - 55.3 3 + 55.2 2 + 55.3 1 55.4 1 - 56 3 - 56.1 3 - 56.2 2 - 56.3 3 - 56.4 1 - 56.5 2 - 57 2 + 56 2 + 56.1 1 + 56.2 3 + 56.3 1 + 56.4 2 + 56.5 1 + 57 1 57.1 1 - 57.2 3 - 57.3 2 - 58 2 - 58.1 3 + 57.2 1 + 57.3 1 + 58 3 + 58.1 2 58.2 1 - 58.3 1 - 58.4 1 - 58.5 1 - 59 1 + 58.3 3 + 58.4 3 + 58.5 3 + 59 3 59.1 1 60 3 61 1 61.1 2 - 61.2 1 - 61.3 1 + 61.2 2 + 61.3 3 61.4 2 62 2 - 62.1 3 + 62.1 1 62.2 3 - 62.3 1 - 63 1 - 63.1 2 + 62.3 2 + 63 3 + 63.1 1 64 3 65 3 65.1 3 65.2 2 - 65.3 2 - 66 2 + 65.3 3 + 66 3 66.1 3 - 66.2 2 + 66.2 1 67 3 68 3 68.1 1 - 68.2 3 + 68.2 2 68.3 3 68.4 1 - 69 2 - 70 2 - 70.1 3 - 71 1 - 71.1 3 - 71.2 1 - 71.3 2 - 71.4 1 + 69 1 + 70 1 + 70.1 2 + 71 3 + 71.1 2 + 71.2 2 + 71.3 1 + 71.4 2 72 1 72.1 2 - 72.2 3 - 72.3 3 - 72.4 1 - 72.5 2 - 73 1 - 74 2 - 75 1 + 72.2 1 + 72.3 2 + 72.4 2 + 72.5 1 + 73 2 + 74 1 + 75 3 76 3 - 76.1 1 - 76.2 1 - 77 1 - 78 1 + 76.1 3 + 76.2 2 + 77 2 + 78 2 79 2 79.1 2 - 79.2 3 - 80 3 - 80.1 2 + 79.2 2 + 80 2 + 80.1 1 80.2 3 81 2 81.1 3 81.2 2 81.3 1 - 82 3 + 82 1 82.1 2 - 82.2 1 - 83 1 + 82.2 3 + 83 2 83.1 3 - 83.2 2 - 83.3 2 + 83.2 3 + 83.3 3 84 2 - 84.1 1 + 84.1 3 85 1 - 85.1 1 - 85.2 2 - 85.3 2 - 85.4 3 - 85.5 3 + 85.1 2 + 85.2 3 + 85.3 3 + 85.4 2 + 85.5 2 86 1 86.1 2 - 86.2 3 - 86.3 3 + 86.2 1 + 86.3 1 86.4 1 - 86.5 3 - 87 2 + 86.5 2 + 87 3 87.1 3 87.2 2 - 88 1 + 88 3 88.1 3 88.2 3 88.3 1 - 89 1 - 90 3 + 89 2 + 90 1 90.1 2 90.2 2 90.3 2 - 91 1 + 91 3 91.1 3 - 91.2 2 + 91.2 3 92 2 93 2 - 93.1 1 - 93.2 1 + 93.1 2 + 93.2 2 93.3 3 93.4 2 - 94 1 - 94.1 1 - 94.2 2 + 94 2 + 94.1 3 + 94.2 3 94.3 2 - 94.4 2 - 94.5 1 - 95 1 - 95.1 2 + 94.4 3 + 94.5 2 + 95 2 + 95.1 3 95.2 2 - 96 2 - 96.1 3 - 96.2 1 - 96.3 3 + 96 3 + 96.1 2 + 96.2 3 + 96.3 2 96.4 2 - 96.5 2 - 97 1 + 96.5 3 + 97 3 97.1 3 - 98 3 - 98.1 1 - 98.2 3 - 99 1 + 98 2 + 98.1 3 + 98.2 1 + 99 2 99.1 1 - 99.2 2 - 100 3 - 100.1 2 + 99.2 3 + 100 2 + 100.1 1 100.2 2 - 100.3 3 - 100.4 1 + 100.3 2 + 100.4 3 $m0a$mu_reg_multinomial [1] 0 @@ -580,334 +580,334 @@ $m0b$M_lvlone m2 1 3 - 1.1 3 - 1.2 1 - 1.3 2 - 2 3 + 1.1 1 + 1.2 3 + 1.3 1 + 2 2 2.1 1 2.2 NA - 3 1 - 3.1 1 - 3.2 3 - 4 2 - 4.1 3 + 3 3 + 3.1 2 + 3.2 1 + 4 1 + 4.1 2 4.2 3 - 4.3 2 - 5 1 - 5.1 1 - 5.2 3 - 5.3 3 + 4.3 3 + 5 2 + 5.1 3 + 5.2 1 + 5.3 1 6 2 - 7 3 + 7 2 7.1 1 - 7.2 1 - 8 3 - 8.1 NA - 8.2 2 - 8.3 1 - 8.4 1 + 7.2 3 + 8 2 + 8.1 2 + 8.2 1 + 8.3 3 + 8.4 NA 8.5 3 9 NA - 9.1 2 - 9.2 2 - 10 2 - 10.1 2 - 11 3 - 11.1 2 + 9.1 3 + 9.2 1 + 10 1 + 10.1 1 + 11 1 + 11.1 1 11.2 1 - 11.3 2 + 11.3 NA 11.4 1 - 12 3 - 13 NA + 12 1 + 13 2 13.1 2 - 14 2 - 14.1 1 - 14.2 3 - 14.3 2 - 15 NA - 15.1 1 - 15.2 1 + 14 3 + 14.1 2 + 14.2 1 + 14.3 1 + 15 1 + 15.1 2 + 15.2 3 15.3 3 - 16 3 - 16.1 2 - 16.2 NA - 16.3 3 - 16.4 1 - 16.5 NA - 17 3 - 17.1 1 - 17.2 3 - 17.3 3 + 16 2 + 16.1 NA + 16.2 3 + 16.3 2 + 16.4 3 + 16.5 1 + 17 1 + 17.1 3 + 17.2 NA + 17.3 2 17.4 1 - 18 1 - 19 3 - 19.1 2 - 19.2 1 - 19.3 1 - 20 1 - 20.1 3 - 20.2 2 - 20.3 NA + 18 3 + 19 NA + 19.1 1 + 19.2 3 + 19.3 3 + 20 2 + 20.1 NA + 20.2 3 + 20.3 1 20.4 3 - 20.5 3 - 21 1 - 21.1 2 - 21.2 3 - 22 NA - 22.1 2 - 23 2 + 20.5 2 + 21 3 + 21.1 1 + 21.2 NA + 22 3 + 22.1 1 + 23 1 23.1 2 - 24 1 - 25 NA - 25.1 1 - 25.2 1 - 25.3 2 - 25.4 NA + 24 2 + 25 2 + 25.1 3 + 25.2 3 + 25.3 1 + 25.4 3 25.5 2 - 26 3 - 26.1 1 - 26.2 2 - 26.3 1 - 27 1 + 26 NA + 26.1 3 + 26.2 3 + 26.3 NA + 27 3 27.1 3 28 3 - 28.1 NA - 28.2 NA - 28.3 NA - 29 3 - 29.1 1 - 29.2 1 + 28.1 2 + 28.2 2 + 28.3 3 + 29 1 + 29.1 NA + 29.2 2 29.3 2 - 30 3 - 30.1 2 - 30.2 2 - 31 NA - 32 1 - 32.1 NA - 32.2 3 - 32.3 2 - 33 1 - 33.1 2 - 34 1 - 34.1 3 - 34.2 3 - 34.3 3 - 35 1 - 35.1 3 + 30 2 + 30.1 3 + 30.2 3 + 31 3 + 32 3 + 32.1 3 + 32.2 1 + 32.3 1 + 33 3 + 33.1 3 + 34 3 + 34.1 NA + 34.2 1 + 34.3 NA + 35 2 + 35.1 2 35.2 2 36 3 - 36.1 NA + 36.1 3 36.2 3 - 36.3 NA - 36.4 NA - 37 NA + 36.3 2 + 36.4 2 + 37 2 37.1 2 - 37.2 3 + 37.2 1 38 2 - 39 NA + 39 3 39.1 2 - 39.2 1 + 39.2 3 39.3 NA - 39.4 2 - 39.5 1 + 39.4 3 + 39.5 3 40 3 - 40.1 3 - 40.2 NA + 40.1 1 + 40.2 3 40.3 2 - 41 1 - 41.1 2 - 41.2 3 - 41.3 3 - 41.4 1 + 41 3 + 41.1 3 + 41.2 1 + 41.3 2 + 41.4 3 42 2 42.1 NA - 43 2 + 43 3 43.1 3 - 43.2 3 + 43.2 2 44 3 - 44.1 1 - 44.2 2 + 44.1 3 + 44.2 NA 44.3 1 45 3 - 45.1 2 - 46 2 - 46.1 3 + 45.1 1 + 46 NA + 46.1 1 46.2 2 - 47 3 - 47.1 3 - 47.2 2 + 47 2 + 47.1 NA + 47.2 NA 47.3 3 - 47.4 2 - 48 NA + 47.4 3 + 48 3 48.1 1 - 49 2 + 49 1 50 NA - 51 NA + 51 1 52 2 - 52.1 3 - 52.2 3 - 52.3 2 + 52.1 1 + 52.2 1 + 52.3 NA 52.4 2 - 52.5 1 + 52.5 3 53 2 - 53.1 2 - 53.2 3 + 53.1 1 + 53.2 2 54 NA - 54.1 3 - 54.2 3 - 54.3 1 - 54.4 NA + 54.1 1 + 54.2 NA + 54.3 3 + 54.4 3 55 1 55.1 1 - 55.2 3 - 55.3 1 - 55.4 1 - 56 3 - 56.1 1 - 56.2 2 - 56.3 2 - 56.4 3 - 56.5 2 - 57 3 - 57.1 2 - 57.2 NA - 57.3 1 + 55.2 1 + 55.3 NA + 55.4 2 + 56 2 + 56.1 3 + 56.2 1 + 56.3 1 + 56.4 2 + 56.5 NA + 57 2 + 57.1 3 + 57.2 2 + 57.3 NA 58 1 - 58.1 NA - 58.2 2 - 58.3 3 - 58.4 3 - 58.5 2 - 59 2 - 59.1 3 - 60 NA - 61 NA - 61.1 NA + 58.1 1 + 58.2 NA + 58.3 1 + 58.4 2 + 58.5 NA + 59 1 + 59.1 1 + 60 1 + 61 2 + 61.1 1 61.2 1 61.3 2 - 61.4 3 - 62 3 - 62.1 3 - 62.2 2 - 62.3 2 - 63 1 - 63.1 1 - 64 1 - 65 2 - 65.1 NA - 65.2 2 - 65.3 1 + 61.4 2 + 62 1 + 62.1 1 + 62.2 NA + 62.3 1 + 63 NA + 63.1 3 + 64 3 + 65 NA + 65.1 2 + 65.2 3 + 65.3 3 66 3 - 66.1 1 - 66.2 3 - 67 3 - 68 2 - 68.1 3 + 66.1 3 + 66.2 1 + 67 NA + 68 1 + 68.1 1 68.2 1 68.3 2 68.4 3 - 69 1 - 70 NA - 70.1 1 - 71 2 - 71.1 2 - 71.2 3 + 69 NA + 70 1 + 70.1 NA + 71 1 + 71.1 1 + 71.2 NA 71.3 1 - 71.4 NA - 72 3 - 72.1 2 - 72.2 1 + 71.4 1 + 72 2 + 72.1 3 + 72.2 2 72.3 1 - 72.4 3 - 72.5 3 - 73 2 + 72.4 2 + 72.5 1 + 73 NA 74 1 - 75 2 - 76 3 - 76.1 3 - 76.2 3 - 77 3 - 78 2 - 79 1 + 75 NA + 76 1 + 76.1 2 + 76.2 2 + 77 NA + 78 1 + 79 3 79.1 3 79.2 NA - 80 1 - 80.1 NA - 80.2 3 - 81 3 - 81.1 1 - 81.2 NA - 81.3 3 - 82 2 - 82.1 3 + 80 3 + 80.1 2 + 80.2 NA + 81 1 + 81.1 2 + 81.2 1 + 81.3 1 + 82 3 + 82.1 1 82.2 1 83 2 - 83.1 1 - 83.2 3 + 83.1 3 + 83.2 2 83.3 3 - 84 3 - 84.1 1 + 84 1 + 84.1 2 85 2 85.1 1 - 85.2 2 - 85.3 3 - 85.4 3 - 85.5 2 - 86 3 - 86.1 3 - 86.2 1 - 86.3 NA - 86.4 1 - 86.5 3 + 85.2 1 + 85.3 NA + 85.4 2 + 85.5 1 + 86 1 + 86.1 NA + 86.2 2 + 86.3 1 + 86.4 2 + 86.5 2 87 NA - 87.1 3 - 87.2 1 - 88 3 - 88.1 1 - 88.2 3 + 87.1 1 + 87.2 NA + 88 1 + 88.1 2 + 88.2 NA 88.3 2 - 89 2 + 89 3 90 3 90.1 2 - 90.2 3 + 90.2 NA 90.3 2 - 91 1 - 91.1 2 - 91.2 2 + 91 3 + 91.1 1 + 91.2 3 92 2 - 93 1 - 93.1 2 - 93.2 3 + 93 2 + 93.1 3 + 93.2 NA 93.3 2 - 93.4 1 - 94 1 + 93.4 3 + 94 2 94.1 2 - 94.2 NA + 94.2 1 94.3 2 - 94.4 NA - 94.5 NA - 95 1 - 95.1 3 - 95.2 3 + 94.4 1 + 94.5 2 + 95 2 + 95.1 2 + 95.2 NA 96 1 - 96.1 2 + 96.1 1 96.2 2 - 96.3 1 - 96.4 NA + 96.3 3 + 96.4 2 96.5 NA 97 1 - 97.1 3 - 98 NA - 98.1 3 - 98.2 1 + 97.1 2 + 98 3 + 98.1 2 + 98.2 2 99 2 99.1 2 - 99.2 NA - 100 2 - 100.1 NA + 99.2 1 + 100 1 + 100.1 2 100.2 3 - 100.3 3 - 100.4 3 + 100.3 2 + 100.4 1 $m0b$mu_reg_multinomial [1] 0 @@ -1049,335 +1049,335 @@ $m1a$M_lvlone m1 - 1 2 + 1 3 1.1 2 - 1.2 2 - 1.3 2 - 2 1 - 2.1 1 + 1.2 1 + 1.3 1 + 2 2 + 2.1 2 2.2 1 - 3 2 - 3.1 1 + 3 1 + 3.1 2 3.2 2 - 4 3 - 4.1 3 - 4.2 3 - 4.3 2 + 4 2 + 4.1 1 + 4.2 2 + 4.3 3 5 2 - 5.1 2 - 5.2 3 + 5.1 1 + 5.2 2 5.3 2 6 2 - 7 1 - 7.1 1 - 7.2 1 - 8 3 - 8.1 3 + 7 3 + 7.1 2 + 7.2 3 + 8 2 + 8.1 1 8.2 3 - 8.3 1 + 8.3 2 8.4 2 - 8.5 3 - 9 2 + 8.5 2 + 9 3 9.1 2 9.2 3 - 10 1 - 10.1 3 - 11 3 + 10 3 + 10.1 1 + 11 1 11.1 1 11.2 2 - 11.3 1 - 11.4 3 - 12 2 - 13 1 + 11.3 3 + 11.4 1 + 12 1 + 13 2 13.1 3 14 1 - 14.1 3 - 14.2 3 - 14.3 2 - 15 2 - 15.1 2 + 14.1 1 + 14.2 1 + 14.3 3 + 15 1 + 15.1 1 15.2 3 - 15.3 3 - 16 1 + 15.3 2 + 16 2 16.1 2 16.2 1 16.3 3 16.4 2 - 16.5 3 - 17 1 - 17.1 2 + 16.5 1 + 17 2 + 17.1 3 17.2 1 - 17.3 2 + 17.3 1 17.4 2 18 1 19 2 - 19.1 1 + 19.1 3 19.2 2 19.3 3 20 2 - 20.1 3 - 20.2 2 - 20.3 2 - 20.4 1 - 20.5 2 - 21 3 + 20.1 2 + 20.2 1 + 20.3 3 + 20.4 2 + 20.5 3 + 21 1 21.1 2 - 21.2 2 + 21.2 3 22 2 - 22.1 1 - 23 1 + 22.1 2 + 23 2 23.1 1 - 24 3 - 25 3 + 24 1 + 25 1 25.1 3 - 25.2 1 - 25.3 1 - 25.4 2 - 25.5 2 - 26 1 + 25.2 2 + 25.3 2 + 25.4 1 + 25.5 1 + 26 2 26.1 1 - 26.2 2 - 26.3 1 + 26.2 1 + 26.3 2 27 1 - 27.1 2 - 28 3 - 28.1 2 - 28.2 3 - 28.3 2 - 29 1 + 27.1 3 + 28 1 + 28.1 3 + 28.2 1 + 28.3 1 + 29 3 29.1 3 - 29.2 1 - 29.3 3 - 30 2 - 30.1 2 - 30.2 1 - 31 3 + 29.2 3 + 29.3 2 + 30 1 + 30.1 3 + 30.2 3 + 31 1 32 3 - 32.1 1 - 32.2 1 - 32.3 3 + 32.1 3 + 32.2 2 + 32.3 1 33 3 - 33.1 2 - 34 3 - 34.1 2 - 34.2 1 + 33.1 1 + 34 1 + 34.1 1 + 34.2 2 34.3 2 - 35 3 + 35 1 35.1 1 - 35.2 2 - 36 1 - 36.1 2 + 35.2 1 + 36 2 + 36.1 3 36.2 3 - 36.3 2 + 36.3 3 36.4 3 37 1 - 37.1 2 - 37.2 3 - 38 1 - 39 3 + 37.1 3 + 37.2 1 + 38 2 + 39 2 39.1 3 - 39.2 3 - 39.3 3 - 39.4 2 - 39.5 2 - 40 2 + 39.2 1 + 39.3 2 + 39.4 3 + 39.5 3 + 40 3 40.1 3 - 40.2 2 - 40.3 2 - 41 1 - 41.1 2 - 41.2 2 - 41.3 2 + 40.2 1 + 40.3 3 + 41 3 + 41.1 3 + 41.2 1 + 41.3 1 41.4 1 - 42 3 - 42.1 2 + 42 1 + 42.1 1 43 3 43.1 3 - 43.2 1 + 43.2 2 44 2 44.1 2 - 44.2 3 + 44.2 1 44.3 1 - 45 1 - 45.1 1 - 46 2 - 46.1 1 + 45 2 + 45.1 3 + 46 3 + 46.1 2 46.2 3 47 1 - 47.1 1 + 47.1 2 47.2 2 - 47.3 3 + 47.3 2 47.4 2 48 3 - 48.1 3 - 49 1 - 50 2 - 51 2 + 48.1 1 + 49 3 + 50 1 + 51 3 52 3 - 52.1 3 - 52.2 2 - 52.3 2 - 52.4 1 - 52.5 2 - 53 3 - 53.1 2 - 53.2 1 - 54 1 + 52.1 2 + 52.2 1 + 52.3 3 + 52.4 3 + 52.5 3 + 53 1 + 53.1 3 + 53.2 2 + 54 3 54.1 3 - 54.2 1 - 54.3 3 + 54.2 3 + 54.3 1 54.4 1 - 55 3 + 55 1 55.1 3 - 55.2 1 - 55.3 3 + 55.2 2 + 55.3 1 55.4 1 - 56 3 - 56.1 3 - 56.2 2 - 56.3 3 - 56.4 1 - 56.5 2 - 57 2 + 56 2 + 56.1 1 + 56.2 3 + 56.3 1 + 56.4 2 + 56.5 1 + 57 1 57.1 1 - 57.2 3 - 57.3 2 - 58 2 - 58.1 3 + 57.2 1 + 57.3 1 + 58 3 + 58.1 2 58.2 1 - 58.3 1 - 58.4 1 - 58.5 1 - 59 1 + 58.3 3 + 58.4 3 + 58.5 3 + 59 3 59.1 1 60 3 61 1 61.1 2 - 61.2 1 - 61.3 1 + 61.2 2 + 61.3 3 61.4 2 62 2 - 62.1 3 + 62.1 1 62.2 3 - 62.3 1 - 63 1 - 63.1 2 + 62.3 2 + 63 3 + 63.1 1 64 3 65 3 65.1 3 65.2 2 - 65.3 2 - 66 2 + 65.3 3 + 66 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NA NA + 62 2 1 NA NA NA NA + 62.1 1 1 NA NA NA NA + 62.2 3 NA NA NA NA NA + 62.3 2 1 NA NA NA NA + 63 3 NA NA NA NA NA + 63.1 1 3 NA NA NA NA + 64 3 3 NA NA NA NA + 65 3 NA NA NA NA NA + 65.1 3 2 NA NA NA NA + 65.2 2 3 NA NA NA NA + 65.3 3 3 NA NA NA NA + 66 3 3 NA NA NA NA + 66.1 3 3 NA NA NA NA + 66.2 1 1 NA NA NA NA + 67 3 NA NA NA NA NA + 68 3 1 NA NA NA NA + 68.1 1 1 NA NA NA NA + 68.2 2 1 NA NA NA NA 68.3 3 2 NA NA NA NA 68.4 1 3 NA NA NA NA - 69 2 1 NA NA NA NA - 70 2 NA NA NA NA NA - 70.1 3 1 NA NA NA NA - 71 1 2 NA NA NA NA - 71.1 3 2 NA NA NA NA - 71.2 1 3 NA NA NA NA - 71.3 2 1 NA NA NA NA - 71.4 1 NA NA NA NA NA - 72 1 3 NA NA NA NA - 72.1 2 2 NA NA NA NA - 72.2 3 1 NA NA NA NA - 72.3 3 1 NA NA NA NA - 72.4 1 3 NA NA NA NA - 72.5 2 3 NA NA NA NA - 73 1 2 NA NA NA NA - 74 2 1 NA NA NA NA - 75 1 2 NA NA NA NA - 76 3 3 NA NA NA NA - 76.1 1 3 NA NA NA NA - 76.2 1 3 NA NA NA NA - 77 1 3 NA NA NA NA - 78 1 2 NA NA NA NA - 79 2 1 NA NA NA NA + 69 1 NA NA NA NA NA + 70 1 1 NA NA NA NA + 70.1 2 NA NA NA NA NA + 71 3 1 NA NA NA NA + 71.1 2 1 NA NA NA NA + 71.2 2 NA NA NA NA NA + 71.3 1 1 NA NA NA NA + 71.4 2 1 NA NA NA NA + 72 1 2 NA NA NA NA + 72.1 2 3 NA NA NA NA + 72.2 1 2 NA NA NA NA + 72.3 2 1 NA NA NA NA + 72.4 2 2 NA NA NA NA + 72.5 1 1 NA NA NA NA + 73 2 NA NA NA NA NA + 74 1 1 NA NA NA NA + 75 3 NA NA NA NA NA + 76 3 1 NA NA NA NA + 76.1 3 2 NA NA NA NA + 76.2 2 2 NA NA NA NA + 77 2 NA NA NA NA NA + 78 2 1 NA NA NA NA + 79 2 3 NA NA NA NA 79.1 2 3 NA NA NA NA - 79.2 3 NA NA NA NA NA - 80 3 1 NA NA NA NA - 80.1 2 NA NA NA NA NA - 80.2 3 3 NA NA NA NA - 81 2 3 NA NA NA NA - 81.1 3 1 NA NA NA NA - 81.2 2 NA NA NA NA NA - 81.3 1 3 NA NA NA NA - 82 3 2 NA NA NA NA - 82.1 2 3 NA NA NA NA - 82.2 1 1 NA NA NA NA - 83 1 2 NA NA NA NA - 83.1 3 1 NA NA NA NA - 83.2 2 3 NA NA NA NA - 83.3 2 3 NA NA NA NA - 84 2 3 NA NA NA NA - 84.1 1 1 NA NA NA NA + 79.2 2 NA NA NA NA NA + 80 2 3 NA NA NA NA + 80.1 1 2 NA NA NA NA + 80.2 3 NA NA NA NA NA + 81 2 1 NA NA NA NA + 81.1 3 2 NA NA NA NA + 81.2 2 1 NA NA NA NA + 81.3 1 1 NA NA NA NA + 82 1 3 NA NA NA NA + 82.1 2 1 NA NA NA NA + 82.2 3 1 NA NA NA NA + 83 2 2 NA NA NA NA + 83.1 3 3 NA NA NA NA + 83.2 3 2 NA NA NA NA + 83.3 3 3 NA NA NA NA + 84 2 1 NA NA NA NA + 84.1 3 2 NA NA NA NA 85 1 2 NA NA NA NA - 85.1 1 1 NA NA NA NA - 85.2 2 2 NA NA NA NA - 85.3 2 3 NA NA NA NA - 85.4 3 3 NA NA NA NA - 85.5 3 2 NA NA NA NA - 86 1 3 NA NA NA NA - 86.1 2 3 NA NA NA NA - 86.2 3 1 NA NA NA NA - 86.3 3 NA NA NA NA NA - 86.4 1 1 NA NA NA NA - 86.5 3 3 NA NA NA NA - 87 2 NA NA NA NA NA - 87.1 3 3 NA NA NA NA - 87.2 2 1 NA NA NA NA - 88 1 3 NA NA NA NA - 88.1 3 1 NA NA NA NA - 88.2 3 3 NA NA NA NA + 85.1 2 1 NA NA NA NA + 85.2 3 1 NA NA NA NA + 85.3 3 NA NA NA NA NA + 85.4 2 2 NA NA NA NA + 85.5 2 1 NA NA NA NA + 86 1 1 NA NA NA NA + 86.1 2 NA NA NA NA NA + 86.2 1 2 NA NA NA NA + 86.3 1 1 NA NA NA NA + 86.4 1 2 NA NA NA NA + 86.5 2 2 NA NA NA NA + 87 3 NA NA NA NA NA + 87.1 3 1 NA NA NA NA + 87.2 2 NA NA NA NA NA + 88 3 1 NA NA NA NA + 88.1 3 2 NA NA NA NA + 88.2 3 NA NA NA NA NA 88.3 1 2 NA NA NA NA - 89 1 2 NA NA NA NA - 90 3 3 NA NA NA NA + 89 2 3 NA NA NA NA + 90 1 3 NA NA NA NA 90.1 2 2 NA NA NA NA - 90.2 2 3 NA NA NA NA + 90.2 2 NA NA NA NA NA 90.3 2 2 NA NA NA NA - 91 1 1 NA NA NA NA - 91.1 3 2 NA NA NA NA - 91.2 2 2 NA NA NA NA + 91 3 3 NA NA NA NA + 91.1 3 1 NA NA NA NA + 91.2 3 3 NA NA NA NA 92 2 2 NA NA NA NA - 93 2 1 NA NA NA NA - 93.1 1 2 NA NA NA NA - 93.2 1 3 NA NA NA NA + 93 2 2 NA NA NA NA + 93.1 2 3 NA NA NA NA + 93.2 2 NA NA NA NA NA 93.3 3 2 NA NA NA NA - 93.4 2 1 NA NA NA NA - 94 1 1 NA NA NA NA - 94.1 1 2 NA NA NA NA - 94.2 2 NA NA NA NA NA + 93.4 2 3 NA NA NA NA + 94 2 2 NA NA NA NA + 94.1 3 2 NA NA NA NA + 94.2 3 1 NA NA NA NA 94.3 2 2 NA NA NA NA - 94.4 2 NA NA NA NA NA - 94.5 1 NA NA NA NA NA - 95 1 1 NA NA NA NA - 95.1 2 3 NA NA NA NA - 95.2 2 3 NA NA NA NA - 96 2 1 NA NA NA NA - 96.1 3 2 NA NA NA NA - 96.2 1 2 NA NA NA NA - 96.3 3 1 NA NA NA NA - 96.4 2 NA NA NA NA NA - 96.5 2 NA NA NA NA NA - 97 1 1 NA NA NA NA - 97.1 3 3 NA NA NA NA - 98 3 NA NA NA NA NA - 98.1 1 3 NA NA NA NA - 98.2 3 1 NA NA NA NA - 99 1 2 NA NA NA NA + 94.4 3 1 NA NA NA NA + 94.5 2 2 NA NA NA NA + 95 2 2 NA NA NA NA + 95.1 3 2 NA NA NA NA + 95.2 2 NA NA NA NA NA + 96 3 1 NA NA NA NA + 96.1 2 1 NA NA NA NA + 96.2 3 2 NA NA NA NA + 96.3 2 3 NA NA NA NA + 96.4 2 2 NA NA NA NA + 96.5 3 NA NA NA NA NA + 97 3 1 NA NA NA NA + 97.1 3 2 NA NA NA NA + 98 2 3 NA NA NA NA + 98.1 3 2 NA NA NA NA + 98.2 1 2 NA NA NA NA + 99 2 2 NA NA NA NA 99.1 1 2 NA NA NA NA - 99.2 2 NA NA NA NA NA - 100 3 2 NA NA NA NA - 100.1 2 NA NA NA NA NA + 99.2 3 1 NA NA NA NA + 100 2 1 NA NA NA NA + 100.1 1 2 NA NA NA NA 100.2 2 3 NA NA NA NA - 100.3 3 3 NA NA NA NA - 100.4 1 3 NA NA NA NA + 100.3 2 2 NA NA NA NA + 100.4 3 1 NA NA NA NA $m4a$spM_id center scale @@ -6203,8 +6203,8 @@ m2 NA NA m2B NA NA m2C NA NA - m2B:abs(C1 - C2) 0.4354752 0.7317719 - m2C:abs(C1 - C2) 0.4780416 0.7965218 + m2B:abs(C1 - C2) 0.4042255 0.7594704 + m2C:abs(C1 - C2) 0.5491518 0.8130082 $m4a$mu_reg_norm [1] 0 @@ -6358,335 +6358,335 @@ $m4b$M_lvlone m1 m2 ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) - 1 2 3 NA - 1.1 2 3 NA - 1.2 2 1 NA - 1.3 2 2 NA - 2 1 3 NA - 2.1 1 1 NA + 1 3 3 NA + 1.1 2 1 NA + 1.2 1 3 NA + 1.3 1 1 NA + 2 2 2 NA + 2.1 2 1 NA 2.2 1 NA NA - 3 2 1 NA - 3.1 1 1 NA - 3.2 2 3 NA - 4 3 2 NA - 4.1 3 3 NA - 4.2 3 3 NA - 4.3 2 2 NA - 5 2 1 NA - 5.1 2 1 NA - 5.2 3 3 NA - 5.3 2 3 NA + 3 1 3 NA + 3.1 2 2 NA + 3.2 2 1 NA + 4 2 1 NA + 4.1 1 2 NA + 4.2 2 3 NA + 4.3 3 3 NA + 5 2 2 NA + 5.1 1 3 NA + 5.2 2 1 NA + 5.3 2 1 NA 6 2 2 NA - 7 1 3 NA - 7.1 1 1 NA - 7.2 1 1 NA - 8 3 3 NA - 8.1 3 NA NA - 8.2 3 2 NA - 8.3 1 1 NA - 8.4 2 1 NA - 8.5 3 3 NA - 9 2 NA NA - 9.1 2 2 NA - 9.2 3 2 NA - 10 1 2 NA - 10.1 3 2 NA - 11 3 3 NA - 11.1 1 2 NA + 7 3 2 NA + 7.1 2 1 NA + 7.2 3 3 NA + 8 2 2 NA + 8.1 1 2 NA + 8.2 3 1 NA + 8.3 2 3 NA + 8.4 2 NA NA + 8.5 2 3 NA + 9 3 NA NA + 9.1 2 3 NA + 9.2 3 1 NA + 10 3 1 NA + 10.1 1 1 NA + 11 1 1 NA + 11.1 1 1 NA 11.2 2 1 NA - 11.3 1 2 NA - 11.4 3 1 NA - 12 2 3 NA - 13 1 NA NA + 11.3 3 NA NA + 11.4 1 1 NA + 12 1 1 NA + 13 2 2 NA 13.1 3 2 NA - 14 1 2 NA - 14.1 3 1 NA - 14.2 3 3 NA - 14.3 2 2 NA - 15 2 NA NA - 15.1 2 1 NA - 15.2 3 1 NA - 15.3 3 3 NA - 16 1 3 NA - 16.1 2 2 NA - 16.2 1 NA NA - 16.3 3 3 NA - 16.4 2 1 NA - 16.5 3 NA NA - 17 1 3 NA - 17.1 2 1 NA - 17.2 1 3 NA - 17.3 2 3 NA + 14 1 3 NA + 14.1 1 2 NA + 14.2 1 1 NA + 14.3 3 1 NA + 15 1 1 NA + 15.1 1 2 NA + 15.2 3 3 NA + 15.3 2 3 NA + 16 2 2 NA + 16.1 2 NA NA + 16.2 1 3 NA + 16.3 3 2 NA + 16.4 2 3 NA + 16.5 1 1 NA + 17 2 1 NA + 17.1 3 3 NA + 17.2 1 NA NA + 17.3 1 2 NA 17.4 2 1 NA - 18 1 1 NA - 19 2 3 NA - 19.1 1 2 NA - 19.2 2 1 NA - 19.3 3 1 NA - 20 2 1 NA - 20.1 3 3 NA - 20.2 2 2 NA - 20.3 2 NA NA - 20.4 1 3 NA - 20.5 2 3 NA - 21 3 1 NA - 21.1 2 2 NA - 21.2 2 3 NA - 22 2 NA NA - 22.1 1 2 NA - 23 1 2 NA + 18 1 3 NA + 19 2 NA NA + 19.1 3 1 NA + 19.2 2 3 NA + 19.3 3 3 NA + 20 2 2 NA + 20.1 2 NA NA + 20.2 1 3 NA + 20.3 3 1 NA + 20.4 2 3 NA + 20.5 3 2 NA + 21 1 3 NA + 21.1 2 1 NA + 21.2 3 NA NA + 22 2 3 NA + 22.1 2 1 NA + 23 2 1 NA 23.1 1 2 NA - 24 3 1 NA - 25 3 NA NA - 25.1 3 1 NA - 25.2 1 1 NA - 25.3 1 2 NA - 25.4 2 NA NA - 25.5 2 2 NA - 26 1 3 NA - 26.1 1 1 NA - 26.2 2 2 NA - 26.3 1 1 NA - 27 1 1 NA - 27.1 2 3 NA - 28 3 3 NA - 28.1 2 NA NA - 28.2 3 NA NA - 28.3 2 NA NA - 29 1 3 NA - 29.1 3 1 NA - 29.2 1 1 NA - 29.3 3 2 NA - 30 2 3 NA - 30.1 2 2 NA - 30.2 1 2 NA - 31 3 NA NA - 32 3 1 NA - 32.1 1 NA NA - 32.2 1 3 NA - 32.3 3 2 NA - 33 3 1 NA - 33.1 2 2 NA - 34 3 1 NA - 34.1 2 3 NA - 34.2 1 3 NA - 34.3 2 3 NA - 35 3 1 NA - 35.1 1 3 NA - 35.2 2 2 NA - 36 1 3 NA - 36.1 2 NA NA + 24 1 2 NA + 25 1 2 NA + 25.1 3 3 NA + 25.2 2 3 NA + 25.3 2 1 NA + 25.4 1 3 NA + 25.5 1 2 NA + 26 2 NA NA + 26.1 1 3 NA + 26.2 1 3 NA + 26.3 2 NA NA + 27 1 3 NA + 27.1 3 3 NA + 28 1 3 NA + 28.1 3 2 NA + 28.2 1 2 NA + 28.3 1 3 NA + 29 3 1 NA + 29.1 3 NA NA + 29.2 3 2 NA + 29.3 2 2 NA + 30 1 2 NA + 30.1 3 3 NA + 30.2 3 3 NA + 31 1 3 NA + 32 3 3 NA + 32.1 3 3 NA + 32.2 2 1 NA + 32.3 1 1 NA + 33 3 3 NA + 33.1 1 3 NA + 34 1 3 NA + 34.1 1 NA NA + 34.2 2 1 NA + 34.3 2 NA NA + 35 1 2 NA + 35.1 1 2 NA + 35.2 1 2 NA + 36 2 3 NA + 36.1 3 3 NA 36.2 3 3 NA - 36.3 2 NA NA - 36.4 3 NA NA - 37 1 NA NA - 37.1 2 2 NA - 37.2 3 3 NA - 38 1 2 NA - 39 3 NA NA + 36.3 3 2 NA + 36.4 3 2 NA + 37 1 2 NA + 37.1 3 2 NA + 37.2 1 1 NA + 38 2 2 NA + 39 2 3 NA 39.1 3 2 NA - 39.2 3 1 NA - 39.3 3 NA NA - 39.4 2 2 NA - 39.5 2 1 NA - 40 2 3 NA - 40.1 3 3 NA - 40.2 2 NA NA - 40.3 2 2 NA - 41 1 1 NA - 41.1 2 2 NA - 41.2 2 3 NA - 41.3 2 3 NA - 41.4 1 1 NA - 42 3 2 NA - 42.1 2 NA NA - 43 3 2 NA + 39.2 1 3 NA + 39.3 2 NA NA + 39.4 3 3 NA + 39.5 3 3 NA + 40 3 3 NA + 40.1 3 1 NA + 40.2 1 3 NA + 40.3 3 2 NA + 41 3 3 NA + 41.1 3 3 NA + 41.2 1 1 NA + 41.3 1 2 NA + 41.4 1 3 NA + 42 1 2 NA + 42.1 1 NA NA + 43 3 3 NA 43.1 3 3 NA - 43.2 1 3 NA + 43.2 2 2 NA 44 2 3 NA - 44.1 2 1 NA - 44.2 3 2 NA + 44.1 2 3 NA + 44.2 1 NA NA 44.3 1 1 NA - 45 1 3 NA - 45.1 1 2 NA - 46 2 2 NA - 46.1 1 3 NA + 45 2 3 NA + 45.1 3 1 NA + 46 3 NA NA + 46.1 2 1 NA 46.2 3 2 NA - 47 1 3 NA - 47.1 1 3 NA - 47.2 2 2 NA - 47.3 3 3 NA - 47.4 2 2 NA - 48 3 NA NA - 48.1 3 1 NA - 49 1 2 NA - 50 2 NA NA - 51 2 NA NA + 47 1 2 NA + 47.1 2 NA NA + 47.2 2 NA NA + 47.3 2 3 NA + 47.4 2 3 NA + 48 3 3 NA + 48.1 1 1 NA + 49 3 1 NA + 50 1 NA NA + 51 3 1 NA 52 3 2 NA - 52.1 3 3 NA - 52.2 2 3 NA - 52.3 2 2 NA - 52.4 1 2 NA - 52.5 2 1 NA - 53 3 2 NA - 53.1 2 2 NA - 53.2 1 3 NA - 54 1 NA NA - 54.1 3 3 NA - 54.2 1 3 NA - 54.3 3 1 NA - 54.4 1 NA NA - 55 3 1 NA + 52.1 2 1 NA + 52.2 1 1 NA + 52.3 3 NA NA + 52.4 3 2 NA + 52.5 3 3 NA + 53 1 2 NA + 53.1 3 1 NA + 53.2 2 2 NA + 54 3 NA NA + 54.1 3 1 NA + 54.2 3 NA NA + 54.3 1 3 NA + 54.4 1 3 NA + 55 1 1 NA 55.1 3 1 NA - 55.2 1 3 NA - 55.3 3 1 NA - 55.4 1 1 NA - 56 3 3 NA - 56.1 3 1 NA - 56.2 2 2 NA - 56.3 3 2 NA - 56.4 1 3 NA - 56.5 2 2 NA - 57 2 3 NA - 57.1 1 2 NA - 57.2 3 NA NA - 57.3 2 1 NA - 58 2 1 NA - 58.1 3 NA NA - 58.2 1 2 NA - 58.3 1 3 NA - 58.4 1 3 NA - 58.5 1 2 NA - 59 1 2 NA - 59.1 1 3 NA - 60 3 NA NA - 61 1 NA NA - 61.1 2 NA NA - 61.2 1 1 NA - 61.3 1 2 NA - 61.4 2 3 NA - 62 2 3 NA - 62.1 3 3 NA - 62.2 3 2 NA - 62.3 1 2 NA - 63 1 1 NA - 63.1 2 1 NA - 64 3 1 NA - 65 3 2 NA - 65.1 3 NA NA - 65.2 2 2 NA - 65.3 2 1 NA - 66 2 3 NA - 66.1 3 1 NA - 66.2 2 3 NA - 67 3 3 NA - 68 3 2 NA - 68.1 1 3 NA - 68.2 3 1 NA + 55.2 2 1 NA + 55.3 1 NA NA + 55.4 1 2 NA + 56 2 2 NA + 56.1 1 3 NA + 56.2 3 1 NA + 56.3 1 1 NA + 56.4 2 2 NA + 56.5 1 NA NA + 57 1 2 NA + 57.1 1 3 NA + 57.2 1 2 NA + 57.3 1 NA NA + 58 3 1 NA + 58.1 2 1 NA + 58.2 1 NA NA + 58.3 3 1 NA + 58.4 3 2 NA + 58.5 3 NA NA + 59 3 1 NA + 59.1 1 1 NA + 60 3 1 NA + 61 1 2 NA + 61.1 2 1 NA + 61.2 2 1 NA + 61.3 3 2 NA + 61.4 2 2 NA + 62 2 1 NA + 62.1 1 1 NA + 62.2 3 NA NA + 62.3 2 1 NA + 63 3 NA NA + 63.1 1 3 NA + 64 3 3 NA + 65 3 NA NA + 65.1 3 2 NA + 65.2 2 3 NA + 65.3 3 3 NA + 66 3 3 NA + 66.1 3 3 NA + 66.2 1 1 NA + 67 3 NA NA + 68 3 1 NA + 68.1 1 1 NA + 68.2 2 1 NA 68.3 3 2 NA 68.4 1 3 NA - 69 2 1 NA - 70 2 NA NA - 70.1 3 1 NA - 71 1 2 NA - 71.1 3 2 NA - 71.2 1 3 NA - 71.3 2 1 NA - 71.4 1 NA NA - 72 1 3 NA - 72.1 2 2 NA - 72.2 3 1 NA - 72.3 3 1 NA - 72.4 1 3 NA - 72.5 2 3 NA - 73 1 2 NA - 74 2 1 NA - 75 1 2 NA - 76 3 3 NA - 76.1 1 3 NA - 76.2 1 3 NA - 77 1 3 NA - 78 1 2 NA - 79 2 1 NA + 69 1 NA NA + 70 1 1 NA + 70.1 2 NA NA + 71 3 1 NA + 71.1 2 1 NA + 71.2 2 NA NA + 71.3 1 1 NA + 71.4 2 1 NA + 72 1 2 NA + 72.1 2 3 NA + 72.2 1 2 NA + 72.3 2 1 NA + 72.4 2 2 NA + 72.5 1 1 NA + 73 2 NA NA + 74 1 1 NA + 75 3 NA NA + 76 3 1 NA + 76.1 3 2 NA + 76.2 2 2 NA + 77 2 NA NA + 78 2 1 NA + 79 2 3 NA 79.1 2 3 NA - 79.2 3 NA NA - 80 3 1 NA - 80.1 2 NA NA - 80.2 3 3 NA - 81 2 3 NA - 81.1 3 1 NA - 81.2 2 NA NA - 81.3 1 3 NA - 82 3 2 NA - 82.1 2 3 NA - 82.2 1 1 NA - 83 1 2 NA - 83.1 3 1 NA - 83.2 2 3 NA - 83.3 2 3 NA - 84 2 3 NA - 84.1 1 1 NA + 79.2 2 NA NA + 80 2 3 NA + 80.1 1 2 NA + 80.2 3 NA NA + 81 2 1 NA + 81.1 3 2 NA + 81.2 2 1 NA + 81.3 1 1 NA + 82 1 3 NA + 82.1 2 1 NA + 82.2 3 1 NA + 83 2 2 NA + 83.1 3 3 NA + 83.2 3 2 NA + 83.3 3 3 NA + 84 2 1 NA + 84.1 3 2 NA 85 1 2 NA - 85.1 1 1 NA - 85.2 2 2 NA - 85.3 2 3 NA - 85.4 3 3 NA - 85.5 3 2 NA - 86 1 3 NA - 86.1 2 3 NA - 86.2 3 1 NA - 86.3 3 NA NA - 86.4 1 1 NA - 86.5 3 3 NA - 87 2 NA NA - 87.1 3 3 NA - 87.2 2 1 NA - 88 1 3 NA - 88.1 3 1 NA - 88.2 3 3 NA + 85.1 2 1 NA + 85.2 3 1 NA + 85.3 3 NA NA + 85.4 2 2 NA + 85.5 2 1 NA + 86 1 1 NA + 86.1 2 NA NA + 86.2 1 2 NA + 86.3 1 1 NA + 86.4 1 2 NA + 86.5 2 2 NA + 87 3 NA NA + 87.1 3 1 NA + 87.2 2 NA NA + 88 3 1 NA + 88.1 3 2 NA + 88.2 3 NA NA 88.3 1 2 NA - 89 1 2 NA - 90 3 3 NA + 89 2 3 NA + 90 1 3 NA 90.1 2 2 NA - 90.2 2 3 NA + 90.2 2 NA NA 90.3 2 2 NA - 91 1 1 NA - 91.1 3 2 NA - 91.2 2 2 NA + 91 3 3 NA + 91.1 3 1 NA + 91.2 3 3 NA 92 2 2 NA - 93 2 1 NA - 93.1 1 2 NA - 93.2 1 3 NA + 93 2 2 NA + 93.1 2 3 NA + 93.2 2 NA NA 93.3 3 2 NA - 93.4 2 1 NA - 94 1 1 NA - 94.1 1 2 NA - 94.2 2 NA NA + 93.4 2 3 NA + 94 2 2 NA + 94.1 3 2 NA + 94.2 3 1 NA 94.3 2 2 NA - 94.4 2 NA NA - 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0.6701303425 4.490747e-01 NA -0.4857246503 0.4879672359 NA + 96.3 2 NA 2.1775037691 4.741523e+00 NA 0.8771471244 1.5855877997 NA 96.4 2 1 2.2246142488 4.948909e+00 NA 1.9030768981 1.6198921270 NA - 96.5 2 1 4.2377650598 1.795865e+01 NA -0.1684332749 3.0858034196 NA - 97 1 0 1.1955102731 1.429245e+00 NA 1.3775130083 0.8662239621 NA + 96.5 3 1 4.2377650598 1.795865e+01 NA -0.1684332749 3.0858034196 NA + 97 3 0 1.1955102731 1.429245e+00 NA 1.3775130083 0.8662239621 NA 97.1 3 0 4.9603108643 2.460468e+01 NA -1.7323228619 3.5940637456 NA - 98 3 0 0.2041732438 4.168671e-02 NA -1.2648518889 0.1536799385 NA - 98.1 1 0 0.4309578973 1.857247e-01 NA -0.9042716241 0.3243793452 NA - 98.2 3 1 3.5172611906 1.237113e+01 NA -0.1560385207 2.6474207553 NA - 99 1 0 0.3531786101 1.247351e-01 NA 0.7993356425 0.2568424071 NA + 98 2 0 0.2041732438 4.168671e-02 NA -1.2648518889 0.1536799385 NA + 98.1 3 0 0.4309578973 1.857247e-01 NA -0.9042716241 0.3243793452 NA + 98.2 1 1 3.5172611906 1.237113e+01 NA 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m1 log(time) I(time^2) p1 time - 1 2 -0.67522439 2.591239e-01 5 0.5090421822 + 1 3 -0.67522439 2.591239e-01 5 0.5090421822 1.1 2 -0.40555367 4.443657e-01 3 0.6666076288 - 1.2 2 0.75635394 4.539005e+00 8 2.1304941282 - 1.3 2 0.91446673 6.227241e+00 6 2.4954441458 - 2 1 1.10409692 9.099267e+00 5 3.0164990982 - 2.1 1 1.19382570 1.088789e+01 3 3.2996806887 + 1.2 1 0.75635394 4.539005e+00 8 2.1304941282 + 1.3 1 0.91446673 6.227241e+00 6 2.4954441458 + 2 2 1.10409692 9.099267e+00 5 3.0164990982 + 2.1 2 1.19382570 1.088789e+01 3 3.2996806887 2.2 1 1.42905614 1.742860e+01 2 4.1747569619 - 3 2 -0.16502467 7.188883e-01 7 0.8478727890 - 3.1 1 1.12018813 9.396866e+00 2 3.0654308549 + 3 1 -0.16502467 7.188883e-01 7 0.8478727890 + 3.1 2 1.12018813 9.396866e+00 2 3.0654308549 3.2 2 1.55564789 2.245012e+01 8 4.7381553578 - 4 3 -1.08724748 1.136655e-01 2 0.3371432109 - 4.1 3 0.06700602 1.143407e+00 4 1.0693019140 - 4.2 3 0.96122482 6.837688e+00 2 2.6148973033 - 4.3 2 1.14219951 9.819783e+00 6 3.1336532847 + 4 2 -1.08724748 1.136655e-01 2 0.3371432109 + 4.1 1 0.06700602 1.143407e+00 4 1.0693019140 + 4.2 2 0.96122482 6.837688e+00 2 2.6148973033 + 4.3 3 1.14219951 9.819783e+00 6 3.1336532847 5 2 0.07348511 1.158319e+00 6 1.0762525082 - 5.1 2 0.58291628 3.208593e+00 2 1.7912546196 - 5.2 3 1.02819270 7.817661e+00 3 2.7960080339 + 5.1 1 0.58291628 3.208593e+00 2 1.7912546196 + 5.2 2 1.02819270 7.817661e+00 3 2.7960080339 5.3 2 1.03389386 7.907311e+00 2 2.8119940578 6 2 0.57748169 3.173907e+00 4 1.7815462884 - 7 1 1.19616503 1.093895e+01 2 3.3074087673 - 7.1 1 1.30855992 1.369622e+01 6 3.7008403614 - 7.2 1 1.56269618 2.276883e+01 4 4.7716691741 - 8 3 0.11746285 1.264815e+00 2 1.1246398522 - 8.1 3 0.58928609 3.249731e+00 2 1.8027009873 + 7 3 1.19616503 1.093895e+01 2 3.3074087673 + 7.1 2 1.30855992 1.369622e+01 6 3.7008403614 + 7.2 3 1.56269618 2.276883e+01 4 4.7716691741 + 8 2 0.11746285 1.264815e+00 2 1.1246398522 + 8.1 1 0.58928609 3.249731e+00 2 1.8027009873 8.2 3 0.59750733 3.303606e+00 1 1.8175825174 - 8.3 1 1.04324992 8.056666e+00 2 2.8384267003 + 8.3 2 1.04324992 8.056666e+00 2 2.8384267003 8.4 2 1.21284162 1.130995e+01 2 3.3630275307 - 8.5 3 1.48977222 1.967885e+01 4 4.4360849704 - 9 2 -0.04000943 9.230989e-01 3 0.9607803822 + 8.5 2 1.48977222 1.967885e+01 4 4.4360849704 + 9 3 -0.04000943 9.230989e-01 3 0.9607803822 9.1 2 1.07082146 8.513413e+00 3 2.9177753383 9.2 3 1.57071564 2.313696e+01 2 4.8100892501 - 10 1 0.83184373 5.278740e+00 4 2.2975509102 - 10.1 3 1.42873389 1.741737e+01 5 4.1734118364 - 11 3 0.16827866 1.400119e+00 2 1.1832662905 + 10 3 0.83184373 5.278740e+00 4 2.2975509102 + 10.1 1 1.42873389 1.741737e+01 5 4.1734118364 + 11 1 0.16827866 1.400119e+00 2 1.1832662905 11.1 1 0.21075122 1.524250e+00 4 1.2346051680 11.2 2 0.49684736 2.701196e+00 6 1.6435316263 - 11.3 1 1.21962028 1.146433e+01 2 3.3859017969 - 11.4 3 1.57107306 2.315350e+01 1 4.8118087661 - 12 2 -0.04165702 9.200622e-01 5 0.9591987054 - 13 1 -2.78209660 3.832672e-03 2 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2 0.99582370 7.327595e+00 3 2.7069531474 + 86.5 2 1.34040901 1.459703e+01 4 3.8206058508 + 87 3 0.99582370 7.327595e+00 3 2.7069531474 87.1 3 1.23728720 1.187665e+01 6 3.4462517721 87.2 2 1.50943340 2.046808e+01 2 4.5241666853 - 88 1 -7.43666969 3.472088e-07 1 0.0005892443 + 88 3 -7.43666969 3.472088e-07 1 0.0005892443 88.1 3 -0.34022529 5.063888e-01 6 0.7116099866 88.2 3 0.91439786 6.226384e+00 1 2.4952722900 88.3 1 1.19379568 1.088724e+01 6 3.2995816297 - 89 1 -0.43663289 4.175856e-01 7 0.6462086167 - 90 3 -1.77429443 2.876520e-02 3 0.1696030737 + 89 2 -0.43663289 4.175856e-01 7 0.6462086167 + 90 1 -1.77429443 2.876520e-02 3 0.1696030737 90.1 2 0.95475675 6.749804e+00 8 2.5980385230 90.2 2 0.98025630 7.102967e+00 4 2.6651392167 90.3 2 1.13920034 9.761057e+00 2 3.1242690247 - 91 1 -0.44900667 4.073782e-01 4 0.6382618390 + 91 3 -0.44900667 4.073782e-01 4 0.6382618390 91.1 3 0.96409219 6.877013e+00 2 2.6224059286 - 91.2 2 1.56386564 2.282214e+01 5 4.7772527603 + 91.2 3 1.56386564 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1.918945e+01 5 4.3805759016 + 95 2 0.70305113 4.080021e+00 8 2.0199063048 + 95.1 3 1.39089487 1.614790e+01 4 4.0184444457 95.2 2 1.51724656 2.079044e+01 1 4.5596531732 - 96 2 -3.46947576 9.692853e-04 2 0.0311333477 - 96.1 3 -2.02172545 1.753685e-02 3 0.1324267720 - 96.2 1 -0.40028304 4.490747e-01 2 0.6701303425 - 96.3 3 0.77817916 4.741523e+00 6 2.1775037691 + 96 3 -3.46947576 9.692853e-04 2 0.0311333477 + 96.1 2 -2.02172545 1.753685e-02 3 0.1324267720 + 96.2 3 -0.40028304 4.490747e-01 2 0.6701303425 + 96.3 2 0.77817916 4.741523e+00 6 2.1775037691 96.4 2 0.79958353 4.948909e+00 6 2.2246142488 - 96.5 2 1.44403602 1.795865e+01 3 4.2377650598 - 97 1 0.17857310 1.429245e+00 2 1.1955102731 + 96.5 3 1.44403602 1.795865e+01 3 4.2377650598 + 97 3 0.17857310 1.429245e+00 2 1.1955102731 97.1 3 1.60146841 2.460468e+01 5 4.9603108643 - 98 3 -1.58878641 4.168671e-02 7 0.2041732438 - 98.1 1 -0.84174488 1.857247e-01 2 0.4309578973 - 98.2 3 1.25768262 1.237113e+01 6 3.5172611906 - 99 1 -1.04078137 1.247351e-01 3 0.3531786101 + 98 2 -1.58878641 4.168671e-02 7 0.2041732438 + 98.1 3 -0.84174488 1.857247e-01 2 0.4309578973 + 98.2 1 1.25768262 1.237113e+01 6 3.5172611906 + 99 2 -1.04078137 1.247351e-01 3 0.3531786101 99.1 1 1.54307253 2.189252e+01 4 4.6789444226 - 99.2 2 1.60797853 2.492714e+01 5 4.9927084171 - 100 3 0.06685343 1.143058e+00 2 1.0691387602 - 100.1 2 0.41272829 2.282923e+00 3 1.5109344281 + 99.2 3 1.60797853 2.492714e+01 5 4.9927084171 + 100 2 0.06685343 1.143058e+00 2 1.0691387602 + 100.1 1 0.41272829 2.282923e+00 3 1.5109344281 100.2 2 0.76557633 4.623503e+00 3 2.1502332564 - 100.3 3 1.35443144 1.501220e+01 7 3.8745574222 - 100.4 1 1.53832012 2.168542e+01 6 4.6567608765 + 100.3 2 1.35443144 1.501220e+01 7 3.8745574222 + 100.4 3 1.53832012 2.168542e+01 6 4.6567608765 $m4e$spM_id center scale diff --git a/tests/testthat/test-mlogitmm.R b/tests/testthat/test-mlogitmm.R index 206ad52a..5a3e64c4 100644 --- a/tests/testthat/test-mlogitmm.R +++ b/tests/testthat/test-mlogitmm.R @@ -4,6 +4,8 @@ library("JointAI") skip_on_cran() +cat("ON CRAN:", testthat:::on_cran(), "\n") + set_seed(2020) longDF$m1 <- factor(sample(c('A', 'B', 'C'), size = nrow(longDF), replace = TRUE)) @@ -17,6 +19,7 @@ run_mlogitmm_models <- function() { on.exit(sink()) invisible(force(suppressWarnings({ + models <- list( # no covariates From 4f92bd0bb78fef1bc2ad15cb062bcdd0c07bbd58 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 2 Sep 2022 09:06:22 +0200 Subject: [PATCH 126/176] trigger re-build --- README.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/README.Rmd b/README.Rmd index 7f897a4a..b2dbc949 100644 --- a/README.Rmd +++ b/README.Rmd @@ -28,7 +28,6 @@ knitr::opts_chunk$set( - The package **JointAI** provides functionality to perform joint analysis and imputation of a range of model types in the Bayesian framework. Implemented are (generalized) linear regression models and extensions thereof, models for From 10adcadb7f067e851e9c256d7eeb8fe338f03648 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 2 Sep 2022 12:43:25 +0200 Subject: [PATCH 127/176] update action test-coverage [skip-check] --- .github/workflows/test-coverage.yaml | 62 +++++++++------------------- 1 file changed, 20 insertions(+), 42 deletions(-) diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml index 53df6db4..3d13624b 100644 --- a/.github/workflows/test-coverage.yaml +++ b/.github/workflows/test-coverage.yaml @@ -1,63 +1,41 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: - branches: - - master - - JMdevel + branches: [main, master] pull_request: - branches: - - master + branches: [main, master] name: test-coverage jobs: test-coverage: - runs-on: windows-latest - if: "! contains(toJSON(github.event.commits.*.message), '[skip-covr]')" - + runs-on: ubuntu-latest env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-r@master - - - uses: r-lib/actions/setup-pandoc@master - - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} - - - name: Cache R packages - uses: actions/cache@v1 + - uses: r-lib/actions/setup-r@v2 with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- + use-public-rspm: true - - name: Download JAGS Windows - if: runner.os == 'Windows' - run: (New-Object System.Net.WebClient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe', 'C:\JAGS-4.3.1.exe') - shell: powershell - - name: Install JAGS Windows - if: runner.os == 'Windows' - run: C:\JAGS-4.3.1.exe /S - shell: cmd + - name: Install system dependencies + if: runner.os == 'Linux' + run: | + while read -r cmd + do + eval sudo $cmd + done < <(Rscript -e 'cat(remotes::system_requirements("ubuntu", "20.04"), sep = "\n")') - - name: Install dependencies - run: | - install.packages(c("remotes")) - remotes::install_deps(dependencies = TRUE) - remotes::install_cran("covr") - shell: Rscript {0} + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::covr + needs: coverage - name: Test coverage - env: - IS_CHECK: true - run: | - covr::codecov(quiet = FALSE, type = "all") + run: covr::codecov(quiet = FALSE) shell: Rscript {0} From 4af70c0a96dd63bd5ba43557dc569b58e84293fb Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 2 Sep 2022 14:13:26 +0200 Subject: [PATCH 128/176] remove unneeded test output --- .../testout/clm_lapply.models.data_list..rds | Bin 6721 -> 0 bytes .../testout/clm_lapply.models.jagsmodel..txt | 1867 -------- .../clm_lapply.models0.GR_crit.multiva.txt | 387 -- .../testout/clm_lapply.models0.MC_error..txt | 578 --- .../testout/clm_lapply.models0.coef..txt | 166 - .../testout/clm_lapply.models0.confint..txt | 359 -- .../clm_lapply.models0.function.x.coef.txt | 572 --- .../testout/clm_lapply.models0.print..txt | 616 --- .../testout/clm_lapply.models0.summary..txt | 1020 ----- .../testout/clmm_lapply.models.data_list..rds | Bin 72460 -> 0 bytes .../testout/clmm_lapply.models.jagsmodel..txt | 2679 ----------- .../clmm_lapply.models0.GR_crit.multiva.txt | 467 -- 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{ - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- 0 - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - } -$m0b -model { - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- 0 - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - } -$m1a -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - } -$m1b -model { - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - } - - # Priors for the model for O2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - } -$m2a -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } -$m2b -model { - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - } - - # Priors for the model for O2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } -$m3a -model { - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] + - M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4] - } - - # Priors for the model for C1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - } -$m3b -model { - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] - } - - # Priors for the model for C1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- 0 - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - } -$m4a -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 6] * beta[1] + M_lvlone[i, 7] * beta[2] + - M_lvlone[i, 8] * beta[3] + M_lvlone[i, 9] * beta[4] + - M_lvlone[i, 10] * beta[5] + M_lvlone[i, 11] * beta[6] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[7] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[8] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[9] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[10] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[11] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] - - M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] - M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] - M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] - } - - } -$m4b -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] - } - - # Priors for the model for O1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] + - M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] + - M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] + - M_lvlone[i, 14] * alpha[7] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8] - - M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- M_lvlone[i, 12] * alpha[9] + M_lvlone[i, 13] * alpha[10] + - M_lvlone[i, 14] * alpha[11] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[12] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - - M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0) - - } - - # Priors for the model for O2 - for (k in 9:12) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] - } - - } -$m5a -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - } -$m5b -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:13) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] - } - - } -$m5c -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] - } - - } -$m5d -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] - M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] - M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] - } - - } -$m5e -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- 0 - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] + - M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] + - M_lvlone[i, 9] * beta[4] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + - M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] + - M_lvlone[i, 12] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] + - M_lvlone[i, 9] * beta[15] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] + - M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] + - M_lvlone[i, 12] * beta[19] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] + - M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] + - M_lvlone[i, 9] * beta[26] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] + - M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] + - M_lvlone[i, 12] * beta[30] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33] - - p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4]))) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 1] - psum_O1[i, 2])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 3])) - p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3])) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:33) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] - exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] - M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] - M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] - } - - } -$m6a -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - } -$m6b -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:13) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] - } - - } -$m6c -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2] - } - - } -$m6d -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] + - M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] + - M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9] - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] - M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] - M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] - } - - } -$m6e -model { - - # Cumulative logit model for O1 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4]) - eta_O1[i] <- 0 - - eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] + - M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] + - M_lvlone[i, 9] * beta[4] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + - M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] + - M_lvlone[i, 12] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] - eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] + - M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] + - M_lvlone[i, 9] * beta[15] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] + - M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] + - M_lvlone[i, 12] * beta[19] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22] - eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] + - M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] + - M_lvlone[i, 9] * beta[26] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] + - M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] + - M_lvlone[i, 12] * beta[30] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33] - - p_O1[i, 1] <- max(1e-10, min(1-1e-10, psum_O1[i, 1])) - p_O1[i, 2] <- max(1e-10, min(1-1e-10, psum_O1[i, 2] - psum_O1[i, 1])) - p_O1[i, 3] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2])) - p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3]))) - - logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i] + eta_O1_1[i] - logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i] + eta_O1_2[i] - logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i] - } - - # Priors for the model for O1 - for (k in 1:33) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O1[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O1[2] <- gamma_O1[1] + exp(delta_O1[1]) - gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2]) - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8] - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - M_lvlone[i, 12] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - M_lvlone[i, 12] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - M_lvlone[i, 12] * alpha[23] - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2] - M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2] - M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2] - } - - } diff --git a/tests/testthat/testout/clm_lapply.models0.GR_crit.multiva.txt b/tests/testthat/testout/clm_lapply.models0.GR_crit.multiva.txt deleted file mode 100644 index b38dec61..00000000 --- a/tests/testthat/testout/clm_lapply.models0.GR_crit.multiva.txt +++ /dev/null @@ -1,387 +0,0 @@ -$m0a -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m0b -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_O2[1] NaN NaN -gamma_O2[2] NaN NaN -gamma_O2[3] NaN NaN - - -$m1a -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN -C1 NaN NaN - - -$m1b -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_O2[1] NaN NaN -gamma_O2[2] NaN NaN -gamma_O2[3] NaN NaN -C1 NaN NaN - - -$m2a -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN -C2 NaN NaN - - -$m2b -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_O2[1] NaN NaN -gamma_O2[2] NaN NaN -gamma_O2[3] NaN NaN -C2 NaN NaN - - -$m3a -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -O1.L NaN NaN -O1.Q NaN NaN -O1.C NaN NaN -sigma_C1 NaN NaN - - -$m3b -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -sigma_C1 NaN NaN - - -$m4a -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -abs(C1 - C2) NaN NaN -log(C1) NaN NaN -O22:abs(C1 - C2) NaN NaN -O23:abs(C1 - C2) NaN NaN -O24:abs(C1 - C2) NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m4b -Potential scale reduction factors: - - Point est. -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN -abs(C1 - C2) NaN -log(C1) NaN -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -gamma_O1[1] NaN -gamma_O1[2] NaN -gamma_O1[3] NaN - Upper C.I. -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN -abs(C1 - C2) NaN -log(C1) NaN -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -gamma_O1[1] NaN -gamma_O1[2] NaN -gamma_O1[3] NaN - - -$m5a -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -O12: C1 NaN NaN -O12: C2 NaN NaN -O13: C1 NaN NaN -O13: C2 NaN NaN -O14: C1 NaN NaN -O14: C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m5b -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -C1:C2 NaN NaN -O12: C1 NaN NaN -O12: C2 NaN NaN -O13: C1 NaN NaN -O13: C2 NaN NaN -O14: C1 NaN NaN -O14: C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m5c -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -O12: C1 NaN NaN -O12: C2 NaN NaN -O12: C1:C2 NaN NaN -O13: C1 NaN NaN -O13: C2 NaN NaN -O13: C1:C2 NaN NaN -O14: C1 NaN NaN -O14: C2 NaN NaN -O14: C1:C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m5d -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -M22:C2 NaN NaN -M23:C2 NaN NaN -M24:C2 NaN NaN -O12: C1 NaN NaN -O12: C2 NaN NaN -O13: C1 NaN NaN -O13: C2 NaN NaN -O14: C1 NaN NaN -O14: C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m5e -Potential scale reduction factors: - - Point est. Upper C.I. -O12: C1 NaN NaN -O12: M22 NaN NaN -O12: M23 NaN NaN -O12: M24 NaN NaN -O12: C2 NaN NaN -O12: O22 NaN NaN -O12: O23 NaN NaN -O12: O24 NaN NaN -O12: M22:C2 NaN NaN -O12: M23:C2 NaN NaN -O12: M24:C2 NaN NaN -O13: C1 NaN NaN -O13: M22 NaN NaN -O13: M23 NaN NaN -O13: M24 NaN NaN -O13: C2 NaN NaN -O13: O22 NaN NaN -O13: O23 NaN NaN -O13: O24 NaN NaN -O13: M22:C2 NaN NaN -O13: M23:C2 NaN NaN -O13: M24:C2 NaN NaN -O14: C1 NaN NaN -O14: M22 NaN NaN -O14: M23 NaN NaN -O14: M24 NaN NaN -O14: C2 NaN NaN -O14: O22 NaN NaN -O14: O23 NaN NaN -O14: O24 NaN NaN -O14: M22:C2 NaN NaN -O14: M23:C2 NaN NaN -O14: M24:C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m6a -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -O12: C1 NaN NaN -O12: C2 NaN NaN -O13: C1 NaN NaN -O13: C2 NaN NaN -O14: C1 NaN NaN -O14: C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m6b -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -C1:C2 NaN NaN -O12: C1 NaN NaN -O12: C2 NaN NaN -O13: C1 NaN NaN -O13: C2 NaN NaN -O14: C1 NaN NaN -O14: C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m6c -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -O12: C1 NaN NaN -O12: C2 NaN NaN -O12: C1:C2 NaN NaN -O13: C1 NaN NaN -O13: C2 NaN NaN -O13: C1:C2 NaN NaN -O14: C1 NaN NaN -O14: C2 NaN NaN -O14: C1:C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m6d -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -O24 NaN NaN -M22:C2 NaN NaN -M23:C2 NaN NaN -M24:C2 NaN NaN -O12: C1 NaN NaN -O12: C2 NaN NaN -O13: C1 NaN NaN -O13: C2 NaN NaN -O14: C1 NaN NaN -O14: C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - -$m6e -Potential scale reduction factors: - - Point est. Upper C.I. -O12: C1 NaN NaN -O12: M22 NaN NaN -O12: M23 NaN NaN -O12: M24 NaN NaN -O12: C2 NaN NaN -O12: O22 NaN NaN -O12: O23 NaN NaN -O12: O24 NaN NaN -O12: M22:C2 NaN NaN -O12: M23:C2 NaN NaN -O12: M24:C2 NaN NaN -O13: C1 NaN NaN -O13: M22 NaN NaN -O13: M23 NaN NaN -O13: M24 NaN NaN -O13: C2 NaN NaN -O13: O22 NaN NaN -O13: O23 NaN NaN -O13: O24 NaN NaN -O13: M22:C2 NaN NaN -O13: M23:C2 NaN NaN -O13: M24:C2 NaN NaN -O14: C1 NaN NaN -O14: M22 NaN NaN -O14: M23 NaN NaN -O14: M24 NaN NaN -O14: C2 NaN NaN -O14: O22 NaN NaN -O14: O23 NaN NaN -O14: O24 NaN NaN -O14: M22:C2 NaN NaN -O14: M23:C2 NaN NaN -O14: M24:C2 NaN NaN -gamma_O1[1] NaN NaN -gamma_O1[2] NaN NaN -gamma_O1[3] NaN NaN - - diff --git a/tests/testthat/testout/clm_lapply.models0.MC_error..txt b/tests/testthat/testout/clm_lapply.models0.MC_error..txt deleted file mode 100644 index 5be1d72f..00000000 --- a/tests/testthat/testout/clm_lapply.models0.MC_error..txt +++ /dev/null @@ -1,578 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - est MCSE SD MCSE/SD -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m0b - est MCSE SD MCSE/SD -gamma_O2[1] 0 0 0 NaN -gamma_O2[2] 0 0 0 NaN -gamma_O2[3] 0 0 0 NaN - -$m1a - est MCSE SD MCSE/SD -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN -C1 0 0 0 NaN - -$m1b - est MCSE SD MCSE/SD -gamma_O2[1] 0 0 0 NaN -gamma_O2[2] 0 0 0 NaN -gamma_O2[3] 0 0 0 NaN -C1 0 0 0 NaN - -$m2a - est MCSE SD MCSE/SD -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN -C2 0 0 0 NaN - -$m2b - est MCSE SD MCSE/SD -gamma_O2[1] 0 0 0 NaN -gamma_O2[2] 0 0 0 NaN -gamma_O2[3] 0 0 0 NaN -C2 0 0 0 NaN - -$m3a - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -O1.L 0 0 0 NaN -O1.Q 0 0 0 NaN -O1.C 0 0 0 NaN -sigma_C1 0 0 0 NaN - -$m3b - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -sigma_C1 0 0 0 NaN - -$m4a - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -abs(C1 - C2) 0 0 0 NaN -log(C1) 0 0 0 NaN -O22:abs(C1 - C2) 0 0 0 NaN -O23:abs(C1 - C2) 0 0 0 NaN -O24:abs(C1 - C2) 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m4b - est MCSE SD MCSE/SD -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 NaN -abs(C1 - C2) 0 0 0 NaN -log(C1) 0 0 0 NaN -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m5a - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -O12: C1 0 0 0 NaN -O12: C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m5b - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -C1:C2 0 0 0 NaN -O12: C1 0 0 0 NaN -O12: C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m5c - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -O12: C1 0 0 0 NaN -O12: C2 0 0 0 NaN -O12: C1:C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: C2 0 0 0 NaN -O13: C1:C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: C2 0 0 0 NaN -O14: C1:C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m5d - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -M22:C2 0 0 0 NaN -M23:C2 0 0 0 NaN -M24:C2 0 0 0 NaN -O12: C1 0 0 0 NaN -O12: C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m5e - est MCSE SD MCSE/SD -O12: C1 0 0 0 NaN -O12: M22 0 0 0 NaN -O12: M23 0 0 0 NaN -O12: M24 0 0 0 NaN -O12: C2 0 0 0 NaN -O12: O22 0 0 0 NaN -O12: O23 0 0 0 NaN -O12: O24 0 0 0 NaN -O12: M22:C2 0 0 0 NaN -O12: M23:C2 0 0 0 NaN -O12: M24:C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: M22 0 0 0 NaN -O13: M23 0 0 0 NaN -O13: M24 0 0 0 NaN -O13: C2 0 0 0 NaN -O13: O22 0 0 0 NaN -O13: O23 0 0 0 NaN -O13: O24 0 0 0 NaN -O13: M22:C2 0 0 0 NaN -O13: M23:C2 0 0 0 NaN -O13: M24:C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: M22 0 0 0 NaN -O14: M23 0 0 0 NaN -O14: M24 0 0 0 NaN -O14: C2 0 0 0 NaN -O14: O22 0 0 0 NaN -O14: O23 0 0 0 NaN -O14: O24 0 0 0 NaN -O14: M22:C2 0 0 0 NaN -O14: M23:C2 0 0 0 NaN -O14: M24:C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m6a - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -O12: C1 0 0 0 NaN -O12: C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m6b - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -C1:C2 0 0 0 NaN -O12: C1 0 0 0 NaN -O12: C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m6c - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -O12: C1 0 0 0 NaN -O12: C2 0 0 0 NaN -O12: C1:C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: C2 0 0 0 NaN -O13: C1:C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: C2 0 0 0 NaN -O14: C1:C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m6d - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -O24 0 0 0 NaN -M22:C2 0 0 0 NaN -M23:C2 0 0 0 NaN -M24:C2 0 0 0 NaN -O12: C1 0 0 0 NaN -O12: C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - -$m6e - est MCSE SD MCSE/SD -O12: C1 0 0 0 NaN -O12: M22 0 0 0 NaN -O12: M23 0 0 0 NaN -O12: M24 0 0 0 NaN -O12: C2 0 0 0 NaN -O12: O22 0 0 0 NaN -O12: O23 0 0 0 NaN -O12: O24 0 0 0 NaN -O12: M22:C2 0 0 0 NaN -O12: M23:C2 0 0 0 NaN -O12: M24:C2 0 0 0 NaN -O13: C1 0 0 0 NaN -O13: M22 0 0 0 NaN -O13: M23 0 0 0 NaN -O13: M24 0 0 0 NaN -O13: C2 0 0 0 NaN -O13: O22 0 0 0 NaN -O13: O23 0 0 0 NaN -O13: O24 0 0 0 NaN -O13: M22:C2 0 0 0 NaN -O13: M23:C2 0 0 0 NaN -O13: M24:C2 0 0 0 NaN -O14: C1 0 0 0 NaN -O14: M22 0 0 0 NaN -O14: M23 0 0 0 NaN -O14: M24 0 0 0 NaN -O14: C2 0 0 0 NaN -O14: O22 0 0 0 NaN -O14: O23 0 0 0 NaN -O14: O24 0 0 0 NaN -O14: M22:C2 0 0 0 NaN -O14: M23:C2 0 0 0 NaN -O14: M24:C2 0 0 0 NaN -gamma_O1[1] 0 0 0 NaN -gamma_O1[2] 0 0 0 NaN -gamma_O1[3] 0 0 0 NaN - diff --git a/tests/testthat/testout/clm_lapply.models0.coef..txt b/tests/testthat/testout/clm_lapply.models0.coef..txt deleted file mode 100644 index 38ff33ea..00000000 --- a/tests/testthat/testout/clm_lapply.models0.coef..txt +++ /dev/null @@ -1,166 +0,0 @@ -$m0a -$m0a$O1 -O1 > 1 O1 > 2 O1 > 3 - 0 0 0 - - -$m0b -$m0b$O2 -O2 > 1 O2 > 2 O2 > 3 - 0 0 0 - - -$m1a -$m1a$O1 - C1 O1 > 1 O1 > 2 O1 > 3 - 0 0 0 0 - - -$m1b -$m1b$O2 - C1 O2 > 1 O2 > 2 O2 > 3 - 0 0 0 0 - - -$m2a -$m2a$O1 - C2 O1 > 1 O1 > 2 O1 > 3 - 0 0 0 0 - - -$m2b -$m2b$O2 - C2 O2 > 1 O2 > 2 O2 > 3 - 0 0 0 0 - - -$m3a -$m3a$C1 -(Intercept) O1.L O1.Q O1.C sigma_C1 - 0 0 0 0 0 - - -$m3b -$m3b$C1 -(Intercept) O22 O23 O24 sigma_C1 - 0 0 0 0 0 - - -$m4a -$m4a$O1 - M22 M23 M24 O22 - 0 0 0 0 - O23 O24 abs(C1 - C2) log(C1) - 0 0 0 0 -O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) O1 > 1 - 0 0 0 0 - O1 > 2 O1 > 3 - 0 0 - - -$m4b -$m4b$O1 - ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - O1 > 1 - 0 - O1 > 2 - 0 - O1 > 3 - 0 - - -$m5a -$m5a$O1 - M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1 - 0 0 0 0 0 0 0 0 0 0 0 - C2 O1 > 1 O1 > 2 O1 > 3 - 0 0 0 0 - - -$m5b -$m5b$O1 - M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 O1 > 1 O1 > 2 O1 > 3 - 0 0 0 0 0 - - -$m5c -$m5c$O1 - M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1:C2 C1 C2 C1:C2 O1 > 1 O1 > 2 O1 > 3 - 0 0 0 0 0 0 0 - - -$m5d -$m5d$O1 - M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 C1 C2 O1 > 1 O1 > 2 O1 > 3 - 0 0 0 0 0 0 0 - - -$m5e -$m5e$O1 - C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 -O1 > 1 O1 > 2 O1 > 3 - 0 0 0 - - -$m6a -$m6a$O1 - M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1 - 0 0 0 0 0 0 0 0 0 0 0 - C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 - 0 0 0 0 - - -$m6b -$m6b$O1 - M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 - 0 0 0 0 0 - - -$m6c -$m6c$O1 - M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1:C2 C1 C2 C1:C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 - 0 0 0 0 0 0 0 - - -$m6d -$m6d$O1 - M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 - 0 0 0 0 0 0 0 - - -$m6e -$m6e$O1 - C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 - 0 0 0 - - diff --git a/tests/testthat/testout/clm_lapply.models0.confint..txt b/tests/testthat/testout/clm_lapply.models0.confint..txt deleted file mode 100644 index 70700c47..00000000 --- a/tests/testthat/testout/clm_lapply.models0.confint..txt +++ /dev/null @@ -1,359 +0,0 @@ -$m0a -$m0a$O1 - 2.5% 97.5% -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m0b -$m0b$O2 - 2.5% 97.5% -O2 > 1 0 0 -O2 > 2 0 0 -O2 > 3 0 0 - - -$m1a -$m1a$O1 - 2.5% 97.5% -C1 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m1b -$m1b$O2 - 2.5% 97.5% -C1 0 0 -O2 > 1 0 0 -O2 > 2 0 0 -O2 > 3 0 0 - - -$m2a -$m2a$O1 - 2.5% 97.5% -C2 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m2b -$m2b$O2 - 2.5% 97.5% -C2 0 0 -O2 > 1 0 0 -O2 > 2 0 0 -O2 > 3 0 0 - - -$m3a -$m3a$C1 - 2.5% 97.5% -(Intercept) 0 0 -O1.L 0 0 -O1.Q 0 0 -O1.C 0 0 -sigma_C1 0 0 - - -$m3b -$m3b$C1 - 2.5% 97.5% -(Intercept) 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -sigma_C1 0 0 - - -$m4a -$m4a$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -O22:abs(C1 - C2) 0 0 -O23:abs(C1 - C2) 0 0 -O24:abs(C1 - C2) 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m4b -$m4b$O1 - 2.5% 97.5% -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m5a -$m5a$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m5b -$m5b$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -C1:C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m5c -$m5c$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -C1 0 0 -C2 0 0 -C1:C2 0 0 -C1 0 0 -C2 0 0 -C1:C2 0 0 -C1 0 0 -C2 0 0 -C1:C2 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m5d -$m5d$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m5e -$m5e$O1 - 2.5% 97.5% -C1 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -C1 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -C1 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -O1 > 1 0 0 -O1 > 2 0 0 -O1 > 3 0 0 - - -$m6a -$m6a$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -O1 ≤ 1 0 0 -O1 ≤ 2 0 0 -O1 ≤ 3 0 0 - - -$m6b -$m6b$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -C1:C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -O1 ≤ 1 0 0 -O1 ≤ 2 0 0 -O1 ≤ 3 0 0 - - -$m6c -$m6c$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -C1 0 0 -C2 0 0 -C1:C2 0 0 -C1 0 0 -C2 0 0 -C1:C2 0 0 -C1 0 0 -C2 0 0 -C1:C2 0 0 -O1 ≤ 1 0 0 -O1 ≤ 2 0 0 -O1 ≤ 3 0 0 - - -$m6d -$m6d$O1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -O1 ≤ 1 0 0 -O1 ≤ 2 0 0 -O1 ≤ 3 0 0 - - -$m6e -$m6e$O1 - 2.5% 97.5% -C1 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -C1 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -C1 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -O1 ≤ 1 0 0 -O1 ≤ 2 0 0 -O1 ≤ 3 0 0 - - diff --git a/tests/testthat/testout/clm_lapply.models0.function.x.coef.txt b/tests/testthat/testout/clm_lapply.models0.function.x.coef.txt deleted file mode 100644 index d9abf59b..00000000 --- a/tests/testthat/testout/clm_lapply.models0.function.x.coef.txt +++ /dev/null @@ -1,572 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a -$m0a$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - - -$m0b -$m0b$O2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - - -$m1a -$m1a$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN - - -$m1b -$m1b$O2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN - - -$m2a -$m2a$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C2 0 0 0 0 0 NaN NaN - - -$m2b -$m2b$O2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C2 0 0 0 0 0 NaN NaN - - -$m3a -$m3a$C1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -O1.L 0 0 0 0 0 NaN NaN -O1.Q 0 0 0 0 0 NaN NaN -O1.C 0 0 0 0 0 NaN NaN - - -$m3b -$m3b$C1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN - - -$m4a -$m4a$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -abs(C1 - C2) 0 0 0 0 0 NaN NaN -log(C1) 0 0 0 0 0 NaN NaN -O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN - - -$m4b -$m4b$O1 - Mean SD 2.5% 97.5% -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 0 -abs(C1 - C2) 0 0 0 0 -log(C1) 0 0 0 0 -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0 - tail-prob. GR-crit -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 NaN -abs(C1 - C2) 0 NaN -log(C1) 0 NaN -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN - MCE/SD -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN -abs(C1 - C2) NaN -log(C1) NaN -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN - - -$m5a -$m5a$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - - -$m5b -$m5b$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -C1:C2 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - - -$m5c -$m5c$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O12: C1:C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O13: C1:C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN -O14: C1:C2 0 0 0 0 0 NaN NaN - - -$m5d -$m5d$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -M22:C2 0 0 0 0 0 NaN NaN -M23:C2 0 0 0 0 0 NaN NaN -M24:C2 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - - -$m5e -$m5e$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O12: C1 0 0 0 0 0 NaN NaN -O12: M22 0 0 0 0 0 NaN NaN -O12: M23 0 0 0 0 0 NaN NaN -O12: M24 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O12: O22 0 0 0 0 0 NaN NaN -O12: O23 0 0 0 0 0 NaN NaN -O12: O24 0 0 0 0 0 NaN NaN -O12: M22:C2 0 0 0 0 0 NaN NaN -O12: M23:C2 0 0 0 0 0 NaN NaN -O12: M24:C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: M22 0 0 0 0 0 NaN NaN -O13: M23 0 0 0 0 0 NaN NaN -O13: M24 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O13: O22 0 0 0 0 0 NaN NaN -O13: O23 0 0 0 0 0 NaN NaN -O13: O24 0 0 0 0 0 NaN NaN -O13: M22:C2 0 0 0 0 0 NaN NaN -O13: M23:C2 0 0 0 0 0 NaN NaN -O13: M24:C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: M22 0 0 0 0 0 NaN NaN -O14: M23 0 0 0 0 0 NaN NaN -O14: M24 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN -O14: O22 0 0 0 0 0 NaN NaN -O14: O23 0 0 0 0 0 NaN NaN -O14: O24 0 0 0 0 0 NaN NaN -O14: M22:C2 0 0 0 0 0 NaN NaN -O14: M23:C2 0 0 0 0 0 NaN NaN -O14: M24:C2 0 0 0 0 0 NaN NaN - - -$m6a -$m6a$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - - -$m6b -$m6b$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -C1:C2 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - - -$m6c -$m6c$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O12: C1:C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O13: C1:C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN -O14: C1:C2 0 0 0 0 0 NaN NaN - - -$m6d -$m6d$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -M22:C2 0 0 0 0 0 NaN NaN -M23:C2 0 0 0 0 0 NaN NaN -M24:C2 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - - -$m6e -$m6e$O1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O12: C1 0 0 0 0 0 NaN NaN -O12: M22 0 0 0 0 0 NaN NaN -O12: M23 0 0 0 0 0 NaN NaN -O12: M24 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O12: O22 0 0 0 0 0 NaN NaN -O12: O23 0 0 0 0 0 NaN NaN -O12: O24 0 0 0 0 0 NaN NaN -O12: M22:C2 0 0 0 0 0 NaN NaN -O12: M23:C2 0 0 0 0 0 NaN NaN -O12: M24:C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: M22 0 0 0 0 0 NaN NaN -O13: M23 0 0 0 0 0 NaN NaN -O13: M24 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O13: O22 0 0 0 0 0 NaN NaN -O13: O23 0 0 0 0 0 NaN NaN -O13: O24 0 0 0 0 0 NaN NaN -O13: M22:C2 0 0 0 0 0 NaN NaN -O13: M23:C2 0 0 0 0 0 NaN NaN -O13: M24:C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: M22 0 0 0 0 0 NaN NaN -O14: M23 0 0 0 0 0 NaN NaN -O14: M24 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN -O14: O22 0 0 0 0 0 NaN NaN -O14: O23 0 0 0 0 0 NaN NaN -O14: O24 0 0 0 0 0 NaN NaN -O14: M22:C2 0 0 0 0 0 NaN NaN -O14: M23:C2 0 0 0 0 0 NaN NaN -O14: M24:C2 0 0 0 0 0 NaN NaN - - diff --git a/tests/testthat/testout/clm_lapply.models0.print..txt b/tests/testthat/testout/clm_lapply.models0.print..txt deleted file mode 100644 index 026a5aea..00000000 --- a/tests/testthat/testout/clm_lapply.models0.print..txt +++ /dev/null @@ -1,616 +0,0 @@ - -Call: -clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 - 0 0 0 - -Call: -clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O2" - - -Coefficients: -O2 > 1 O2 > 2 O2 > 3 - 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 C1 - 0 0 0 0 - -Call: -clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O2" - - -Coefficients: -O2 > 1 O2 > 2 O2 > 3 C1 - 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 C2 - 0 0 0 0 - -Call: -clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O2" - - -Coefficients: -O2 > 1 O2 > 2 O2 > 3 C2 - 0 0 0 0 - -Call: -lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear model for "C1" - - -Coefficients: -(Intercept) O1.L O1.Q O1.C - 0 0 0 0 - - -Residual standard deviation: -sigma_C1 - 0 - -Call: -lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear model for "C1" - - -Coefficients: -(Intercept) O22 O23 O24 - 0 0 0 0 - - -Residual standard deviation: -sigma_C1 - 0 - -Call: -clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: - O1 > 1 O1 > 2 O1 > 3 M22 - 0 0 0 0 - M23 M24 O22 O23 - 0 0 0 0 - O24 abs(C1 - C2) log(C1) O22:abs(C1 - C2) - 0 0 0 0 -O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 - -Call: -clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: - O1 > 1 - 0 - O1 > 2 - 0 - O1 > 3 - 0 - ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - -Call: -clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 C1 C2 - 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 - 0 0 0 0 0 0 0 0 0 0 0 - C2 C1 C2 C1 C2 - 0 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * - C2), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 - 0 0 0 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 - 0 0 0 0 0 0 0 0 0 0 0 -M24:C2 C1 C2 C1 C2 C1 C2 - 0 0 0 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + - M2 * C2 + O2, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 - 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 C1 C2 - 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 - 0 0 0 0 0 0 0 0 0 0 0 - C2 C1 C2 C1 C2 - 0 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 - 0 0 0 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 - 0 0 0 0 0 0 0 0 0 0 0 -M24:C2 C1 C2 C1 C2 C1 C2 - 0 0 0 0 0 0 0 - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + - M2 * C2 + O2, rev = "O1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 - 0 0 0 -$m0a - -Call: -clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 - 0 0 0 - -$m0b - -Call: -clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O2" - - -Coefficients: -O2 > 1 O2 > 2 O2 > 3 - 0 0 0 - -$m1a - -Call: -clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 C1 - 0 0 0 0 - -$m1b - -Call: -clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O2" - - -Coefficients: -O2 > 1 O2 > 2 O2 > 3 C1 - 0 0 0 0 - -$m2a - -Call: -clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 C2 - 0 0 0 0 - -$m2b - -Call: -clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O2" - - -Coefficients: -O2 > 1 O2 > 2 O2 > 3 C2 - 0 0 0 0 - -$m3a - -Call: -lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear model for "C1" - - -Coefficients: -(Intercept) O1.L O1.Q O1.C - 0 0 0 0 - - -Residual standard deviation: -sigma_C1 - 0 - -$m3b - -Call: -lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear model for "C1" - - -Coefficients: -(Intercept) O22 O23 O24 - 0 0 0 0 - - -Residual standard deviation: -sigma_C1 - 0 - -$m4a - -Call: -clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: - O1 > 1 O1 > 2 O1 > 3 M22 - 0 0 0 0 - M23 M24 O22 O23 - 0 0 0 0 - O24 abs(C1 - C2) log(C1) O22:abs(C1 - C2) - 0 0 0 0 -O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 - -$m4b - -Call: -clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: - O1 > 1 - 0 - O1 > 2 - 0 - O1 > 3 - 0 - ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - -$m5a - -Call: -clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 C1 C2 - 0 0 0 0 - -$m5b - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 - 0 0 0 0 0 0 0 0 0 0 0 - C2 C1 C2 C1 C2 - 0 0 0 0 0 - -$m5c - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * - C2), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 - 0 0 0 0 0 0 0 - -$m5d - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 - 0 0 0 0 0 0 0 0 0 0 0 -M24:C2 C1 C2 C1 C2 C1 C2 - 0 0 0 0 0 0 0 - -$m5e - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + - M2 * C2 + O2, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 > 1 O1 > 2 O1 > 3 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 - 0 0 0 - -$m6a - -Call: -clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1 C2 C1 C2 - 0 0 0 0 - -$m6b - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1 - 0 0 0 0 0 0 0 0 0 0 0 - C2 C1 C2 C1 C2 - 0 0 0 0 0 - -$m6c - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C1:C2 C1 C2 C1:C2 C1 C2 C1:C2 - 0 0 0 0 0 0 0 - -$m6d - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 - 0 0 0 0 0 0 0 0 0 0 0 -M24:C2 C1 C2 C1 C2 C1 C2 - 0 0 0 0 0 0 0 - -$m6e - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + - M2 * C2 + O2, rev = "O1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit model for "O1" - - -Coefficients: -O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24 - 0 0 0 0 0 0 0 0 0 0 0 -M22:C2 M23:C2 M24:C2 - 0 0 0 - diff --git a/tests/testthat/testout/clm_lapply.models0.summary..txt b/tests/testthat/testout/clm_lapply.models0.summary..txt deleted file mode 100644 index ffc96593..00000000 --- a/tests/testthat/testout/clm_lapply.models0.summary..txt +++ /dev/null @@ -1,1020 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m0b - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O2 > 1 0 0 0 0 0 NaN NaN -O2 > 2 0 0 0 0 0 NaN NaN -O2 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m1a - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m1b - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O2 > 1 0 0 0 0 0 NaN NaN -O2 > 2 0 0 0 0 0 NaN NaN -O2 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m2a - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m2b - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O2 > 1 0 0 0 0 0 NaN NaN -O2 > 2 0 0 0 0 0 NaN NaN -O2 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m3a - -Bayesian linear model fitted with JointAI - -Call: -lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -O1.L 0 0 0 0 0 NaN NaN -O1.Q 0 0 0 0 0 NaN NaN -O1.C 0 0 0 0 0 NaN NaN - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_C1 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 1:10 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m3b - -Bayesian linear model fitted with JointAI - -Call: -lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_C1 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m4a - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -abs(C1 - C2) 0 0 0 0 0 NaN NaN -log(C1) 0 0 0 0 0 NaN NaN -O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m4b - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 0 -abs(C1 - C2) 0 0 0 0 -log(C1) 0 0 0 0 -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0 - tail-prob. GR-crit -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 NaN -abs(C1 - C2) 0 NaN -log(C1) 0 NaN -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN - MCE/SD -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN -abs(C1 - C2) NaN -log(C1) NaN -ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m5a - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m5b - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -C1:C2 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m5c - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * - C2), seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O12: C1:C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O13: C1:C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN -O14: C1:C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m5d - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -M22:C2 0 0 0 0 0 NaN NaN -M23:C2 0 0 0 0 0 NaN NaN -M24:C2 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m5e - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + - M2 * C2 + O2, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O12: C1 0 0 0 0 0 NaN NaN -O12: M22 0 0 0 0 0 NaN NaN -O12: M23 0 0 0 0 0 NaN NaN -O12: M24 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O12: O22 0 0 0 0 0 NaN NaN -O12: O23 0 0 0 0 0 NaN NaN -O12: O24 0 0 0 0 0 NaN NaN -O12: M22:C2 0 0 0 0 0 NaN NaN -O12: M23:C2 0 0 0 0 0 NaN NaN -O12: M24:C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: M22 0 0 0 0 0 NaN NaN -O13: M23 0 0 0 0 0 NaN NaN -O13: M24 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O13: O22 0 0 0 0 0 NaN NaN -O13: O23 0 0 0 0 0 NaN NaN -O13: O24 0 0 0 0 0 NaN NaN -O13: M22:C2 0 0 0 0 0 NaN NaN -O13: M23:C2 0 0 0 0 0 NaN NaN -O13: M24:C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: M22 0 0 0 0 0 NaN NaN -O14: M23 0 0 0 0 0 NaN NaN -O14: M24 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN -O14: O22 0 0 0 0 0 NaN NaN -O14: O23 0 0 0 0 0 NaN NaN -O14: O24 0 0 0 0 0 NaN NaN -O14: M22:C2 0 0 0 0 0 NaN NaN -O14: M23:C2 0 0 0 0 0 NaN NaN -O14: M24:C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 > 1 0 0 0 0 0 NaN NaN -O1 > 2 0 0 0 0 0 NaN NaN -O1 > 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m6a - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 ≤ 1 0 0 0 0 0 NaN NaN -O1 ≤ 2 0 0 0 0 0 NaN NaN -O1 ≤ 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m6b - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -C1:C2 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 ≤ 1 0 0 0 0 0 NaN NaN -O1 ≤ 2 0 0 0 0 0 NaN NaN -O1 ≤ 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m6c - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O12: C1:C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O13: C1:C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN -O14: C1:C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 ≤ 1 0 0 0 0 0 NaN NaN -O1 ≤ 2 0 0 0 0 0 NaN NaN -O1 ≤ 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m6d - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + - C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -O24 0 0 0 0 0 NaN NaN -M22:C2 0 0 0 0 0 NaN NaN -M23:C2 0 0 0 0 0 NaN NaN -M24:C2 0 0 0 0 0 NaN NaN -O12: C1 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O1 ≤ 1 0 0 0 0 0 NaN NaN -O1 ≤ 2 0 0 0 0 0 NaN NaN -O1 ≤ 3 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m6e - -Bayesian cumulative logit model fitted with JointAI - -Call: -clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + - M2 * C2 + O2, rev = "O1", seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O12: C1 0 0 0 0 0 NaN NaN -O12: M22 0 0 0 0 0 NaN NaN -O12: M23 0 0 0 0 0 NaN NaN -O12: M24 0 0 0 0 0 NaN NaN -O12: C2 0 0 0 0 0 NaN NaN -O12: O22 0 0 0 0 0 NaN NaN -O12: O23 0 0 0 0 0 NaN NaN -O12: O24 0 0 0 0 0 NaN NaN -O12: M22:C2 0 0 0 0 0 NaN NaN -O12: M23:C2 0 0 0 0 0 NaN NaN -O12: M24:C2 0 0 0 0 0 NaN NaN -O13: C1 0 0 0 0 0 NaN NaN -O13: M22 0 0 0 0 0 NaN NaN -O13: M23 0 0 0 0 0 NaN NaN -O13: M24 0 0 0 0 0 NaN NaN -O13: C2 0 0 0 0 0 NaN NaN -O13: O22 0 0 0 0 0 NaN NaN -O13: O23 0 0 0 0 0 NaN NaN -O13: O24 0 0 0 0 0 NaN NaN -O13: M22:C2 0 0 0 0 0 NaN NaN -O13: M23:C2 0 0 0 0 0 NaN NaN -O13: M24:C2 0 0 0 0 0 NaN NaN -O14: C1 0 0 0 0 0 NaN NaN -O14: M22 0 0 0 0 0 NaN NaN -O14: M23 0 0 0 0 0 NaN NaN -O14: M24 0 0 0 0 0 NaN NaN -O14: C2 0 0 0 0 0 NaN NaN -O14: O22 0 0 0 0 0 NaN NaN -O14: O23 0 0 0 0 0 NaN NaN -O14: O24 0 0 0 0 0 NaN NaN -O14: M22:C2 0 0 0 0 0 NaN NaN -O14: M23:C2 0 0 0 0 0 NaN NaN -O14: M24:C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. 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a/tests/testthat/testout/clmm_lapply.models.jagsmodel..txt +++ /dev/null @@ -1,2679 +0,0 @@ -$m0a -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - } -$m0b -model { - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - } -$m1a -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] - } - - - - # Priors for the model for o1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - } -$m1b -model { - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] - } - - - - # Priors for the model for o2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - } -$m1c -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - } -$m1d -model { - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] + - beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - } -$m2a -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - - - # Priors for the model for o1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } -$m2b -model { - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - - - # Priors for the model for o2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } -$m2c -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } -$m2d -model { - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] + - beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } -$m3a -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 2] + - beta[3] * M_lvlone[i, 3] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - } -$m3b -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 3] + - beta[3] * M_lvlone[i, 4] + beta[4] * M_lvlone[i, 5] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - } -$m4a -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + beta[6] * M_lvlone[i, 3] + - beta[7] * M_lvlone[i, 4] + beta[8] * M_lvlone[i, 5] + - beta[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[10] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + - beta[11] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 4] * beta[1] + M_id[ii, 5] * beta[2] + - M_id[ii, 6] * beta[3] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * beta[4] + - (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] - } - - - - # Priors for the model for o2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 3] * alpha[6] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[7] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[8] - log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[9] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[10] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[11] - log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[12] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[13] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 6:14) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 3] * alpha[15] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[16] - - M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2]) - - - } - - # Priors for the model for C2 - for (k in 15:16) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 6] <- M_lvlone[i, 3] * M_id[group_id[i], 7] - M_lvlone[i, 7] <- M_lvlone[i, 4] * M_id[group_id[i], 7] - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_id[group_id[i], 7] - } - - } -$m4b -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[2] - } - - - - # Priors for the model for o1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - - M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0) - - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- M_id[ii, 5] * alpha[1] + M_id[ii, 6] * alpha[2] + - M_id[ii, 7] * alpha[3] + - (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[4] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[5] - } - - - - # Priors for the model for o2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[6] + M_id[ii, 5] * alpha[7] + M_id[ii, 6] * alpha[8] + - M_id[ii, 7] * alpha[9] + - (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[10] - - M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1]) - - - } - - # Priors for the model for C2 - for (k in 6:10) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3] - } - - } -$m4c -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_o1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_o1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:4] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] + - M_id[ii, 4] * beta[2] - mu_b_o1_id[ii, 2] <- beta[4] - mu_b_o1_id[ii, 3] <- beta[3] - mu_b_o1_id[ii, 4] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - for (k in 1:4) { - RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_o1_id[1:4, 1:4] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id) - D_o1_id[1:4, 1:4] <- inverse(invD_o1_id[ , ]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for time - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - M_id[ii, 4] * alpha[7] - } - - # Priors for the model for c1 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - } -$m4d -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[4] * M_lvlone[i, 5] + - beta[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[7] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:2] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] - mu_b_o1_id[ii, 2] <- beta[2] + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[6] - } - - - - # Priors for the model for o1 - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - for (k in 1:2) { - RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_o1_id[1:2, 1:2] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id) - D_o1_id[1:2, 1:2] <- inverse(invD_o1_id[ , ]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] - } - - # Priors for the model for b2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] - } - - } -$m4e -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1] - } - - - - # Priors for the model for o1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal_ridge_beta[k]) - tau_reg_ordinal_ridge_beta[k] ~ dgamma(0.01, 0.01) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - } -$m5a -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[7] * M_lvlone[i, 3] - eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[8] * M_lvlone[i, 3] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] - } - - - - # Priors for the model for o1 - for (k in 1:8) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] - - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + - M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5] - } - - # Priors for the model for b2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 3] * alpha[6] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] + - M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9] - } - - # Priors for the model for C2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10] - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0) - } - - # Priors for the model for O2 - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - } -$m5b -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + - M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + - M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + - M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] - } - - } -$m5c -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + - M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + - M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + - M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] - } - - } -$m5d -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] + - (M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] + - (M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] + - (M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8] - } - - - - # Priors for the model for o1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + - M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (ii in 1:100) { - M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] - M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] - M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] - } - - } -$m5e -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] + - beta[3] * M_id[group_id[i], 7] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] + - beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + - beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + - beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + - beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] + - beta[12] * M_id[group_id[i], 7] + - beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] + - beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + - beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + - beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + - beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3]))) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2])) - p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2])) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:20) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + - M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (ii in 1:100) { - M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] - M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] - M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] - } - - } -$m6a -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[7] * M_lvlone[i, 3] - eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[8] * M_lvlone[i, 3] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] - } - - - - # Priors for the model for o1 - for (k in 1:8) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] - - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + - M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5] - } - - # Priors for the model for b2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 3] * alpha[6] + - (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] + - M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9] - } - - # Priors for the model for C2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10] - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0) - } - - # Priors for the model for O2 - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - } -$m6b -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] + - beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + - M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + - M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + - M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] - } - - } -$m6c -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] - } - - - - # Priors for the model for o1 - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] + - M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] + - M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] + - M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2] - } - - } -$m6d -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] + - M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] + - M_id[ii, 9] * beta[5] + - (M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] + - (M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] + - (M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8] - } - - - - # Priors for the model for o1 - for (k in 1:12) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + - M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (ii in 1:100) { - M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] - M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] - M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] - } - - } -$m6e -model { - - # Cumulative logit mixed effects model for o1 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3]) - eta_o1[i] <- b_o1_id[group_id[i], 1] - - eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] + - beta[3] * M_id[group_id[i], 7] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] + - beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + - beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + - beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + - beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] + - beta[12] * M_id[group_id[i], 7] + - beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] + - beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] + - beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] + - beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] + - beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - - p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1])) - p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1])) - p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2]))) - - logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i] - logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i] - - } - - for (ii in 1:100) { - b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ]) - mu_b_o1_id[ii, 1] <- 0 - } - - - - # Priors for the model for o1 - for (k in 1:20) { - beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1]) - - invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] + - M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] + - M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7] - } - - # Priors for the model for c1 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 4] * alpha[8] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] + - M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11] - log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] + - M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15] - log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] + - M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19] - - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 8:19) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 4] * alpha[20] + M_id[ii, 8] * alpha[21] + - M_id[ii, 9] * alpha[22] - } - - # Priors for the model for C2 - for (k in 20:22) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 3] ~ dcat(p_O2[ii, 1:3]) - eta_O2[ii] <- 0 - - p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3]))) - p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2])) - p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2])) - - logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii] - logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii] - - M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0) - M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0) - } - - # Priors for the model for O2 - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - - # Re-calculate interaction terms - for (ii in 1:100) { - M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2] - M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2] - M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2] - } - - } -$m7a -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + beta[3] * M_lvlone[i, 4] + - beta[4] * M_lvlone[i, 5] + beta[5] * M_lvlone[i, 6] + - beta[6] * M_lvlone[i, 7] + beta[7] * M_lvlone[i, 8] + - beta[8] * M_lvlone[i, 9] + beta[9] * M_lvlone[i, 10] + - beta[10] * M_lvlone[i, 11] + - beta[11] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for y - for (k in 1:11) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 9] + - alpha[3] * M_lvlone[i, 10] + alpha[4] * M_lvlone[i, 11] + - alpha[5] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[1] - } - - - - # Priors for the model for o2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Cumulative logit mixed effects model for x ------------------------------------ - for (i in 1:329) { - M_lvlone[i, 3] ~ dcat(p_x[i, 1:4]) - eta_x[i] <- b_x_id[group_id[i], 1] + - alpha[7] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] - - p_x[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_x[i, 2:4]))) - p_x[i, 2] <- max(1e-10, min(1-1e-10, psum_x[i, 1] - psum_x[i, 2])) - p_x[i, 3] <- max(1e-10, min(1-1e-10, psum_x[i, 2] - psum_x[i, 3])) - p_x[i, 4] <- max(1e-10, min(1-1e-10, psum_x[i, 3])) - - logit(psum_x[i, 1]) <- gamma_x[1] + eta_x[i] - logit(psum_x[i, 2]) <- gamma_x[2] + eta_x[i] - logit(psum_x[i, 3]) <- gamma_x[3] + eta_x[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_x_id[ii, 1:1] ~ dnorm(mu_b_x_id[ii, ], invD_x_id[ , ]) - mu_b_x_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[6] - } - - - - # Priors for the model for x - for (k in 6:7) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_x[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_x[2] <- gamma_x[1] - exp(delta_x[1]) - gamma_x[3] <- gamma_x[2] - exp(delta_x[2]) - - invD_x_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_x_id[1, 1] <- 1 / (invD_x_id[1, 1]) - } -$m7b -model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + beta[2] * M_lvlone[i, 5] + - beta[3] * M_lvlone[i, 6] + beta[4] * M_lvlone[i, 7] + - beta[5] * M_lvlone[i, 8] + beta[6] * M_lvlone[i, 9] + - beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[8] * M_lvlone[i, 10] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:8) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 5] + - alpha[3] * M_lvlone[i, 6] + alpha[4] * M_lvlone[i, 7] + - alpha[5] * M_lvlone[i, 8] + alpha[6] * M_lvlone[i, 9] + - alpha[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - - - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for b2 - for (k in 1:7) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] + - alpha[10] * M_lvlone[i, 6] + alpha[11] * M_lvlone[i, 7] + - alpha[12] * M_lvlone[i, 8] + alpha[13] * M_lvlone[i, 9] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] - } - - # Priors for the model for c2 - for (k in 8:13) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- 0 - } - - - - # Priors for the model for o2 - delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - } diff --git a/tests/testthat/testout/clmm_lapply.models0.GR_crit.multiva.txt b/tests/testthat/testout/clmm_lapply.models0.GR_crit.multiva.txt deleted file mode 100644 index 6cabfa61..00000000 --- a/tests/testthat/testout/clmm_lapply.models0.GR_crit.multiva.txt +++ /dev/null @@ -1,467 +0,0 @@ -$m0a -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m0b -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o2[1] NaN NaN -gamma_o2[2] NaN NaN -gamma_o2[3] NaN NaN -D_o2_id[1,1] NaN NaN - - -$m1a -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -C1 NaN NaN -D_o1_id[1,1] NaN NaN - - -$m1b -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o2[1] NaN NaN -gamma_o2[2] NaN NaN -gamma_o2[3] NaN NaN -C1 NaN NaN -D_o2_id[1,1] NaN NaN - - -$m1c -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -c1 NaN NaN -D_o1_id[1,1] NaN NaN - - -$m1d -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o2[1] NaN NaN -gamma_o2[2] NaN NaN -gamma_o2[3] NaN NaN -c1 NaN NaN -D_o2_id[1,1] NaN NaN - - -$m2a -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -C2 NaN NaN -D_o1_id[1,1] NaN NaN - - -$m2b -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o2[1] NaN NaN -gamma_o2[2] NaN NaN -gamma_o2[3] NaN NaN -C2 NaN NaN -D_o2_id[1,1] NaN NaN - - -$m2c -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -c2 NaN NaN -D_o1_id[1,1] NaN NaN - - -$m2d -Potential scale reduction factors: - - Point est. Upper C.I. -gamma_o2[1] NaN NaN -gamma_o2[2] NaN NaN -gamma_o2[3] NaN NaN -c2 NaN NaN -D_o2_id[1,1] NaN NaN - - -$m3a -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -o1.L NaN NaN -o1.Q NaN NaN -sigma_c1 NaN NaN -D_c1_id[1,1] NaN NaN - - -$m3b -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -o22 NaN NaN -o23 NaN NaN -o24 NaN NaN -sigma_c1 NaN NaN -D_c1_id[1,1] NaN NaN - - -$m4a -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -abs(C1 - C2) NaN NaN -log(C1) NaN NaN -o22 NaN NaN -o23 NaN NaN -o24 NaN NaN -o22:abs(C1 - C2) NaN NaN -o23:abs(C1 - C2) NaN NaN -o24:abs(C1 - C2) NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m4b -Potential scale reduction factors: - - Point est. -abs(C1 - C2) NaN -log(C1) NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -gamma_o1[1] NaN -gamma_o1[2] NaN -D_o1_id[1,1] NaN - Upper C.I. -abs(C1 - C2) NaN -log(C1) NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -gamma_o1[1] NaN -gamma_o1[2] NaN -D_o1_id[1,1] NaN - - -$m4c -Potential scale reduction factors: - - Point est. Upper C.I. -C1 NaN NaN -B21 NaN NaN -time NaN NaN -c1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN -D_o1_id[1,2] NaN NaN -D_o1_id[2,2] NaN NaN -D_o1_id[1,3] NaN NaN -D_o1_id[2,3] NaN NaN -D_o1_id[3,3] NaN NaN -D_o1_id[1,4] NaN NaN -D_o1_id[2,4] NaN NaN -D_o1_id[3,4] NaN NaN -D_o1_id[4,4] NaN NaN - - -$m4d -Potential scale reduction factors: - - Point est. Upper C.I. -C1 NaN NaN -time NaN NaN -I(time^2) NaN NaN -b21 NaN NaN -c1 NaN NaN -C1:time NaN NaN -b21:c1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN -D_o1_id[1,2] NaN NaN -D_o1_id[2,2] NaN NaN - - -$m4e -Potential scale reduction factors: - - Point est. Upper C.I. -C1 NaN NaN -log(time) NaN NaN -I(time^2) NaN NaN -p1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m5a -Potential scale reduction factors: - - Point est. Upper C.I. -O22 NaN NaN -O23 NaN NaN -o12: C1 NaN NaN -o12: C2 NaN NaN -o13: C1 NaN NaN -o13: C2 NaN NaN -o12: b21 NaN NaN -o13: b21 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m5b -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -o13: C2 NaN NaN -c1:C2 NaN NaN -o12: C2 NaN NaN -o12: c1 NaN NaN -o13: c1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m5c -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -o12: C2 NaN NaN -o13: C2 NaN NaN -o12: c1 NaN NaN -o12: c1:C2 NaN NaN -o13: c1 NaN NaN -o13: c1:C2 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m5d -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -M22:C2 NaN NaN -M23:C2 NaN NaN -M24:C2 NaN NaN -o12: C2 NaN NaN -o13: C2 NaN NaN -o12: c1 NaN NaN -o13: c1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m5e -Potential scale reduction factors: - - Point est. Upper C.I. -o12: M22 NaN NaN -o12: M23 NaN NaN -o12: M24 NaN NaN -o12: C2 NaN NaN -o12: O22 NaN NaN -o12: O23 NaN NaN -o12: M22:C2 NaN NaN -o12: M23:C2 NaN NaN -o12: M24:C2 NaN NaN -o13: M22 NaN NaN -o13: M23 NaN NaN -o13: M24 NaN NaN -o13: C2 NaN NaN -o13: O22 NaN NaN -o13: O23 NaN NaN -o13: M22:C2 NaN NaN -o13: M23:C2 NaN NaN -o13: M24:C2 NaN NaN -o12: c1 NaN NaN -o13: c1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m6a -Potential scale reduction factors: - - Point est. Upper C.I. -O22 NaN NaN -O23 NaN NaN -o12: C1 NaN NaN -o12: C2 NaN NaN -o13: C1 NaN NaN -o13: C2 NaN NaN -o12: b21 NaN NaN -o13: b21 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m6b -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -o13: C2 NaN NaN -c1:C2 NaN NaN -o12: C2 NaN NaN -o12: c1 NaN NaN -o13: c1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m6c -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -o12: C2 NaN NaN -o13: C2 NaN NaN -o12: c1 NaN NaN -o12: c1:C2 NaN NaN -o13: c1 NaN NaN -o13: c1:C2 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m6d -Potential scale reduction factors: - - Point est. Upper C.I. -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -O22 NaN NaN -O23 NaN NaN -M22:C2 NaN NaN -M23:C2 NaN NaN -M24:C2 NaN NaN -o12: C2 NaN NaN -o13: C2 NaN NaN -o12: c1 NaN NaN -o13: c1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m6e -Potential scale reduction factors: - - Point est. Upper C.I. -o12: M22 NaN NaN -o12: M23 NaN NaN -o12: M24 NaN NaN -o12: C2 NaN NaN -o12: O22 NaN NaN -o12: O23 NaN NaN -o12: M22:C2 NaN NaN -o12: M23:C2 NaN NaN -o12: M24:C2 NaN NaN -o13: M22 NaN NaN -o13: M23 NaN NaN -o13: M24 NaN NaN -o13: C2 NaN NaN -o13: O22 NaN NaN -o13: O23 NaN NaN -o13: M22:C2 NaN NaN -o13: M23:C2 NaN NaN -o13: M24:C2 NaN NaN -o12: c1 NaN NaN -o13: c1 NaN NaN -gamma_o1[1] NaN NaN -gamma_o1[2] NaN NaN -D_o1_id[1,1] NaN NaN - - -$m7a -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -C1 NaN NaN -o1.L NaN NaN -o1.Q NaN NaN -o22 NaN NaN -o23 NaN NaN -o24 NaN NaN -x2 NaN NaN -x3 NaN NaN -x4 NaN NaN -time NaN NaN -sigma_y NaN NaN -D_y_id[1,1] NaN NaN - - -$m7b -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -o22 NaN NaN -o23 NaN NaN -o24 NaN NaN -o1.L NaN NaN -o1.Q NaN NaN -c2 NaN NaN -b21 NaN NaN -sigma_y NaN NaN -D_y_id[1,1] NaN NaN - - diff --git a/tests/testthat/testout/clmm_lapply.models0.MC_error..txt b/tests/testthat/testout/clmm_lapply.models0.MC_error..txt deleted file mode 100644 index 41f6a832..00000000 --- a/tests/testthat/testout/clmm_lapply.models0.MC_error..txt +++ /dev/null @@ -1,657 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - est MCSE SD MCSE/SD -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m0b - est MCSE SD MCSE/SD -gamma_o2[1] 0 0 0 NaN -gamma_o2[2] 0 0 0 NaN -gamma_o2[3] 0 0 0 NaN -D_o2_id[1,1] 0 0 0 NaN - -$m1a - est MCSE SD MCSE/SD -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -C1 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m1b - est MCSE SD MCSE/SD -gamma_o2[1] 0 0 0 NaN -gamma_o2[2] 0 0 0 NaN -gamma_o2[3] 0 0 0 NaN -C1 0 0 0 NaN -D_o2_id[1,1] 0 0 0 NaN - -$m1c - est MCSE SD MCSE/SD -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -c1 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m1d - est MCSE SD MCSE/SD -gamma_o2[1] 0 0 0 NaN -gamma_o2[2] 0 0 0 NaN -gamma_o2[3] 0 0 0 NaN -c1 0 0 0 NaN -D_o2_id[1,1] 0 0 0 NaN - -$m2a - est MCSE SD MCSE/SD -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -C2 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m2b - est MCSE SD MCSE/SD -gamma_o2[1] 0 0 0 NaN -gamma_o2[2] 0 0 0 NaN -gamma_o2[3] 0 0 0 NaN -C2 0 0 0 NaN -D_o2_id[1,1] 0 0 0 NaN - -$m2c - est MCSE SD MCSE/SD -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -c2 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m2d - est MCSE SD MCSE/SD -gamma_o2[1] 0 0 0 NaN -gamma_o2[2] 0 0 0 NaN -gamma_o2[3] 0 0 0 NaN -c2 0 0 0 NaN -D_o2_id[1,1] 0 0 0 NaN - -$m3a - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -o1.L 0 0 0 NaN -o1.Q 0 0 0 NaN -sigma_c1 0 0 0 NaN -D_c1_id[1,1] 0 0 0 NaN - -$m3b - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -o22 0 0 0 NaN -o23 0 0 0 NaN -o24 0 0 0 NaN -sigma_c1 0 0 0 NaN -D_c1_id[1,1] 0 0 0 NaN - -$m4a - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -abs(C1 - C2) 0 0 0 NaN -log(C1) 0 0 0 NaN -o22 0 0 0 NaN -o23 0 0 0 NaN -o24 0 0 0 NaN -o22:abs(C1 - C2) 0 0 0 NaN -o23:abs(C1 - C2) 0 0 0 NaN -o24:abs(C1 - C2) 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m4b - est MCSE SD MCSE/SD -abs(C1 - C2) 0 0 0 NaN -log(C1) 0 0 0 NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m4c - est MCSE SD MCSE/SD -C1 0 0 0 NaN -B21 0 0 0 NaN -time 0 0 0 NaN -c1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN -D_o1_id[1,2] 0 0 0 NaN -D_o1_id[2,2] 0 0 0 NaN -D_o1_id[1,3] 0 0 0 NaN -D_o1_id[2,3] 0 0 0 NaN -D_o1_id[3,3] 0 0 0 NaN -D_o1_id[1,4] 0 0 0 NaN -D_o1_id[2,4] 0 0 0 NaN -D_o1_id[3,4] 0 0 0 NaN -D_o1_id[4,4] 0 0 0 NaN - -$m4d - est MCSE SD MCSE/SD -C1 0 0 0 NaN -time 0 0 0 NaN -I(time^2) 0 0 0 NaN -b21 0 0 0 NaN -c1 0 0 0 NaN -C1:time 0 0 0 NaN -b21:c1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN -D_o1_id[1,2] 0 0 0 NaN -D_o1_id[2,2] 0 0 0 NaN - -$m4e - est MCSE SD MCSE/SD -C1 0 0 0 NaN -log(time) 0 0 0 NaN -I(time^2) 0 0 0 NaN -p1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m5a - est MCSE SD MCSE/SD -O22 0 0 0 NaN -O23 0 0 0 NaN -o12: C1 0 0 0 NaN -o12: C2 0 0 0 NaN -o13: C1 0 0 0 NaN -o13: C2 0 0 0 NaN -o12: b21 0 0 0 NaN -o13: b21 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m5b - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -o13: C2 0 0 0 NaN -c1:C2 0 0 0 NaN -o12: C2 0 0 0 NaN -o12: c1 0 0 0 NaN -o13: c1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m5c - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -o12: C2 0 0 0 NaN -o13: C2 0 0 0 NaN -o12: c1 0 0 0 NaN -o12: c1:C2 0 0 0 NaN -o13: c1 0 0 0 NaN -o13: c1:C2 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m5d - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -M22:C2 0 0 0 NaN -M23:C2 0 0 0 NaN -M24:C2 0 0 0 NaN -o12: C2 0 0 0 NaN -o13: C2 0 0 0 NaN -o12: c1 0 0 0 NaN -o13: c1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m5e - est MCSE SD MCSE/SD -o12: M22 0 0 0 NaN -o12: M23 0 0 0 NaN -o12: M24 0 0 0 NaN -o12: C2 0 0 0 NaN -o12: O22 0 0 0 NaN -o12: O23 0 0 0 NaN -o12: M22:C2 0 0 0 NaN -o12: M23:C2 0 0 0 NaN -o12: M24:C2 0 0 0 NaN -o13: M22 0 0 0 NaN -o13: M23 0 0 0 NaN -o13: M24 0 0 0 NaN -o13: C2 0 0 0 NaN -o13: O22 0 0 0 NaN -o13: O23 0 0 0 NaN -o13: M22:C2 0 0 0 NaN -o13: M23:C2 0 0 0 NaN -o13: M24:C2 0 0 0 NaN -o12: c1 0 0 0 NaN -o13: c1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m6a - est MCSE SD MCSE/SD -O22 0 0 0 NaN -O23 0 0 0 NaN -o12: C1 0 0 0 NaN -o12: C2 0 0 0 NaN -o13: C1 0 0 0 NaN -o13: C2 0 0 0 NaN -o12: b21 0 0 0 NaN -o13: b21 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m6b - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -o13: C2 0 0 0 NaN -c1:C2 0 0 0 NaN -o12: C2 0 0 0 NaN -o12: c1 0 0 0 NaN -o13: c1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m6c - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -o12: C2 0 0 0 NaN -o13: C2 0 0 0 NaN -o12: c1 0 0 0 NaN -o12: c1:C2 0 0 0 NaN -o13: c1 0 0 0 NaN -o13: c1:C2 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m6d - est MCSE SD MCSE/SD -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -O22 0 0 0 NaN -O23 0 0 0 NaN -M22:C2 0 0 0 NaN -M23:C2 0 0 0 NaN -M24:C2 0 0 0 NaN -o12: C2 0 0 0 NaN -o13: C2 0 0 0 NaN -o12: c1 0 0 0 NaN -o13: c1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m6e - est MCSE SD MCSE/SD -o12: M22 0 0 0 NaN -o12: M23 0 0 0 NaN -o12: M24 0 0 0 NaN -o12: C2 0 0 0 NaN -o12: O22 0 0 0 NaN -o12: O23 0 0 0 NaN -o12: M22:C2 0 0 0 NaN -o12: M23:C2 0 0 0 NaN -o12: M24:C2 0 0 0 NaN -o13: M22 0 0 0 NaN -o13: M23 0 0 0 NaN -o13: M24 0 0 0 NaN -o13: C2 0 0 0 NaN -o13: O22 0 0 0 NaN -o13: O23 0 0 0 NaN -o13: M22:C2 0 0 0 NaN -o13: M23:C2 0 0 0 NaN -o13: M24:C2 0 0 0 NaN -o12: c1 0 0 0 NaN -o13: c1 0 0 0 NaN -gamma_o1[1] 0 0 0 NaN -gamma_o1[2] 0 0 0 NaN -D_o1_id[1,1] 0 0 0 NaN - -$m7a - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -C1 0 0 0 NaN -o1.L 0 0 0 NaN -o1.Q 0 0 0 NaN -o22 0 0 0 NaN -o23 0 0 0 NaN -o24 0 0 0 NaN -x2 0 0 0 NaN -x3 0 0 0 NaN -x4 0 0 0 NaN -time 0 0 0 NaN -sigma_y 0 0 0 NaN -D_y_id[1,1] 0 0 0 NaN - -$m7b - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -o22 0 0 0 NaN -o23 0 0 0 NaN -o24 0 0 0 NaN -o1.L 0 0 0 NaN -o1.Q 0 0 0 NaN -c2 0 0 0 NaN -b21 0 0 0 NaN -sigma_y 0 0 0 NaN -D_y_id[1,1] 0 0 0 NaN - diff --git a/tests/testthat/testout/clmm_lapply.models0.coef..txt b/tests/testthat/testout/clmm_lapply.models0.coef..txt deleted file mode 100644 index a66ca9c6..00000000 --- a/tests/testthat/testout/clmm_lapply.models0.coef..txt +++ /dev/null @@ -1,246 +0,0 @@ -$m0a -$m0a$o1 -D_o1_id[1,1] o1 > 1 o1 > 2 - 0 0 0 - - -$m0b -$m0b$o2 -D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 - 0 0 0 0 - - -$m1a -$m1a$o1 - C1 D_o1_id[1,1] o1 > 1 o1 > 2 - 0 0 0 0 - - -$m1b -$m1b$o2 - C1 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 - 0 0 0 0 0 - - -$m1c -$m1c$o1 - c1 D_o1_id[1,1] o1 > 1 o1 > 2 - 0 0 0 0 - - -$m1d -$m1d$o2 - c1 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 - 0 0 0 0 0 - - -$m2a -$m2a$o1 - C2 D_o1_id[1,1] o1 > 1 o1 > 2 - 0 0 0 0 - - -$m2b -$m2b$o2 - C2 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 - 0 0 0 0 0 - - -$m2c -$m2c$o1 - c2 D_o1_id[1,1] o1 > 1 o1 > 2 - 0 0 0 0 - - -$m2d -$m2d$o2 - c2 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3 - 0 0 0 0 0 - - -$m3a -$m3a$c1 - (Intercept) o1.L o1.Q sigma_c1 D_c1_id[1,1] - 0 0 0 0 0 - - -$m3b -$m3b$c1 - (Intercept) o22 o23 o24 sigma_c1 D_c1_id[1,1] - 0 0 0 0 0 0 - - -$m4a -$m4a$o1 - M22 M23 M24 abs(C1 - C2) - 0 0 0 0 - log(C1) o22 o23 o24 - 0 0 0 0 -o22:abs(C1 - C2) o23:abs(C1 - C2) o24:abs(C1 - C2) D_o1_id[1,1] - 0 0 0 0 - o1 > 1 o1 > 2 - 0 0 - - -$m4b -$m4b$o1 - abs(C1 - C2) - 0 - log(C1) - 0 - ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - D_o1_id[1,1] - 0 - o1 > 1 - 0 - o1 > 2 - 0 - - -$m4c -$m4c$o1 - C1 B21 time c1 D_o1_id[1,1] D_o1_id[1,2] - 0 0 0 0 0 0 -D_o1_id[2,2] D_o1_id[1,3] D_o1_id[2,3] D_o1_id[3,3] D_o1_id[1,4] D_o1_id[2,4] - 0 0 0 0 0 0 -D_o1_id[3,4] D_o1_id[4,4] o1 > 1 o1 > 2 - 0 0 0 0 - - -$m4d -$m4d$o1 - C1 time I(time^2) b21 c1 C1:time - 0 0 0 0 0 0 - b21:c1 D_o1_id[1,1] D_o1_id[1,2] D_o1_id[2,2] o1 > 1 o1 > 2 - 0 0 0 0 0 0 - - -$m4e -$m4e$o1 - C1 log(time) I(time^2) p1 D_o1_id[1,1] o1 > 1 - 0 0 0 0 0 0 - o1 > 2 - 0 - - -$m5a -$m5a$o1 - O22 O23 C1 C2 C1 C2 - 0 0 0 0 0 0 - b21 b21 D_o1_id[1,1] o1 > 1 o1 > 2 - 0 0 0 0 0 - - -$m5b -$m5b$o1 - M22 M23 M24 O22 O23 c1:C2 - 0 0 0 0 0 0 - C2 C2 c1 c1 D_o1_id[1,1] o1 > 1 - 0 0 0 0 0 0 - o1 > 2 - 0 - - -$m5c -$m5c$o1 - M22 M23 M24 O22 O23 C2 - 0 0 0 0 0 0 - C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1] - 0 0 0 0 0 0 - o1 > 1 o1 > 2 - 0 0 - - -$m5d -$m5d$o1 - M22 M23 M24 O22 O23 M22:C2 - 0 0 0 0 0 0 - M23:C2 M24:C2 C2 C2 c1 c1 - 0 0 0 0 0 0 -D_o1_id[1,1] o1 > 1 o1 > 2 - 0 0 0 - - -$m5e -$m5e$o1 - M22 M23 M24 C2 O22 O23 - 0 0 0 0 0 0 - M22:C2 M23:C2 M24:C2 M22 M23 M24 - 0 0 0 0 0 0 - C2 O22 O23 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 - c1 c1 D_o1_id[1,1] o1 > 1 o1 > 2 - 0 0 0 0 0 - - -$m6a -$m6a$o1 - O22 O23 C1 C2 C1 C2 - 0 0 0 0 0 0 - b21 b21 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 - 0 0 0 0 0 - - -$m6b -$m6b$o1 - M22 M23 M24 O22 O23 c1:C2 - 0 0 0 0 0 0 - C2 C2 c1 c1 D_o1_id[1,1] o1 ≤ 1 - 0 0 0 0 0 0 - o1 ≤ 2 - 0 - - -$m6c -$m6c$o1 - M22 M23 M24 O22 O23 C2 - 0 0 0 0 0 0 - C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1] - 0 0 0 0 0 0 - o1 ≤ 1 o1 ≤ 2 - 0 0 - - -$m6d -$m6d$o1 - M22 M23 M24 O22 O23 M22:C2 - 0 0 0 0 0 0 - M23:C2 M24:C2 C2 C2 c1 c1 - 0 0 0 0 0 0 -D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 - 0 0 0 - - -$m6e -$m6e$o1 - M22 M23 M24 C2 O22 O23 - 0 0 0 0 0 0 - M22:C2 M23:C2 M24:C2 M22 M23 M24 - 0 0 0 0 0 0 - C2 O22 O23 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 - c1 c1 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2 - 0 0 0 0 0 - - -$m7a -$m7a$y -(Intercept) C1 o1.L o1.Q o22 o23 - 0 0 0 0 0 0 - o24 x2 x3 x4 time sigma_y - 0 0 0 0 0 0 -D_y_id[1,1] - 0 - - -$m7b -$m7b$y -(Intercept) o22 o23 o24 o1.L o1.Q - 0 0 0 0 0 0 - c2 b21 sigma_y D_y_id[1,1] - 0 0 0 0 - - diff --git a/tests/testthat/testout/clmm_lapply.models0.confint..txt b/tests/testthat/testout/clmm_lapply.models0.confint..txt deleted file mode 100644 index 071a70c4..00000000 --- a/tests/testthat/testout/clmm_lapply.models0.confint..txt +++ /dev/null @@ -1,430 +0,0 @@ -$m0a -$m0a$o1 - 2.5% 97.5% -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m0b -$m0b$o2 - 2.5% 97.5% -D_o2_id[1,1] 0 0 -o2 > 1 0 0 -o2 > 2 0 0 -o2 > 3 0 0 - - -$m1a -$m1a$o1 - 2.5% 97.5% -C1 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m1b -$m1b$o2 - 2.5% 97.5% -C1 0 0 -D_o2_id[1,1] 0 0 -o2 > 1 0 0 -o2 > 2 0 0 -o2 > 3 0 0 - - -$m1c -$m1c$o1 - 2.5% 97.5% -c1 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m1d -$m1d$o2 - 2.5% 97.5% -c1 0 0 -D_o2_id[1,1] 0 0 -o2 > 1 0 0 -o2 > 2 0 0 -o2 > 3 0 0 - - -$m2a -$m2a$o1 - 2.5% 97.5% -C2 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m2b -$m2b$o2 - 2.5% 97.5% -C2 0 0 -D_o2_id[1,1] 0 0 -o2 > 1 0 0 -o2 > 2 0 0 -o2 > 3 0 0 - - -$m2c -$m2c$o1 - 2.5% 97.5% -c2 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m2d -$m2d$o2 - 2.5% 97.5% -c2 0 0 -D_o2_id[1,1] 0 0 -o2 > 1 0 0 -o2 > 2 0 0 -o2 > 3 0 0 - - -$m3a -$m3a$c1 - 2.5% 97.5% -(Intercept) 0 0 -o1.L 0 0 -o1.Q 0 0 -sigma_c1 0 0 -D_c1_id[1,1] 0 0 - - -$m3b -$m3b$c1 - 2.5% 97.5% -(Intercept) 0 0 -o22 0 0 -o23 0 0 -o24 0 0 -sigma_c1 0 0 -D_c1_id[1,1] 0 0 - - -$m4a -$m4a$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -o22 0 0 -o23 0 0 -o24 0 0 -o22:abs(C1 - C2) 0 0 -o23:abs(C1 - C2) 0 0 -o24:abs(C1 - C2) 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m4b -$m4b$o1 - 2.5% 97.5% -abs(C1 - C2) 0 0 -log(C1) 0 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m4c -$m4c$o1 - 2.5% 97.5% -C1 0 0 -B21 0 0 -time 0 0 -c1 0 0 -D_o1_id[1,1] 0 0 -D_o1_id[1,2] 0 0 -D_o1_id[2,2] 0 0 -D_o1_id[1,3] 0 0 -D_o1_id[2,3] 0 0 -D_o1_id[3,3] 0 0 -D_o1_id[1,4] 0 0 -D_o1_id[2,4] 0 0 -D_o1_id[3,4] 0 0 -D_o1_id[4,4] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m4d -$m4d$o1 - 2.5% 97.5% -C1 0 0 -time 0 0 -I(time^2) 0 0 -b21 0 0 -c1 0 0 -C1:time 0 0 -b21:c1 0 0 -D_o1_id[1,1] 0 0 -D_o1_id[1,2] 0 0 -D_o1_id[2,2] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m4e -$m4e$o1 - 2.5% 97.5% -C1 0 0 -log(time) 0 0 -I(time^2) 0 0 -p1 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m5a -$m5a$o1 - 2.5% 97.5% -O22 0 0 -O23 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -b21 0 0 -b21 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m5b -$m5b$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -c1:C2 0 0 -C2 0 0 -C2 0 0 -c1 0 0 -c1 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m5c -$m5c$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -C2 0 0 -C2 0 0 -c1 0 0 -c1:C2 0 0 -c1 0 0 -c1:C2 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m5d -$m5d$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -C2 0 0 -C2 0 0 -c1 0 0 -c1 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m5e -$m5e$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -c1 0 0 -c1 0 0 -D_o1_id[1,1] 0 0 -o1 > 1 0 0 -o1 > 2 0 0 - - -$m6a -$m6a$o1 - 2.5% 97.5% -O22 0 0 -O23 0 0 -C1 0 0 -C2 0 0 -C1 0 0 -C2 0 0 -b21 0 0 -b21 0 0 -D_o1_id[1,1] 0 0 -o1 ≤ 1 0 0 -o1 ≤ 2 0 0 - - -$m6b -$m6b$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -c1:C2 0 0 -C2 0 0 -C2 0 0 -c1 0 0 -c1 0 0 -D_o1_id[1,1] 0 0 -o1 ≤ 1 0 0 -o1 ≤ 2 0 0 - - -$m6c -$m6c$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -C2 0 0 -C2 0 0 -c1 0 0 -c1:C2 0 0 -c1 0 0 -c1:C2 0 0 -D_o1_id[1,1] 0 0 -o1 ≤ 1 0 0 -o1 ≤ 2 0 0 - - -$m6d -$m6d$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -C2 0 0 -C2 0 0 -c1 0 0 -c1 0 0 -D_o1_id[1,1] 0 0 -o1 ≤ 1 0 0 -o1 ≤ 2 0 0 - - -$m6e -$m6e$o1 - 2.5% 97.5% -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -C2 0 0 -O22 0 0 -O23 0 0 -M22:C2 0 0 -M23:C2 0 0 -M24:C2 0 0 -c1 0 0 -c1 0 0 -D_o1_id[1,1] 0 0 -o1 ≤ 1 0 0 -o1 ≤ 2 0 0 - - -$m7a -$m7a$y - 2.5% 97.5% -(Intercept) 0 0 -C1 0 0 -o1.L 0 0 -o1.Q 0 0 -o22 0 0 -o23 0 0 -o24 0 0 -x2 0 0 -x3 0 0 -x4 0 0 -time 0 0 -sigma_y 0 0 -D_y_id[1,1] 0 0 - - -$m7b -$m7b$y - 2.5% 97.5% -(Intercept) 0 0 -o22 0 0 -o23 0 0 -o24 0 0 -o1.L 0 0 -o1.Q 0 0 -c2 0 0 -b21 0 0 -sigma_y 0 0 -D_y_id[1,1] 0 0 - - diff --git a/tests/testthat/testout/clmm_lapply.models0.function.x.coef.txt b/tests/testthat/testout/clmm_lapply.models0.function.x.coef.txt deleted file mode 100644 index b28c21a5..00000000 --- a/tests/testthat/testout/clmm_lapply.models0.function.x.coef.txt +++ /dev/null @@ -1,626 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a -$m0a$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - - -$m0b -$m0b$o2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - - -$m1a -$m1a$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN - - -$m1b -$m1b$o2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN - - -$m1c -$m1c$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -c1 0 0 0 0 0 NaN NaN - - -$m1d -$m1d$o2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -c1 0 0 0 0 0 NaN NaN - - -$m2a -$m2a$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C2 0 0 0 0 0 NaN NaN - - -$m2b -$m2b$o2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C2 0 0 0 0 0 NaN NaN - - -$m2c -$m2c$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -c2 0 0 0 0 0 NaN NaN - - -$m2d -$m2d$o2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -c2 0 0 0 0 0 NaN NaN - - -$m3a -$m3a$c1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -o1.L 0 0 0 0 0 NaN NaN -o1.Q 0 0 0 0 0 NaN NaN - - -$m3b -$m3b$c1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -o22 0 0 0 0 0 NaN NaN -o23 0 0 0 0 0 NaN NaN -o24 0 0 0 0 0 NaN NaN - - -$m4a -$m4a$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -abs(C1 - C2) 0 0 0 0 0 NaN NaN -log(C1) 0 0 0 0 0 NaN NaN -o22 0 0 0 0 0 NaN NaN -o23 0 0 0 0 0 NaN NaN -o24 0 0 0 0 0 NaN NaN -o22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -o23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -o24:abs(C1 - C2) 0 0 0 0 0 NaN NaN - - -$m4b -$m4b$o1 - Mean SD 2.5% 97.5% -abs(C1 - C2) 0 0 0 0 -log(C1) 0 0 0 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0 - tail-prob. GR-crit -abs(C1 - C2) 0 NaN -log(C1) 0 NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN - MCE/SD -abs(C1 - C2) NaN -log(C1) NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN - - -$m4c -$m4c$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN -B21 0 0 0 0 0 NaN NaN -time 0 0 0 0 0 NaN NaN -c1 0 0 0 0 0 NaN NaN - - -$m4d -$m4d$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN -time 0 0 0 0 0 NaN NaN -I(time^2) 0 0 0 0 0 NaN NaN -b21 0 0 0 0 0 NaN NaN -c1 0 0 0 0 0 NaN NaN -C1:time 0 0 0 0 0 NaN NaN -b21:c1 0 0 0 0 0 NaN NaN - - -$m4e -$m4e$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN -log(time) 0 0 0 0 0 NaN NaN -I(time^2) 0 0 0 0 0 NaN NaN -p1 0 0 0 0 0 NaN NaN - - -$m5a -$m5a$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C1 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C1 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: b21 0 0 0 0 0 NaN NaN -o13: b21 0 0 0 0 0 NaN NaN - - -$m5b -$m5b$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -c1:C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - - -$m5c -$m5c$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o12: c1:C2 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN -o13: c1:C2 0 0 0 0 0 NaN NaN - - -$m5d -$m5d$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -M22:C2 0 0 0 0 0 NaN NaN -M23:C2 0 0 0 0 0 NaN NaN -M24:C2 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - - -$m5e -$m5e$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o12: M22 0 0 0 0 0 NaN NaN -o12: M23 0 0 0 0 0 NaN NaN -o12: M24 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o12: O22 0 0 0 0 0 NaN NaN -o12: O23 0 0 0 0 0 NaN NaN -o12: M22:C2 0 0 0 0 0 NaN NaN -o12: M23:C2 0 0 0 0 0 NaN NaN -o12: M24:C2 0 0 0 0 0 NaN NaN -o13: M22 0 0 0 0 0 NaN NaN -o13: M23 0 0 0 0 0 NaN NaN -o13: M24 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o13: O22 0 0 0 0 0 NaN NaN -o13: O23 0 0 0 0 0 NaN NaN -o13: M22:C2 0 0 0 0 0 NaN NaN -o13: M23:C2 0 0 0 0 0 NaN NaN -o13: M24:C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - - -$m6a -$m6a$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C1 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C1 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: b21 0 0 0 0 0 NaN NaN -o13: b21 0 0 0 0 0 NaN NaN - - -$m6b -$m6b$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -c1:C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - - -$m6c -$m6c$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o12: c1:C2 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN -o13: c1:C2 0 0 0 0 0 NaN NaN - - -$m6d -$m6d$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -M22:C2 0 0 0 0 0 NaN NaN -M23:C2 0 0 0 0 0 NaN NaN -M24:C2 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - - -$m6e -$m6e$o1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o12: M22 0 0 0 0 0 NaN NaN -o12: M23 0 0 0 0 0 NaN NaN -o12: M24 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o12: O22 0 0 0 0 0 NaN NaN -o12: O23 0 0 0 0 0 NaN NaN -o12: M22:C2 0 0 0 0 0 NaN NaN -o12: M23:C2 0 0 0 0 0 NaN NaN -o12: M24:C2 0 0 0 0 0 NaN NaN -o13: M22 0 0 0 0 0 NaN NaN -o13: M23 0 0 0 0 0 NaN NaN -o13: M24 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o13: O22 0 0 0 0 0 NaN NaN -o13: O23 0 0 0 0 0 NaN NaN -o13: M22:C2 0 0 0 0 0 NaN NaN -o13: M23:C2 0 0 0 0 0 NaN NaN -o13: M24:C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - - -$m7a -$m7a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -C1 0 0 0 0 0 NaN NaN -o1.L 0 0 0 0 0 NaN NaN -o1.Q 0 0 0 0 0 NaN NaN -o22 0 0 0 0 0 NaN NaN -o23 0 0 0 0 0 NaN NaN -o24 0 0 0 0 0 NaN NaN -x2 0 0 0 0 0 NaN NaN -x3 0 0 0 0 0 NaN NaN -x4 0 0 0 0 0 NaN NaN -time 0 0 0 0 0 NaN NaN - - -$m7b -$m7b$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -o22 0 0 0 0 0 NaN NaN -o23 0 0 0 0 0 NaN NaN -o24 0 0 0 0 0 NaN NaN -o1.L 0 0 0 0 0 NaN NaN -o1.Q 0 0 0 0 0 NaN NaN -c2 0 0 0 0 0 NaN NaN -b21 0 0 0 0 0 NaN NaN - - diff --git a/tests/testthat/testout/clmm_lapply.models0.print..txt b/tests/testthat/testout/clmm_lapply.models0.print..txt deleted file mode 100644 index de5c5fdb..00000000 --- a/tests/testthat/testout/clmm_lapply.models0.print..txt +++ /dev/null @@ -1,1276 +0,0 @@ - -Call: -clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 - 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 - 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 C1 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 C1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 c1 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 C2 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 C2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 c2 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 c2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -Call: -lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020) - - Bayesian linear mixed model for "c1" - -Fixed effects: -(Intercept) o1.L o1.Q - 0 0 0 - - -Random effects covariance matrix: -$id - c1 - (Intercept) - c1 (Intercept) 0 - - - -Residual standard deviation: -sigma_c1 - 0 - -Call: -lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020) - - Bayesian linear mixed model for "c1" - -Fixed effects: -(Intercept) o22 o23 o24 - 0 0 0 0 - - -Random effects covariance matrix: -$id - c1 - (Intercept) - c1 (Intercept) 0 - - - -Residual standard deviation: -sigma_c1 - 0 - -Call: -clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 | - id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: - o1 > 1 o1 > 2 M22 M23 - 0 0 0 0 - M24 abs(C1 - C2) log(C1) o22 - 0 0 0 0 - o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2) - 0 0 0 0 -o24:abs(C1 - C2) - 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: - o1 > 1 - 0 - o1 > 2 - 0 - abs(C1 - C2) - 0 - log(C1) - 0 - ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id), - data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 C1 B21 time c1 - 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 o1 o1 o1 - (Intercept) c1 time c1:time - o1 (Intercept) 0 0 0 0 - o1 c1 0 0 0 0 - o1 time 0 0 0 0 - o1 c1:time 0 0 0 0 - - -Call: -clmm_imp(fixed = o1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: - o1 > 1 o1 > 2 C1 time I(time^2) b21 c1 C1:time - 0 0 0 0 0 0 0 0 - b21:c1 - 0 - - -Random effects covariance matrix: -$id - o1 o1 - (Intercept) time - o1 (Intercept) 0 0 - o1 time 0 0 - - -Call: -clmm_imp(fixed = o1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, - random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: - o1 > 1 o1 > 2 C1 log(time) I(time^2) p1 - 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 O22 O23 C1 C2 C1 C2 b21 b21 - 0 0 0 0 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 - 0 0 0 0 0 0 0 0 0 0 0 - c1 - 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 - 0 0 0 0 0 0 0 0 0 0 0 - c1 c1:C2 - 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C2 c1 c1 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 - M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 - 0 0 0 0 0 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21 - 0 0 0 0 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 - 0 0 0 0 0 0 0 0 0 0 0 - c1 - 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 - 0 0 0 0 0 0 0 0 0 0 0 - c1 c1:C2 - 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C2 c1 c1 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 - M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 - 0 0 0 0 0 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -Call: -lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 | - id, n.adapt = 5, n.iter = 10, seed = 2020) - - Bayesian linear mixed model for "y" - -Fixed effects: -(Intercept) C1 o1.L o1.Q o22 o23 - 0 0 0 0 0 0 - o24 x2 x3 x4 time - 0 0 0 0 0 - - -Random effects covariance matrix: -$id - y - (Intercept) - y (Intercept) 0 - - - -Residual standard deviation: -sigma_y - 0 - -Call: -lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 | - id, n.adapt = 5, n.iter = 10, seed = 2020) - - Bayesian linear mixed model for "y" - -Fixed effects: -(Intercept) o22 o23 o24 o1.L o1.Q - 0 0 0 0 0 0 - c2 b21 - 0 0 - - -Random effects covariance matrix: -$id - y - (Intercept) - y (Intercept) 0 - - - -Residual standard deviation: -sigma_y - 0 -$m0a - -Call: -clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 - 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m0b - -Call: -clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 - 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -$m1a - -Call: -clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 C1 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m1b - -Call: -clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 C1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -$m1c - -Call: -clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 c1 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m1d - -Call: -clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -$m2a - -Call: -clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 C2 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m2b - -Call: -clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 C2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -$m2c - -Call: -clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 c2 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m2d - -Call: -clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o2" - -Fixed effects: -o2 > 1 o2 > 2 o2 > 3 c2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - o2 - (Intercept) - o2 (Intercept) 0 - - -$m3a - -Call: -lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020) - - Bayesian linear mixed model for "c1" - -Fixed effects: -(Intercept) o1.L o1.Q - 0 0 0 - - -Random effects covariance matrix: -$id - c1 - (Intercept) - c1 (Intercept) 0 - - - -Residual standard deviation: -sigma_c1 - 0 - -$m3b - -Call: -lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020) - - Bayesian linear mixed model for "c1" - -Fixed effects: -(Intercept) o22 o23 o24 - 0 0 0 0 - - -Random effects covariance matrix: -$id - c1 - (Intercept) - c1 (Intercept) 0 - - - -Residual standard deviation: -sigma_c1 - 0 - -$m4a - -Call: -clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 | - id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: - o1 > 1 o1 > 2 M22 M23 - 0 0 0 0 - M24 abs(C1 - C2) log(C1) o22 - 0 0 0 0 - o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2) - 0 0 0 0 -o24:abs(C1 - C2) - 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m4b - -Call: -clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: - o1 > 1 - 0 - o1 > 2 - 0 - abs(C1 - C2) - 0 - log(C1) - 0 - ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m4c - -Call: -clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id), - data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 C1 B21 time c1 - 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 o1 o1 o1 - (Intercept) c1 time c1:time - o1 (Intercept) 0 0 0 0 - o1 c1 0 0 0 0 - o1 time 0 0 0 0 - o1 c1:time 0 0 0 0 - - -$m4d - -Call: -clmm_imp(fixed = o1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: - o1 > 1 o1 > 2 C1 time I(time^2) b21 c1 C1:time - 0 0 0 0 0 0 0 0 - b21:c1 - 0 - - -Random effects covariance matrix: -$id - o1 o1 - (Intercept) time - o1 (Intercept) 0 0 - o1 time 0 0 - - -$m4e - -Call: -clmm_imp(fixed = o1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, - random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: - o1 > 1 o1 > 2 C1 log(time) I(time^2) p1 - 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m5a - -Call: -clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 O22 O23 C1 C2 C1 C2 b21 b21 - 0 0 0 0 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m5b - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 - 0 0 0 0 0 0 0 0 0 0 0 - c1 - 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m5c - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 - 0 0 0 0 0 0 0 0 0 0 0 - c1 c1:C2 - 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m5d - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C2 c1 c1 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m5e - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 > 1 o1 > 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 - M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 - 0 0 0 0 0 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m6a - -Call: -clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21 - 0 0 0 0 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m6b - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1 - 0 0 0 0 0 0 0 0 0 0 0 - c1 - 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m6c - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2 - 0 0 0 0 0 0 0 0 0 0 0 - c1 c1:C2 - 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m6d - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2 - 0 0 0 0 0 0 0 0 0 0 0 - C2 c1 c1 - 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m6e - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian cumulative logit mixed model for "o1" - -Fixed effects: -o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 - 0 0 0 0 0 0 0 0 0 0 0 - M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1 - 0 0 0 0 0 0 0 0 0 0 0 - - -Random effects covariance matrix: -$id - o1 - (Intercept) - o1 (Intercept) 0 - - -$m7a - -Call: -lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 | - id, n.adapt = 5, n.iter = 10, seed = 2020) - - Bayesian linear mixed model for "y" - -Fixed effects: -(Intercept) C1 o1.L o1.Q o22 o23 - 0 0 0 0 0 0 - o24 x2 x3 x4 time - 0 0 0 0 0 - - -Random effects covariance matrix: -$id - y - (Intercept) - y (Intercept) 0 - - - -Residual standard deviation: -sigma_y - 0 - -$m7b - -Call: -lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 | - id, n.adapt = 5, n.iter = 10, seed = 2020) - - Bayesian linear mixed model for "y" - -Fixed effects: -(Intercept) o22 o23 o24 o1.L o1.Q - 0 0 0 0 0 0 - c2 b21 - 0 0 - - -Random effects covariance matrix: -$id - y - (Intercept) - y (Intercept) 0 - - - -Residual standard deviation: -sigma_y - 0 - diff --git a/tests/testthat/testout/clmm_lapply.models0.summary..txt b/tests/testthat/testout/clmm_lapply.models0.summary..txt deleted file mode 100644 index b8e0ccd3..00000000 --- a/tests/testthat/testout/clmm_lapply.models0.summary..txt +++ /dev/null @@ -1,1504 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m0b - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o2 > 1 0 0 0 0 0 NaN NaN -o2 > 2 0 0 0 0 0 NaN NaN -o2 > 3 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m1a - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m1b - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o2 > 1 0 0 0 0 0 NaN NaN -o2 > 2 0 0 0 0 0 NaN NaN -o2 > 3 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m1c - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m1d - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o2 > 1 0 0 0 0 0 NaN NaN -o2 > 2 0 0 0 0 0 NaN NaN -o2 > 3 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m2a - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m2b - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o2 > 1 0 0 0 0 0 NaN NaN -o2 > 2 0 0 0 0 0 NaN NaN -o2 > 3 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m2c - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -c2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m2d - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -c2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o2 > 1 0 0 0 0 0 NaN NaN -o2 > 2 0 0 0 0 0 NaN NaN -o2 > 3 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m3a - -Bayesian linear mixed model fitted with JointAI - -Call: -lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -o1.L 0 0 0 0 0 NaN NaN -o1.Q 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_c1_id[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_c1 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 1:10 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m3b - -Bayesian linear mixed model fitted with JointAI - -Call: -lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -o22 0 0 0 0 0 NaN NaN -o23 0 0 0 0 0 NaN NaN -o24 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_c1_id[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_c1 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4a - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 | - id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -abs(C1 - C2) 0 0 0 0 0 NaN NaN -log(C1) 0 0 0 0 0 NaN NaN -o22 0 0 0 0 0 NaN NaN -o23 0 0 0 0 0 NaN NaN -o24 0 0 0 0 0 NaN NaN -o22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -o23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -o24:abs(C1 - C2) 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4b - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% -abs(C1 - C2) 0 0 0 0 -log(C1) 0 0 0 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 0 -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0 - tail-prob. GR-crit -abs(C1 - C2) 0 NaN -log(C1) 0 NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN - MCE/SD -abs(C1 - C2) NaN -log(C1) NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN -ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4c - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id), - data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN -B21 0 0 0 0 0 NaN NaN -time 0 0 0 0 0 NaN NaN -c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN -D_o1_id[1,2] 0 0 0 0 0 NaN NaN -D_o1_id[2,2] 0 0 0 0 NaN NaN -D_o1_id[1,3] 0 0 0 0 0 NaN NaN -D_o1_id[2,3] 0 0 0 0 0 NaN NaN -D_o1_id[3,3] 0 0 0 0 NaN NaN -D_o1_id[1,4] 0 0 0 0 0 NaN NaN -D_o1_id[2,4] 0 0 0 0 0 NaN NaN -D_o1_id[3,4] 0 0 0 0 0 NaN NaN -D_o1_id[4,4] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4d - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN -time 0 0 0 0 0 NaN NaN -I(time^2) 0 0 0 0 0 NaN NaN -b21 0 0 0 0 0 NaN NaN -c1 0 0 0 0 0 NaN NaN -C1:time 0 0 0 0 0 NaN NaN -b21:c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN -D_o1_id[1,2] 0 0 0 0 0 NaN NaN -D_o1_id[2,2] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4e - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, - random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -C1 0 0 0 0 0 NaN NaN -log(time) 0 0 0 0 0 NaN NaN -I(time^2) 0 0 0 0 0 NaN NaN -p1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m5a - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C1 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C1 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: b21 0 0 0 0 0 NaN NaN -o13: b21 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m5b - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -c1:C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m5c - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o12: c1:C2 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN -o13: c1:C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m5d - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -M22:C2 0 0 0 0 0 NaN NaN -M23:C2 0 0 0 0 0 NaN NaN -M24:C2 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m5e - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o12: M22 0 0 0 0 0 NaN NaN -o12: M23 0 0 0 0 0 NaN NaN -o12: M24 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o12: O22 0 0 0 0 0 NaN NaN -o12: O23 0 0 0 0 0 NaN NaN -o12: M22:C2 0 0 0 0 0 NaN NaN -o12: M23:C2 0 0 0 0 0 NaN NaN -o12: M24:C2 0 0 0 0 0 NaN NaN -o13: M22 0 0 0 0 0 NaN NaN -o13: M23 0 0 0 0 0 NaN NaN -o13: M24 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o13: O22 0 0 0 0 0 NaN NaN -o13: O23 0 0 0 0 0 NaN NaN -o13: M22:C2 0 0 0 0 0 NaN NaN -o13: M23:C2 0 0 0 0 0 NaN NaN -o13: M24:C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 > 1 0 0 0 0 0 NaN NaN -o1 > 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m6a - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020, - warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C1 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C1 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: b21 0 0 0 0 0 NaN NaN -o13: b21 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 ≤ 1 0 0 0 0 0 NaN NaN -o1 ≤ 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m6b - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -c1:C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 ≤ 1 0 0 0 0 0 NaN NaN -o1 ≤ 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m6c - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o12: c1:C2 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN -o13: c1:C2 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 ≤ 1 0 0 0 0 0 NaN NaN -o1 ≤ 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m6d - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN -O22 0 0 0 0 0 NaN NaN -O23 0 0 0 0 0 NaN NaN -M22:C2 0 0 0 0 0 NaN NaN -M23:C2 0 0 0 0 0 NaN NaN -M24:C2 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 ≤ 1 0 0 0 0 0 NaN NaN -o1 ≤ 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m6e - -Bayesian cumulative logit mixed model fitted with JointAI - -Call: -clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), - nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE, - mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o12: M22 0 0 0 0 0 NaN NaN -o12: M23 0 0 0 0 0 NaN NaN -o12: M24 0 0 0 0 0 NaN NaN -o12: C2 0 0 0 0 0 NaN NaN -o12: O22 0 0 0 0 0 NaN NaN -o12: O23 0 0 0 0 0 NaN NaN -o12: M22:C2 0 0 0 0 0 NaN NaN -o12: M23:C2 0 0 0 0 0 NaN NaN -o12: M24:C2 0 0 0 0 0 NaN NaN -o13: M22 0 0 0 0 0 NaN NaN -o13: M23 0 0 0 0 0 NaN NaN -o13: M24 0 0 0 0 0 NaN NaN -o13: C2 0 0 0 0 0 NaN NaN -o13: O22 0 0 0 0 0 NaN NaN -o13: O23 0 0 0 0 0 NaN NaN -o13: M22:C2 0 0 0 0 0 NaN NaN -o13: M23:C2 0 0 0 0 0 NaN NaN -o13: M24:C2 0 0 0 0 0 NaN NaN -o12: c1 0 0 0 0 0 NaN NaN -o13: c1 0 0 0 0 0 NaN NaN - -Posterior summary of the intercepts: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -o1 ≤ 1 0 0 0 0 0 NaN NaN -o1 ≤ 2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_o1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m7a - -Bayesian linear mixed model fitted with JointAI - -Call: -lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 | - id, n.adapt = 5, n.iter = 10, seed = 2020) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -C1 0 0 0 0 0 NaN NaN -o1.L 0 0 0 0 0 NaN NaN -o1.Q 0 0 0 0 0 NaN NaN -o22 0 0 0 0 0 NaN NaN -o23 0 0 0 0 0 NaN NaN -o24 0 0 0 0 0 NaN NaN -x2 0 0 0 0 0 NaN NaN -x3 0 0 0 0 0 NaN NaN -x4 0 0 0 0 0 NaN NaN -time 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_y_id[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_y 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m7b - -Bayesian linear mixed model fitted with JointAI - -Call: -lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 | - id, n.adapt = 5, n.iter = 10, seed = 2020) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -o22 0 0 0 0 0 NaN NaN -o23 0 0 0 0 0 NaN NaN -o24 0 0 0 0 0 NaN NaN -o1.L 0 0 0 0 0 NaN NaN -o1.Q 0 0 0 0 0 NaN NaN -c2 0 0 0 0 0 NaN NaN -b21 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_y_id[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_y 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - diff --git a/tests/testthat/testout/coxph_lapply.models.jagsmodel..txt b/tests/testthat/testout/coxph_lapply.models.jagsmodel..txt deleted file mode 100644 index 1a4b6107..00000000 --- a/tests/testthat/testout/coxph_lapply.models.jagsmodel..txt +++ /dev/null @@ -1,465 +0,0 @@ -$m0a -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (i in 1:312) { - logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) - eta_Srv_ftm_stts_cn[i] <- 0 - logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - - logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) - - log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) - phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) - zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - -} -$m1a -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (i in 1:312) { - logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) - eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] + - M_lvlone[i, 5] * beta[2] - logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - - logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) - - log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) - phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) - zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - -} -$m1b -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (i in 1:312) { - logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) - eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] + - M_lvlone[i, 5] * beta[2] - logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - - logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) - - log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) - phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) - zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - -} -$m2a -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (i in 1:312) { - logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) - eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1] - logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - - logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) - - log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) - phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) - zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - - - - # Normal model for copper ------------------------------------------------------- - for (i in 1:312) { - M_lvlone[i, 3] ~ dnorm(mu_copper[i], tau_copper) - mu_copper[i] <- M_lvlone[i, 4] * alpha[1] - } - - # Priors for the model for copper - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_copper <- sqrt(1/tau_copper) - - -} -$m3a -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (i in 1:312) { - logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) - eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] + - M_lvlone[i, 6] * beta[2] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4] + - (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * beta[5] - logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - - logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) - - log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) - phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) - zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - - - - # Normal model for trig --------------------------------------------------------- - for (i in 1:312) { - M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, ) - mu_trig[i] <- M_lvlone[i, 5] * alpha[1] + - (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[2] + - M_lvlone[i, 6] * alpha[3] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[4] - - M_lvlone[i, 9] <- log(M_lvlone[i, 3]) - - - } - - # Priors for the model for trig - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_trig <- sqrt(1/tau_trig) - - - - - # Normal model for copper ------------------------------------------------------- - for (i in 1:312) { - M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper) - mu_copper[i] <- M_lvlone[i, 5] * alpha[5] + M_lvlone[i, 6] * alpha[6] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[7] - - M_lvlone[i, 8] <- abs(M_lvlone[i, 7] - M_lvlone[i, 4]) - - - } - - # Priors for the model for copper - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_copper <- sqrt(1/tau_copper) - - -} -$m3b -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (i in 1:312) { - logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ]) - eta_Srv_ftm_stts_cn[i] <- b_Srv_ftm_stts_cn_center[group_center[i], 1] + - beta[1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[2] * M_lvlone[i, 5] + - beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + - beta[5] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i] - - logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ]) - - log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ]) - phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i]) - zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i]) - } - - for (ii in 1:10) { - b_Srv_ftm_stts_cn_center[ii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[ii, ], invD_Srv_ftm_stts_cn_center[ , ]) - mu_b_Srv_ftm_stts_cn_center[ii, 1] <- 0 - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1]) - - - # Normal mixed effects model for trig ------------------------------------------- - for (i in 1:312) { - M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, ) - mu_trig[i] <- b_trig_center[group_center[i], 1] + - alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[3] * M_lvlone[i, 5] + - alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - - - M_lvlone[i, 8] <- log(M_lvlone[i, 3]) - - } - - for (ii in 1:10) { - b_trig_center[ii, 1:1] ~ dnorm(mu_b_trig_center[ii, ], invD_trig_center[ , ]) - mu_b_trig_center[ii, 1] <- M_center[ii, 1] * alpha[1] - } - - # Priors for the model for trig - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_trig <- sqrt(1/tau_trig) - - invD_trig_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_trig_center[1, 1] <- 1 / (invD_trig_center[1, 1]) - - - # Normal mixed effects model for copper ----------------------------------------- - for (i in 1:312) { - M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper) - mu_copper[i] <- b_copper_center[group_center[i], 1] + alpha[6] * M_lvlone[i, 5] + - alpha[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - - - M_lvlone[i, 7] <- abs(M_lvlone[i, 6] - M_lvlone[i, 4]) - - } - - for (ii in 1:10) { - b_copper_center[ii, 1:1] ~ dnorm(mu_b_copper_center[ii, ], invD_copper_center[ , ]) - mu_b_copper_center[ii, 1] <- M_center[ii, 1] * alpha[5] - } - - # Priors for the model for copper - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_copper <- sqrt(1/tau_copper) - - invD_copper_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_copper_center[1, 1] <- 1 / (invD_copper_center[1, 1]) - -} -$m4a -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (ii in 1:312) { - logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ]) - eta_Srv_ftm_stts_cn[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[1] + - M_id[ii, 5] * beta[2] + M_id[ii, 6] * beta[3] - logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + - M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] * beta[6] + - M_lvlone[srow_Srv_ftm_stts_cn[ii], 4] * beta[7] + - M_lvlone[srow_Srv_ftm_stts_cn[ii], 5] * beta[8] - - logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + - (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] + - (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] + - M_lvlonegk[ii, 3, 1:15] * beta[6] + - M_lvlonegk[ii, 4, 1:15] * beta[7] + - M_lvlonegk[ii, 5, 1:15] * beta[8]) - - log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) - phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) - zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii]) - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:8) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - -} -$m4b -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (ii in 1:312) { - logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ]) - eta_Srv_ftm_stts_cn[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[1] + - M_id[ii, 5] * beta[2] + M_id[ii, 6] * beta[3] + - M_id[ii, 7] * beta[4] - logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] - - logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + - (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] + - (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]) - - log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) - phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) - zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii]) - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - -} -$m4c -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (ii in 1:312) { - logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ]) - eta_Srv_ftm_stts_cn[ii] <- b_Srv_ftm_stts_cn_center[group_center[pos_id[ii]], 1] + - beta[1] * (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[2] * M_id[ii, 4] - logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] - - logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + - (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + - (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]) - - log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) - phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) - zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii]) - } - - for (iii in 1:10) { - b_Srv_ftm_stts_cn_center[iii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[iii, ], invD_Srv_ftm_stts_cn_center[ , ]) - mu_b_Srv_ftm_stts_cn_center[iii, 1] <- 0 - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1]) - -} -$m4d -model { - - # Cox PH model for Srv_ftm_stts_cn ---------------------------------------------- - for (ii in 1:312) { - logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ]) - eta_Srv_ftm_stts_cn[ii] <- b_Srv_ftm_stts_cn_center[group_center[pos_id[ii]], 1] + - beta[1] * (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[2] * M_id[ii, 4] - logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + - (M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5] - - logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[] - Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] + - (M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] + - (M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] + - (M_lvlonegk[ii, 3, 1:15] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5]) - - log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ]) - phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii]) - zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii]) - } - - for (iii in 1:10) { - b_Srv_ftm_stts_cn_center[iii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[iii, ], invD_Srv_ftm_stts_cn_center[ , ]) - mu_b_Srv_ftm_stts_cn_center[iii, 1] <- 0 - } - - - # Priors for the coefficients in the model for Srv_ftm_stts_cn - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - for (k in 1:6) { - beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv) - } - - invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1]) - -} diff --git a/tests/testthat/testout/coxph_lapply.models0.GR_crit.multiva.txt b/tests/testthat/testout/coxph_lapply.models0.GR_crit.multiva.txt deleted file mode 100644 index 7d7348b1..00000000 --- a/tests/testthat/testout/coxph_lapply.models0.GR_crit.multiva.txt +++ /dev/null @@ -1,161 +0,0 @@ -$m0a -Potential scale reduction factors: - - Point est. Upper C.I. -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN - - -$m1a -Potential scale reduction factors: - - Point est. Upper C.I. -age NaN NaN -sexfemale NaN NaN -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN - - -$m1b -Potential scale reduction factors: - - Point est. Upper C.I. -age NaN NaN -sexfemale NaN NaN -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN - - -$m2a -Potential scale reduction factors: - - Point est. Upper C.I. -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN -copper NaN NaN - - -$m3a -Potential scale reduction factors: - - Point est. Upper C.I. -copper NaN NaN -sexfemale NaN NaN -age NaN NaN -abs(age - copper) NaN NaN -log(trig) NaN NaN -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN - - -$m3b -Potential scale reduction factors: - - Point est. Upper C.I. -copper NaN NaN -sexfemale NaN NaN -age NaN NaN -abs(age - copper) NaN NaN -log(trig) NaN NaN -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN -D_Srv_ftm_stts_cn_center[1,1] NaN NaN - - -$m4a -Potential scale reduction factors: - - Point est. Upper C.I. -age NaN NaN -sexfemale NaN NaN -trtplacebo NaN NaN -albumin NaN NaN -platelet NaN NaN -stage.L NaN NaN -stage.Q NaN NaN -stage.C NaN NaN -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN - - -$m4b -Potential scale reduction factors: - - Point est. Upper C.I. -age NaN NaN -sexfemale NaN NaN -trtplacebo NaN NaN -sexfemale:trtplacebo NaN NaN -albumin NaN NaN -log(platelet) NaN NaN -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN - - -$m4c -Potential scale reduction factors: - - Point est. Upper C.I. -age NaN NaN -sexfemale NaN NaN -albumin NaN NaN -log(platelet) NaN NaN -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN -D_Srv_ftm_stts_cn_center[1,1] NaN NaN - - -$m4d -Potential scale reduction factors: - - Point est. Upper C.I. -age NaN NaN -sexfemale NaN NaN -albumin NaN NaN -ns(platelet, df = 2)1 NaN NaN -ns(platelet, df = 2)2 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN -D_Srv_ftm_stts_cn_center[1,1] NaN NaN - - diff --git a/tests/testthat/testout/coxph_lapply.models0.MC_error..txt b/tests/testthat/testout/coxph_lapply.models0.MC_error..txt deleted file mode 100644 index 308cec0f..00000000 --- a/tests/testthat/testout/coxph_lapply.models0.MC_error..txt +++ /dev/null @@ -1,232 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - est MCSE SD MCSE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN - -$m1a - est MCSE SD MCSE/SD -age 0 0 0 NaN -sexfemale 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN - -$m1b - est MCSE SD MCSE/SD -age 0 0 0 NaN -sexfemale 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN - -$m2a - est MCSE SD MCSE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN -copper 0 0 0 NaN - -$m3a - est MCSE SD MCSE/SD -copper 0 0 0 NaN -sexfemale 0 0 0 NaN -age 0 0 0 NaN -abs(age - copper) 0 0 0 NaN -log(trig) 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN - -$m3b - est MCSE SD MCSE/SD -copper 0 0 0 NaN -sexfemale 0 0 0 NaN -age 0 0 0 NaN -abs(age - copper) 0 0 0 NaN -log(trig) 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN -D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN - -$m4a - est MCSE SD MCSE/SD -age 0 0 0 NaN -sexfemale 0 0 0 NaN -trtplacebo 0 0 0 NaN -albumin 0 0 0 NaN -platelet 0 0 0 NaN -stage.L 0 0 0 NaN -stage.Q 0 0 0 NaN -stage.C 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN - -$m4b - est MCSE SD MCSE/SD -age 0 0 0 NaN -sexfemale 0 0 0 NaN -trtplacebo 0 0 0 NaN -sexfemale:trtplacebo 0 0 0 NaN -albumin 0 0 0 NaN -log(platelet) 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN - -$m4c - est MCSE SD MCSE/SD -age 0 0 0 NaN -sexfemale 0 0 0 NaN -albumin 0 0 0 NaN -log(platelet) 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN -D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN - -$m4d - est MCSE SD MCSE/SD -age 0 0 0 NaN -sexfemale 0 0 0 NaN -albumin 0 0 0 NaN -ns(platelet, df = 2)1 0 0 0 NaN -ns(platelet, df = 2)2 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN -D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN - diff --git a/tests/testthat/testout/coxph_lapply.models0.coef..txt b/tests/testthat/testout/coxph_lapply.models0.coef..txt deleted file mode 100644 index 7a7c7875..00000000 --- a/tests/testthat/testout/coxph_lapply.models0.coef..txt +++ /dev/null @@ -1,144 +0,0 @@ -$m0a -$m0a$`Surv(futime, status != "censored")` -beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] - 0 0 - - -$m1a -$m1a$`Surv(futime, status != "censored")` - age sexfemale - 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] - 0 0 - - -$m1b -$m1b$`Surv(futime, I(status != "censored"))` - age sexfemale - 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] - 0 0 - - -$m2a -$m2a$`Surv(futime, status != "censored")` - copper beta_Bh0_Srv_ftm_stts_cn[1] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] - 0 - - -$m3a -$m3a$`Surv(futime, status != "censored")` - copper sexfemale - 0 0 - age abs(age - copper) - 0 0 - log(trig) beta_Bh0_Srv_ftm_stts_cn[1] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] - 0 - - -$m3b -$m3b$`Surv(futime, status != "censored")` - copper sexfemale - 0 0 - age abs(age - copper) - 0 0 - log(trig) D_Srv_ftm_stts_cn_center[1,1] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] - 0 0 - - -$m4a -$m4a$`Surv(futime, status != "censored")` - age sexfemale - 0 0 - trtplacebo albumin - 0 0 - platelet stage.L - 0 0 - stage.Q stage.C - 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] - 0 0 - - -$m4b -$m4b$`Surv(futime, status != "censored")` - age sexfemale - 0 0 - trtplacebo sexfemale:trtplacebo - 0 0 - albumin log(platelet) - 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] - 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] - 0 0 - - -$m4c -$m4c$`Surv(futime, status != "censored")` - age sexfemale - 0 0 - albumin log(platelet) - 0 0 -D_Srv_ftm_stts_cn_center[1,1] beta_Bh0_Srv_ftm_stts_cn[1] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[6] - 0 - - -$m4d -$m4d$`Surv(futime, status != "censored")` - age sexfemale - 0 0 - albumin ns(platelet, df = 2)1 - 0 0 - ns(platelet, df = 2)2 D_Srv_ftm_stts_cn_center[1,1] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4] - 0 0 - beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6] - 0 0 - - diff --git a/tests/testthat/testout/coxph_lapply.models0.confint..txt b/tests/testthat/testout/coxph_lapply.models0.confint..txt deleted file mode 100644 index 9545e954..00000000 --- a/tests/testthat/testout/coxph_lapply.models0.confint..txt +++ /dev/null @@ -1,151 +0,0 @@ -$m0a -$m0a$`Surv(futime, status != "censored")` - 2.5% 97.5% -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m1a -$m1a$`Surv(futime, status != "censored")` - 2.5% 97.5% -age 0 0 -sexfemale 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m1b -$m1b$`Surv(futime, I(status != "censored"))` - 2.5% 97.5% -age 0 0 -sexfemale 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m2a -$m2a$`Surv(futime, status != "censored")` - 2.5% 97.5% -copper 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m3a -$m3a$`Surv(futime, status != "censored")` - 2.5% 97.5% -copper 0 0 -sexfemale 0 0 -age 0 0 -abs(age - copper) 0 0 -log(trig) 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m3b -$m3b$`Surv(futime, status != "censored")` - 2.5% 97.5% -copper 0 0 -sexfemale 0 0 -age 0 0 -abs(age - copper) 0 0 -log(trig) 0 0 -D_Srv_ftm_stts_cn_center[1,1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m4a -$m4a$`Surv(futime, status != "censored")` - 2.5% 97.5% -age 0 0 -sexfemale 0 0 -trtplacebo 0 0 -albumin 0 0 -platelet 0 0 -stage.L 0 0 -stage.Q 0 0 -stage.C 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m4b -$m4b$`Surv(futime, status != "censored")` - 2.5% 97.5% -age 0 0 -sexfemale 0 0 -trtplacebo 0 0 -sexfemale:trtplacebo 0 0 -albumin 0 0 -log(platelet) 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m4c -$m4c$`Surv(futime, status != "censored")` - 2.5% 97.5% -age 0 0 -sexfemale 0 0 -albumin 0 0 -log(platelet) 0 0 -D_Srv_ftm_stts_cn_center[1,1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - -$m4d -$m4d$`Surv(futime, status != "censored")` - 2.5% 97.5% -age 0 0 -sexfemale 0 0 -albumin 0 0 -ns(platelet, df = 2)1 0 0 -ns(platelet, df = 2)2 0 0 -D_Srv_ftm_stts_cn_center[1,1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 - - diff --git a/tests/testthat/testout/coxph_lapply.models0.function.x.coef.txt b/tests/testthat/testout/coxph_lapply.models0.function.x.coef.txt deleted file mode 100644 index 0f9754df..00000000 --- a/tests/testthat/testout/coxph_lapply.models0.function.x.coef.txt +++ /dev/null @@ -1,189 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a -$m0a$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - - -$m1a -$m1a$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN - - -$m1b -$m1b$`Surv(futime, I(status != "censored"))` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN - - -$m2a -$m2a$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -copper 0 0 0 0 0 NaN NaN - - -$m3a -$m3a$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -copper 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -age 0 0 0 0 0 NaN NaN -abs(age - copper) 0 0 0 0 0 NaN NaN -log(trig) 0 0 0 0 0 NaN NaN - - -$m3b -$m3b$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -copper 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -age 0 0 0 0 0 NaN NaN -abs(age - copper) 0 0 0 0 0 NaN NaN -log(trig) 0 0 0 0 0 NaN NaN - - -$m4a -$m4a$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -trtplacebo 0 0 0 0 0 NaN NaN -albumin 0 0 0 0 0 NaN NaN -platelet 0 0 0 0 0 NaN NaN -stage.L 0 0 0 0 0 NaN NaN -stage.Q 0 0 0 0 0 NaN NaN -stage.C 0 0 0 0 0 NaN NaN - - -$m4b -$m4b$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -trtplacebo 0 0 0 0 0 NaN NaN -sexfemale:trtplacebo 0 0 0 0 0 NaN NaN -albumin 0 0 0 0 0 NaN NaN -log(platelet) 0 0 0 0 0 NaN NaN - - -$m4c -$m4c$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -albumin 0 0 0 0 0 NaN NaN -log(platelet) 0 0 0 0 0 NaN NaN - - -$m4d -$m4d$`Surv(futime, status != "censored")` - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -albumin 0 0 0 0 0 NaN NaN -ns(platelet, df = 2)1 0 0 0 0 0 NaN NaN -ns(platelet, df = 2)2 0 0 0 0 0 NaN NaN - - diff --git a/tests/testthat/testout/coxph_lapply.models0.print..txt b/tests/testthat/testout/coxph_lapply.models0.print..txt deleted file mode 100644 index 013de711..00000000 --- a/tests/testthat/testout/coxph_lapply.models0.print..txt +++ /dev/null @@ -1,280 +0,0 @@ - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2, - n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale - 0 0 - -Call: -coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age + - sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))" - - -Coefficients: - age sexfemale - 0 0 - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper, - data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: -copper - 0 - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper + - sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, - NA))) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - copper sexfemale age abs(age - copper) - 0 0 0 0 - log(trig) - 0 - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper + - sex + age + abs(age - copper) + log(trig) + (1 | center), - data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, trunc = list(trig = c(1e-04, NA))) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - copper sexfemale age abs(age - copper) - 0 0 0 0 - log(trig) - 0 - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + trt + albumin + platelet + stage + (1 | id), data = PBC, - n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, - timevar = "day") - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale trtplacebo albumin platelet stage.L stage.Q - 0 0 0 0 0 0 0 - stage.C - 0 - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex * trt + albumin + log(platelet) + (1 | id), data = PBC, - n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, - timevar = "day") - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale trtplacebo - 0 0 0 -sexfemale:trtplacebo albumin log(platelet) - 0 0 0 - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + albumin + log(platelet) + (1 | id) + (1 | center), - data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, timevar = "day") - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale albumin log(platelet) - 0 0 0 0 - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center), - data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, timevar = "day") - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale albumin - 0 0 0 -ns(platelet, df = 2)1 ns(platelet, df = 2)2 - 0 0 -$m0a - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2, - n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - -$m1a - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale - 0 0 - -$m1b - -Call: -coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age + - sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))" - - -Coefficients: - age sexfemale - 0 0 - -$m2a - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper, - data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: -copper - 0 - -$m3a - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper + - sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, - NA))) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - copper sexfemale age abs(age - copper) - 0 0 0 0 - log(trig) - 0 - -$m3b - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper + - sex + age + abs(age - copper) + log(trig) + (1 | center), - data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, trunc = list(trig = c(1e-04, NA))) - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - copper sexfemale age abs(age - copper) - 0 0 0 0 - log(trig) - 0 - -$m4a - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + trt + albumin + platelet + stage + (1 | id), data = PBC, - n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, - timevar = "day") - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale trtplacebo albumin platelet stage.L stage.Q - 0 0 0 0 0 0 0 - stage.C - 0 - -$m4b - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex * trt + albumin + log(platelet) + (1 | id), data = PBC, - n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, - timevar = "day") - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale trtplacebo - 0 0 0 -sexfemale:trtplacebo albumin log(platelet) - 0 0 0 - -$m4c - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + albumin + log(platelet) + (1 | id) + (1 | center), - data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, timevar = "day") - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale albumin log(platelet) - 0 0 0 0 - -$m4d - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center), - data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, timevar = "day") - - Bayesian proportional hazards model for "Surv(futime, status != "censored")" - - -Coefficients: - age sexfemale albumin - 0 0 0 -ns(platelet, df = 2)1 ns(platelet, df = 2)2 - 0 0 - diff --git a/tests/testthat/testout/coxph_lapply.models0.summary..txt b/tests/testthat/testout/coxph_lapply.models0.summary..txt deleted file mode 100644 index 83e9cd3a..00000000 --- a/tests/testthat/testout/coxph_lapply.models0.summary..txt +++ /dev/null @@ -1,504 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2, - n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE) - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 2:5 -Sample size per chain = 4 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 312 - -$m1a - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:6 -Sample size per chain = 4 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 312 - -$m1b - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age + - sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:6 -Sample size per chain = 4 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 312 - -$m2a - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper, - data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, - mess = FALSE) - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -copper 0 0 0 0 0 NaN NaN - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:6 -Sample size per chain = 4 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 312 - -$m3a - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper + - sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, - NA))) - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -copper 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -age 0 0 0 0 0 NaN NaN -abs(age - copper) 0 0 0 0 0 NaN NaN -log(trig) 0 0 0 0 0 NaN NaN - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:12 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 312 - -$m3b - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ copper + - sex + age + abs(age - copper) + log(trig) + (1 | center), - data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, trunc = list(trig = c(1e-04, NA))) - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -copper 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -age 0 0 0 0 0 NaN NaN -abs(age - copper) 0 0 0 0 0 NaN NaN -log(trig) 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:12 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 312 -Number of groups: - - center: 10 - -$m4a - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + trt + albumin + platelet + stage + (1 | id), data = PBC, - n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, - timevar = "day") - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -trtplacebo 0 0 0 0 0 NaN NaN -albumin 0 0 0 0 0 NaN NaN -platelet 0 0 0 0 0 NaN NaN -stage.L 0 0 0 0 0 NaN NaN -stage.Q 0 0 0 0 0 NaN NaN -stage.C 0 0 0 0 0 NaN NaN - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:12 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 2257 -Number of groups: - - id: 312 - -$m4b - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex * trt + albumin + log(platelet) + (1 | id), data = PBC, - n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, - timevar = "day") - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -trtplacebo 0 0 0 0 0 NaN NaN -sexfemale:trtplacebo 0 0 0 0 0 NaN NaN -albumin 0 0 0 0 0 NaN NaN -log(platelet) 0 0 0 0 0 NaN NaN - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:12 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 2257 -Number of groups: - - id: 312 - -$m4c - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + albumin + log(platelet) + (1 | id) + (1 | center), - data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, timevar = "day") - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -albumin 0 0 0 0 0 NaN NaN -log(platelet) 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:12 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 2257 -Number of groups: - - center: 10 - - id: 312 - -$m4d - -Bayesian proportional hazards model fitted with JointAI - -Call: -coxph_imp(formula = Surv(futime, status != "censored") ~ age + - sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center), - data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE, timevar = "day") - - -Number of events: 169 - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -age 0 0 0 0 0 NaN NaN -sexfemale 0 0 0 0 0 NaN NaN -albumin 0 0 0 0 0 NaN NaN -ns(platelet, df = 2)1 0 0 0 0 0 NaN NaN -ns(platelet, df = 2)2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN -beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 3:12 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 2257 -Number of groups: - - center: 10 - - id: 312 - diff --git a/tests/testthat/testout/glm_lapply.models.data_list..rds b/tests/testthat/testout/glm_lapply.models.data_list..rds deleted file mode 100644 index bbb2e66975ffb4cd835c1b163efb1f9f36b12080..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 24653 zcmeFZbx>SQw=SN903mn?5ZocSyGwA_;K75t%LIp@fdC=E5Ii^pml+b=2Y0tY2OS*d zl6>#^-h1oZbIx~e)vxOJ$LZ>Nde`dN-MxEHKfPA3HA4~e?9rcua$t=5_NBtOi6jnA z%D1OA+eDl!L`Yr5U)+a7p3Ure)IdrU1WTB*&J?%BfAr`j5= 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zZMu4e=0(Ha&lJPcsG-ENP}L{My9RPW1;dpX`ctIw-06w%eRHEAT*j*0JpgmcTamONQ0pPQ8@fA@BZOo>U- e=*OPb+m8^Rb{eb>5BND~pdW5g$VMt1-TweJ`p~lg diff --git a/tests/testthat/testout/mlogit_lapply.models.jagsmodel..txt b/tests/testthat/testout/mlogit_lapply.models.jagsmodel..txt deleted file mode 100644 index 44b415f7..00000000 --- a/tests/testthat/testout/mlogit_lapply.models.jagsmodel..txt +++ /dev/null @@ -1,495 +0,0 @@ -$m0a -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] - log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[2] - log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[3] - } - - # Priors for the model for M1 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - } -$m0b -model { - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 2] * beta[1] - log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[2] - log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[3] - } - - # Priors for the model for M2 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - } -$m1a -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] - log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[5] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6] - } - - # Priors for the model for M1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - } -$m1b -model { - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 2] * beta[1] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] - log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[3] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] - log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[5] + - (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6] - } - - # Priors for the model for M2 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - } -$m2a -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - log(phi_M1[i, 3]) <- M_lvlone[i, 3] * beta[3] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] - log(phi_M1[i, 4]) <- M_lvlone[i, 3] * beta[5] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] - } - - # Priors for the model for M1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } -$m2b -model { - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 3] * beta[1] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2] - log(phi_M2[i, 3]) <- M_lvlone[i, 3] * beta[3] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] - log(phi_M2[i, 4]) <- M_lvlone[i, 3] * beta[5] + - (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6] - } - - # Priors for the model for M2 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 3] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } -$m3a -model { - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] + - M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4] - } - - # Priors for the model for C1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - } -$m3b -model { - - # Normal model for C1 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1) - mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] + - M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] - } - - # Priors for the model for C1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C1 <- sqrt(1/tau_C1) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 2] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 3] * alpha[1] - log(phi_M2[i, 3]) <- M_lvlone[i, 3] * alpha[2] - log(phi_M2[i, 4]) <- M_lvlone[i, 3] * alpha[3] - - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - } -$m4a -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] + - M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] + - M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] + - M_lvlone[i, 11] * beta[7] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[8] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12] - log(phi_M1[i, 3]) <- M_lvlone[i, 5] * beta[13] + M_lvlone[i, 6] * beta[14] + - M_lvlone[i, 7] * beta[15] + M_lvlone[i, 8] * beta[16] + - M_lvlone[i, 9] * beta[17] + M_lvlone[i, 10] * beta[18] + - M_lvlone[i, 11] * beta[19] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[20] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[21] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[22] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[23] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[24] - log(phi_M1[i, 4]) <- M_lvlone[i, 5] * beta[25] + M_lvlone[i, 6] * beta[26] + - M_lvlone[i, 7] * beta[27] + M_lvlone[i, 8] * beta[28] + - M_lvlone[i, 9] * beta[29] + M_lvlone[i, 10] * beta[30] + - M_lvlone[i, 11] * beta[31] + - (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[32] + - (M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[33] + - (M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[34] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[35] + - (M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[36] - } - - # Priors for the model for M1 - for (k in 1:36) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] + - M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] + - M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] + - M_lvlone[i, 11] * alpha[7] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8] - - M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] + - M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] + - M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] + - M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] + - (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23] - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Cumulative logit model for O2 ------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4]) - eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24] - - p_O2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[i, 2:4]))) - p_O2[i, 2] <- max(1e-10, min(1-1e-10, psum_O2[i, 1] - psum_O2[i, 2])) - p_O2[i, 3] <- max(1e-10, min(1-1e-10, psum_O2[i, 2] - psum_O2[i, 3])) - p_O2[i, 4] <- max(1e-10, min(1-1e-10, psum_O2[i, 3])) - - logit(psum_O2[i, 1]) <- gamma_O2[1] + eta_O2[i] - logit(psum_O2[i, 2]) <- gamma_O2[2] + eta_O2[i] - logit(psum_O2[i, 3]) <- gamma_O2[3] + eta_O2[i] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0) - } - - # Priors for the model for O2 - for (k in 24:24) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } - - delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_O2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1]) - gamma_O2[3] <- gamma_O2[2] - exp(delta_O2[2]) - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12] - M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12] - M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12] - } - - } -$m4b -model { - - # Multinomial logit model for M1 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4]) - - p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ]))) - p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ]))) - p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ]))) - p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ]))) - - log(phi_M1[i, 1]) <- 0 - log(phi_M1[i, 2]) <- M_lvlone[i, 4] * beta[1] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] - log(phi_M1[i, 3]) <- M_lvlone[i, 4] * beta[6] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[7] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[9] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[10] - log(phi_M1[i, 4]) <- M_lvlone[i, 4] * beta[11] + - (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[12] + - (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] + - (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[14] + - (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[15] - } - - # Priors for the model for M1 - for (k in 1:15) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (i in 1:100) { - M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2) - mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] + - M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] + - M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] + - M_lvlone[i, 14] * alpha[7] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8] - - M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2]) - - - } - - # Priors for the model for C2 - for (k in 1:8) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - - # Multinomial logit model for M2 ------------------------------------------------ - for (i in 1:100) { - M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4]) - - p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ]))) - p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ]))) - p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ]))) - p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ]))) - - log(phi_M2[i, 1]) <- 0 - log(phi_M2[i, 2]) <- M_lvlone[i, 4] * alpha[9] + M_lvlone[i, 12] * alpha[10] + - M_lvlone[i, 13] * alpha[11] + M_lvlone[i, 14] * alpha[12] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[13] - log(phi_M2[i, 3]) <- M_lvlone[i, 4] * alpha[14] + M_lvlone[i, 12] * alpha[15] + - M_lvlone[i, 13] * alpha[16] + M_lvlone[i, 14] * alpha[17] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[18] - log(phi_M2[i, 4]) <- M_lvlone[i, 4] * alpha[19] + M_lvlone[i, 12] * alpha[20] + - M_lvlone[i, 13] * alpha[21] + M_lvlone[i, 14] * alpha[22] + - (M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[23] - - M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - - - M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0) - - } - - # Priors for the model for M2 - for (k in 9:23) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - # Re-calculate interaction terms - for (i in 1:100) { - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] - } - - } diff --git a/tests/testthat/testout/mlogit_lapply.models0.GR_crit.multiva.txt b/tests/testthat/testout/mlogit_lapply.models0.GR_crit.multiva.txt deleted file mode 100644 index 8cd3c415..00000000 --- a/tests/testthat/testout/mlogit_lapply.models0.GR_crit.multiva.txt +++ /dev/null @@ -1,167 +0,0 @@ -$m0a -Potential scale reduction factors: - - Point est. Upper C.I. -M12: (Intercept) NaN NaN -M13: (Intercept) NaN NaN -M14: (Intercept) NaN NaN - - -$m0b -Potential scale reduction factors: - - Point est. Upper C.I. -M22: (Intercept) NaN NaN -M23: (Intercept) NaN NaN -M24: (Intercept) NaN NaN - - -$m1a -Potential scale reduction factors: - - Point est. Upper C.I. -M12: (Intercept) NaN NaN -M12: C1 NaN NaN -M13: (Intercept) NaN NaN -M13: C1 NaN NaN -M14: (Intercept) NaN NaN -M14: C1 NaN NaN - - -$m1b -Potential scale reduction factors: - - Point est. Upper C.I. -M22: (Intercept) NaN NaN -M22: C1 NaN NaN -M23: (Intercept) NaN NaN -M23: C1 NaN NaN -M24: (Intercept) NaN NaN -M24: C1 NaN NaN - - -$m2a -Potential scale reduction factors: - - Point est. Upper C.I. -M12: (Intercept) NaN NaN -M12: C2 NaN NaN -M13: (Intercept) NaN NaN -M13: C2 NaN NaN -M14: (Intercept) NaN NaN -M14: C2 NaN NaN - - -$m2b -Potential scale reduction factors: - - Point est. Upper C.I. -M22: (Intercept) NaN NaN -M22: C2 NaN NaN -M23: (Intercept) NaN NaN -M23: C2 NaN NaN -M24: (Intercept) NaN NaN -M24: C2 NaN NaN - - -$m3a -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -M12 NaN NaN -M13 NaN NaN -M14 NaN NaN -sigma_C1 NaN NaN - - -$m3b -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -M22 NaN NaN -M23 NaN NaN -M24 NaN NaN -sigma_C1 NaN NaN - - -$m4a -Potential scale reduction factors: - - Point est. Upper C.I. -M12: (Intercept) NaN NaN -M12: M22 NaN NaN -M12: M23 NaN NaN -M12: M24 NaN NaN -M12: O22 NaN NaN -M12: O23 NaN NaN -M12: O24 NaN NaN -M12: abs(C1 - C2) NaN NaN -M12: log(C1) NaN NaN -M12: O22:abs(C1 - C2) NaN NaN -M12: O23:abs(C1 - C2) NaN NaN -M12: O24:abs(C1 - C2) NaN NaN -M13: (Intercept) NaN NaN -M13: M22 NaN NaN -M13: M23 NaN NaN -M13: M24 NaN NaN -M13: O22 NaN NaN -M13: O23 NaN NaN -M13: O24 NaN NaN -M13: abs(C1 - C2) NaN NaN -M13: log(C1) NaN NaN -M13: O22:abs(C1 - C2) NaN NaN -M13: O23:abs(C1 - C2) NaN NaN -M13: O24:abs(C1 - C2) NaN NaN -M14: (Intercept) NaN NaN -M14: M22 NaN NaN -M14: M23 NaN NaN -M14: M24 NaN NaN -M14: O22 NaN NaN -M14: O23 NaN NaN -M14: O24 NaN NaN -M14: abs(C1 - C2) NaN NaN -M14: log(C1) NaN NaN -M14: O22:abs(C1 - C2) NaN NaN -M14: O23:abs(C1 - C2) NaN NaN -M14: O24:abs(C1 - C2) NaN NaN - - -$m4b -Potential scale reduction factors: - - Point est. -M12: (Intercept) NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M12: abs(C1 - C2) NaN -M12: log(C1) NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN -M13: (Intercept) NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M13: abs(C1 - C2) NaN -M13: log(C1) NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN -M14: (Intercept) NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M14: abs(C1 - C2) NaN -M14: log(C1) NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN - Upper C.I. -M12: (Intercept) NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M12: abs(C1 - C2) NaN -M12: log(C1) NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN -M13: (Intercept) NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M13: abs(C1 - C2) NaN -M13: log(C1) NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN -M14: (Intercept) NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M14: abs(C1 - C2) NaN -M14: log(C1) NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN - - diff --git a/tests/testthat/testout/mlogit_lapply.models0.MC_error..txt b/tests/testthat/testout/mlogit_lapply.models0.MC_error..txt deleted file mode 100644 index 3e3c2f2e..00000000 --- a/tests/testthat/testout/mlogit_lapply.models0.MC_error..txt +++ /dev/null @@ -1,228 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - est MCSE SD MCSE/SD -M12: (Intercept) 0 0 0 NaN -M13: (Intercept) 0 0 0 NaN -M14: (Intercept) 0 0 0 NaN - -$m0b - est MCSE SD MCSE/SD -M22: (Intercept) 0 0 0 NaN -M23: (Intercept) 0 0 0 NaN -M24: (Intercept) 0 0 0 NaN - -$m1a - est MCSE SD MCSE/SD -M12: (Intercept) 0 0 0 NaN -M12: C1 0 0 0 NaN -M13: (Intercept) 0 0 0 NaN -M13: C1 0 0 0 NaN -M14: (Intercept) 0 0 0 NaN -M14: C1 0 0 0 NaN - -$m1b - est MCSE SD MCSE/SD -M22: (Intercept) 0 0 0 NaN -M22: C1 0 0 0 NaN -M23: (Intercept) 0 0 0 NaN -M23: C1 0 0 0 NaN -M24: (Intercept) 0 0 0 NaN -M24: C1 0 0 0 NaN - -$m2a - est MCSE SD MCSE/SD -M12: (Intercept) 0 0 0 NaN -M12: C2 0 0 0 NaN -M13: (Intercept) 0 0 0 NaN -M13: C2 0 0 0 NaN -M14: (Intercept) 0 0 0 NaN -M14: C2 0 0 0 NaN - -$m2b - est MCSE SD MCSE/SD -M22: (Intercept) 0 0 0 NaN -M22: C2 0 0 0 NaN -M23: (Intercept) 0 0 0 NaN -M23: C2 0 0 0 NaN -M24: (Intercept) 0 0 0 NaN -M24: C2 0 0 0 NaN - -$m3a - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -M12 0 0 0 NaN -M13 0 0 0 NaN -M14 0 0 0 NaN -sigma_C1 0 0 0 NaN - -$m3b - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -M22 0 0 0 NaN -M23 0 0 0 NaN -M24 0 0 0 NaN -sigma_C1 0 0 0 NaN - -$m4a - est MCSE SD MCSE/SD -M12: (Intercept) 0 0 0 NaN -M12: M22 0 0 0 NaN -M12: M23 0 0 0 NaN -M12: M24 0 0 0 NaN -M12: O22 0 0 0 NaN -M12: O23 0 0 0 NaN -M12: O24 0 0 0 NaN -M12: abs(C1 - C2) 0 0 0 NaN -M12: log(C1) 0 0 0 NaN -M12: O22:abs(C1 - C2) 0 0 0 NaN -M12: O23:abs(C1 - C2) 0 0 0 NaN -M12: O24:abs(C1 - C2) 0 0 0 NaN -M13: (Intercept) 0 0 0 NaN -M13: M22 0 0 0 NaN -M13: M23 0 0 0 NaN -M13: M24 0 0 0 NaN -M13: O22 0 0 0 NaN -M13: O23 0 0 0 NaN -M13: O24 0 0 0 NaN -M13: abs(C1 - C2) 0 0 0 NaN -M13: log(C1) 0 0 0 NaN -M13: O22:abs(C1 - C2) 0 0 0 NaN -M13: O23:abs(C1 - C2) 0 0 0 NaN -M13: O24:abs(C1 - C2) 0 0 0 NaN -M14: (Intercept) 0 0 0 NaN -M14: M22 0 0 0 NaN -M14: M23 0 0 0 NaN -M14: M24 0 0 0 NaN -M14: O22 0 0 0 NaN -M14: O23 0 0 0 NaN -M14: O24 0 0 0 NaN -M14: abs(C1 - C2) 0 0 0 NaN -M14: log(C1) 0 0 0 NaN -M14: O22:abs(C1 - C2) 0 0 0 NaN -M14: O23:abs(C1 - C2) 0 0 0 NaN -M14: O24:abs(C1 - C2) 0 0 0 NaN - -$m4b - est MCSE SD -M12: (Intercept) 0 0 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M12: abs(C1 - C2) 0 0 0 -M12: log(C1) 0 0 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 -M13: (Intercept) 0 0 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M13: abs(C1 - C2) 0 0 0 -M13: log(C1) 0 0 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 -M14: (Intercept) 0 0 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M14: abs(C1 - C2) 0 0 0 -M14: log(C1) 0 0 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 - MCSE/SD -M12: (Intercept) NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M12: abs(C1 - C2) NaN -M12: log(C1) NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN -M13: (Intercept) NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M13: abs(C1 - C2) NaN -M13: log(C1) NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN -M14: (Intercept) NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN -M14: abs(C1 - C2) NaN -M14: log(C1) NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN - diff --git a/tests/testthat/testout/mlogit_lapply.models0.coef..txt b/tests/testthat/testout/mlogit_lapply.models0.coef..txt deleted file mode 100644 index f4ab814f..00000000 --- a/tests/testthat/testout/mlogit_lapply.models0.coef..txt +++ /dev/null @@ -1,104 +0,0 @@ -$m0a -$m0a$M1 -(Intercept) (Intercept) (Intercept) - 0 0 0 - - -$m0b -$m0b$M2 -(Intercept) (Intercept) (Intercept) - 0 0 0 - - -$m1a -$m1a$M1 -(Intercept) C1 (Intercept) C1 (Intercept) C1 - 0 0 0 0 0 0 - - -$m1b -$m1b$M2 -(Intercept) C1 (Intercept) C1 (Intercept) C1 - 0 0 0 0 0 0 - - -$m2a -$m2a$M1 -(Intercept) C2 (Intercept) C2 (Intercept) C2 - 0 0 0 0 0 0 - - -$m2b -$m2b$M2 -(Intercept) C2 (Intercept) C2 (Intercept) C2 - 0 0 0 0 0 0 - - -$m3a -$m3a$C1 -(Intercept) M12 M13 M14 sigma_C1 - 0 0 0 0 0 - - -$m3b -$m3b$C1 -(Intercept) M22 M23 M24 sigma_C1 - 0 0 0 0 0 - - -$m4a -$m4a$M1 - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - - -$m4b -$m4b$M1 - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 - - diff --git a/tests/testthat/testout/mlogit_lapply.models0.confint..txt b/tests/testthat/testout/mlogit_lapply.models0.confint..txt deleted file mode 100644 index cac50f75..00000000 --- a/tests/testthat/testout/mlogit_lapply.models0.confint..txt +++ /dev/null @@ -1,141 +0,0 @@ -$m0a -$m0a$M1 - 2.5% 97.5% -(Intercept) 0 0 -(Intercept) 0 0 -(Intercept) 0 0 - - -$m0b -$m0b$M2 - 2.5% 97.5% -(Intercept) 0 0 -(Intercept) 0 0 -(Intercept) 0 0 - - -$m1a -$m1a$M1 - 2.5% 97.5% -(Intercept) 0 0 -C1 0 0 -(Intercept) 0 0 -C1 0 0 -(Intercept) 0 0 -C1 0 0 - - -$m1b -$m1b$M2 - 2.5% 97.5% -(Intercept) 0 0 -C1 0 0 -(Intercept) 0 0 -C1 0 0 -(Intercept) 0 0 -C1 0 0 - - -$m2a -$m2a$M1 - 2.5% 97.5% -(Intercept) 0 0 -C2 0 0 -(Intercept) 0 0 -C2 0 0 -(Intercept) 0 0 -C2 0 0 - - -$m2b -$m2b$M2 - 2.5% 97.5% -(Intercept) 0 0 -C2 0 0 -(Intercept) 0 0 -C2 0 0 -(Intercept) 0 0 -C2 0 0 - - -$m3a -$m3a$C1 - 2.5% 97.5% -(Intercept) 0 0 -M12 0 0 -M13 0 0 -M14 0 0 -sigma_C1 0 0 - - -$m3b -$m3b$C1 - 2.5% 97.5% -(Intercept) 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -sigma_C1 0 0 - - -$m4a -$m4a$M1 - 2.5% 97.5% -(Intercept) 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -O22:abs(C1 - C2) 0 0 -O23:abs(C1 - C2) 0 0 -O24:abs(C1 - C2) 0 0 -(Intercept) 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -O22:abs(C1 - C2) 0 0 -O23:abs(C1 - C2) 0 0 -O24:abs(C1 - C2) 0 0 -(Intercept) 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -O22 0 0 -O23 0 0 -O24 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -O22:abs(C1 - C2) 0 0 -O23:abs(C1 - C2) 0 0 -O24:abs(C1 - C2) 0 0 - - -$m4b -$m4b$M1 - 2.5% 97.5% -(Intercept) 0 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 -(Intercept) 0 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 -(Intercept) 0 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 - - diff --git a/tests/testthat/testout/mlogit_lapply.models0.function.x.coef.txt b/tests/testthat/testout/mlogit_lapply.models0.function.x.coef.txt deleted file mode 100644 index d9b7b13e..00000000 --- a/tests/testthat/testout/mlogit_lapply.models0.function.x.coef.txt +++ /dev/null @@ -1,278 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a -$m0a$M1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M12: (Intercept) 0 0 0 0 0 NaN NaN -M13: (Intercept) 0 0 0 0 0 NaN NaN -M14: (Intercept) 0 0 0 0 0 NaN NaN - - -$m0b -$m0b$M2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22: (Intercept) 0 0 0 0 0 NaN NaN -M23: (Intercept) 0 0 0 0 0 NaN NaN -M24: (Intercept) 0 0 0 0 0 NaN NaN - - -$m1a -$m1a$M1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M12: (Intercept) 0 0 0 0 0 NaN NaN -M12: C1 0 0 0 0 0 NaN NaN -M13: (Intercept) 0 0 0 0 0 NaN NaN -M13: C1 0 0 0 0 0 NaN NaN -M14: (Intercept) 0 0 0 0 0 NaN NaN -M14: C1 0 0 0 0 0 NaN NaN - - -$m1b -$m1b$M2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22: (Intercept) 0 0 0 0 0 NaN NaN -M22: C1 0 0 0 0 0 NaN NaN -M23: (Intercept) 0 0 0 0 0 NaN NaN -M23: C1 0 0 0 0 0 NaN NaN -M24: (Intercept) 0 0 0 0 0 NaN NaN -M24: C1 0 0 0 0 0 NaN NaN - - -$m2a -$m2a$M1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M12: (Intercept) 0 0 0 0 0 NaN NaN -M12: C2 0 0 0 0 0 NaN NaN -M13: (Intercept) 0 0 0 0 0 NaN NaN -M13: C2 0 0 0 0 0 NaN NaN -M14: (Intercept) 0 0 0 0 0 NaN NaN -M14: C2 0 0 0 0 0 NaN NaN - - -$m2b -$m2b$M2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22: (Intercept) 0 0 0 0 0 NaN NaN -M22: C2 0 0 0 0 0 NaN NaN -M23: (Intercept) 0 0 0 0 0 NaN NaN -M23: C2 0 0 0 0 0 NaN NaN -M24: (Intercept) 0 0 0 0 0 NaN NaN -M24: C2 0 0 0 0 0 NaN NaN - - -$m3a -$m3a$C1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -M12 0 0 0 0 0 NaN NaN -M13 0 0 0 0 0 NaN NaN -M14 0 0 0 0 0 NaN NaN - - -$m3b -$m3b$C1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN - - -$m4a -$m4a$M1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M12: (Intercept) 0 0 0 0 0 NaN NaN -M12: M22 0 0 0 0 0 NaN NaN -M12: M23 0 0 0 0 0 NaN NaN -M12: M24 0 0 0 0 0 NaN NaN -M12: O22 0 0 0 0 0 NaN NaN -M12: O23 0 0 0 0 0 NaN NaN -M12: O24 0 0 0 0 0 NaN NaN -M12: abs(C1 - C2) 0 0 0 0 0 NaN NaN -M12: log(C1) 0 0 0 0 0 NaN NaN -M12: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M12: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M12: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M13: (Intercept) 0 0 0 0 0 NaN NaN -M13: M22 0 0 0 0 0 NaN NaN -M13: M23 0 0 0 0 0 NaN NaN -M13: M24 0 0 0 0 0 NaN NaN -M13: O22 0 0 0 0 0 NaN NaN -M13: O23 0 0 0 0 0 NaN NaN -M13: O24 0 0 0 0 0 NaN NaN -M13: abs(C1 - C2) 0 0 0 0 0 NaN NaN -M13: log(C1) 0 0 0 0 0 NaN NaN -M13: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M13: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M13: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M14: (Intercept) 0 0 0 0 0 NaN NaN -M14: M22 0 0 0 0 0 NaN NaN -M14: M23 0 0 0 0 0 NaN NaN -M14: M24 0 0 0 0 0 NaN NaN -M14: O22 0 0 0 0 0 NaN NaN -M14: O23 0 0 0 0 0 NaN NaN -M14: O24 0 0 0 0 0 NaN NaN -M14: abs(C1 - C2) 0 0 0 0 0 NaN NaN -M14: log(C1) 0 0 0 0 0 NaN NaN -M14: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M14: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M14: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN - - -$m4b -$m4b$M1 - Mean SD 2.5% -M12: (Intercept) 0 0 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M12: abs(C1 - C2) 0 0 0 -M12: log(C1) 0 0 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 -M13: (Intercept) 0 0 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M13: abs(C1 - C2) 0 0 0 -M13: log(C1) 0 0 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 -M14: (Intercept) 0 0 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M14: abs(C1 - C2) 0 0 0 -M14: log(C1) 0 0 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 - 97.5% -M12: (Intercept) 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M12: abs(C1 - C2) 0 -M12: log(C1) 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 -M13: (Intercept) 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M13: abs(C1 - C2) 0 -M13: log(C1) 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 -M14: (Intercept) 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M14: abs(C1 - C2) 0 -M14: log(C1) 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 - tail-prob. -M12: (Intercept) 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M12: abs(C1 - C2) 0 -M12: log(C1) 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 -M13: (Intercept) 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M13: abs(C1 - C2) 0 -M13: log(C1) 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 -M14: (Intercept) 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M14: abs(C1 - C2) 0 -M14: log(C1) 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 - GR-crit MCE/SD -M12: (Intercept) NaN NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN -M12: abs(C1 - C2) NaN NaN -M12: log(C1) NaN NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN -M13: (Intercept) NaN NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN -M13: abs(C1 - C2) NaN NaN -M13: log(C1) NaN NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN -M14: (Intercept) NaN NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN -M14: abs(C1 - C2) NaN NaN -M14: log(C1) NaN NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN - - diff --git a/tests/testthat/testout/mlogit_lapply.models0.print..txt b/tests/testthat/testout/mlogit_lapply.models0.print..txt deleted file mode 100644 index 49f998e5..00000000 --- a/tests/testthat/testout/mlogit_lapply.models0.print..txt +++ /dev/null @@ -1,354 +0,0 @@ - -Call: -mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: -(Intercept) (Intercept) (Intercept) - 0 0 0 - -Call: -mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M2" - - -Coefficients: -(Intercept) (Intercept) (Intercept) - 0 0 0 - -Call: -mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: -(Intercept) C1 (Intercept) C1 (Intercept) C1 - 0 0 0 0 0 0 - -Call: -mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M2" - - -Coefficients: -(Intercept) C1 (Intercept) C1 (Intercept) C1 - 0 0 0 0 0 0 - -Call: -mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: -(Intercept) C2 (Intercept) C2 (Intercept) C2 - 0 0 0 0 0 0 - -Call: -mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M2" - - -Coefficients: -(Intercept) C2 (Intercept) C2 (Intercept) C2 - 0 0 0 0 0 0 - -Call: -lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear model for "C1" - - -Coefficients: -(Intercept) M12 M13 M14 - 0 0 0 0 - - -Residual standard deviation: -sigma_C1 - 0 - -Call: -lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear model for "C1" - - -Coefficients: -(Intercept) M22 M23 M24 - 0 0 0 0 - - -Residual standard deviation: -sigma_C1 - 0 - -Call: -mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"), - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - -Call: -mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1), - 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 -$m0a - -Call: -mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: -(Intercept) (Intercept) (Intercept) - 0 0 0 - -$m0b - -Call: -mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M2" - - -Coefficients: -(Intercept) (Intercept) (Intercept) - 0 0 0 - -$m1a - -Call: -mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: -(Intercept) C1 (Intercept) C1 (Intercept) C1 - 0 0 0 0 0 0 - -$m1b - -Call: -mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M2" - - -Coefficients: -(Intercept) C1 (Intercept) C1 (Intercept) C1 - 0 0 0 0 0 0 - -$m2a - -Call: -mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: -(Intercept) C2 (Intercept) C2 (Intercept) C2 - 0 0 0 0 0 0 - -$m2b - -Call: -mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M2" - - -Coefficients: -(Intercept) C2 (Intercept) C2 (Intercept) C2 - 0 0 0 0 0 0 - -$m3a - -Call: -lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear model for "C1" - - -Coefficients: -(Intercept) M12 M13 M14 - 0 0 0 0 - - -Residual standard deviation: -sigma_C1 - 0 - -$m3b - -Call: -lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear model for "C1" - - -Coefficients: -(Intercept) M22 M23 M24 - 0 0 0 0 - - -Residual standard deviation: -sigma_C1 - 0 - -$m4a - -Call: -mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"), - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - (Intercept) M22 M23 M24 - 0 0 0 0 - O22 O23 O24 abs(C1 - C2) - 0 0 0 0 - log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) - 0 0 0 0 - -$m4b - -Call: -mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1), - 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian multinomial logit model for "M1" - - -Coefficients: - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 - (Intercept) - 0 - ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 -ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) - 0 - diff --git a/tests/testthat/testout/mlogit_lapply.models0.summary..txt b/tests/testthat/testout/mlogit_lapply.models0.summary..txt deleted file mode 100644 index dc4fa9b4..00000000 --- a/tests/testthat/testout/mlogit_lapply.models0.summary..txt +++ /dev/null @@ -1,449 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - -Bayesian multinomial logit model fitted with JointAI - -Call: -mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M12: (Intercept) 0 0 0 0 0 NaN NaN -M13: (Intercept) 0 0 0 0 0 NaN NaN -M14: (Intercept) 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m0b - -Bayesian multinomial logit model fitted with JointAI - -Call: -mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22: (Intercept) 0 0 0 0 0 NaN NaN -M23: (Intercept) 0 0 0 0 0 NaN NaN -M24: (Intercept) 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m1a - -Bayesian multinomial logit model fitted with JointAI - -Call: -mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M12: (Intercept) 0 0 0 0 0 NaN NaN -M12: C1 0 0 0 0 0 NaN NaN -M13: (Intercept) 0 0 0 0 0 NaN NaN -M13: C1 0 0 0 0 0 NaN NaN -M14: (Intercept) 0 0 0 0 0 NaN NaN -M14: C1 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m1b - -Bayesian multinomial logit model fitted with JointAI - -Call: -mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22: (Intercept) 0 0 0 0 0 NaN NaN -M22: C1 0 0 0 0 0 NaN NaN -M23: (Intercept) 0 0 0 0 0 NaN NaN -M23: C1 0 0 0 0 0 NaN NaN -M24: (Intercept) 0 0 0 0 0 NaN NaN -M24: C1 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m2a - -Bayesian multinomial logit model fitted with JointAI - -Call: -mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M12: (Intercept) 0 0 0 0 0 NaN NaN -M12: C2 0 0 0 0 0 NaN NaN -M13: (Intercept) 0 0 0 0 0 NaN NaN -M13: C2 0 0 0 0 0 NaN NaN -M14: (Intercept) 0 0 0 0 0 NaN NaN -M14: C2 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m2b - -Bayesian multinomial logit model fitted with JointAI - -Call: -mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M22: (Intercept) 0 0 0 0 0 NaN NaN -M22: C2 0 0 0 0 0 NaN NaN -M23: (Intercept) 0 0 0 0 0 NaN NaN -M23: C2 0 0 0 0 0 NaN NaN -M24: (Intercept) 0 0 0 0 0 NaN NaN -M24: C2 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m3a - -Bayesian linear model fitted with JointAI - -Call: -lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -M12 0 0 0 0 0 NaN NaN -M13 0 0 0 0 0 NaN NaN -M14 0 0 0 0 0 NaN NaN - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_C1 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 1:10 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m3b - -Bayesian linear model fitted with JointAI - -Call: -lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -M22 0 0 0 0 0 NaN NaN -M23 0 0 0 0 0 NaN NaN -M24 0 0 0 0 0 NaN NaN - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_C1 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m4a - -Bayesian multinomial logit model fitted with JointAI - -Call: -mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, - n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"), - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -M12: (Intercept) 0 0 0 0 0 NaN NaN -M12: M22 0 0 0 0 0 NaN NaN -M12: M23 0 0 0 0 0 NaN NaN -M12: M24 0 0 0 0 0 NaN NaN -M12: O22 0 0 0 0 0 NaN NaN -M12: O23 0 0 0 0 0 NaN NaN -M12: O24 0 0 0 0 0 NaN NaN -M12: abs(C1 - C2) 0 0 0 0 0 NaN NaN -M12: log(C1) 0 0 0 0 0 NaN NaN -M12: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M12: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M12: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M13: (Intercept) 0 0 0 0 0 NaN NaN -M13: M22 0 0 0 0 0 NaN NaN -M13: M23 0 0 0 0 0 NaN NaN -M13: M24 0 0 0 0 0 NaN NaN -M13: O22 0 0 0 0 0 NaN NaN -M13: O23 0 0 0 0 0 NaN NaN -M13: O24 0 0 0 0 0 NaN NaN -M13: abs(C1 - C2) 0 0 0 0 0 NaN NaN -M13: log(C1) 0 0 0 0 0 NaN NaN -M13: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M13: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M13: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M14: (Intercept) 0 0 0 0 0 NaN NaN -M14: M22 0 0 0 0 0 NaN NaN -M14: M23 0 0 0 0 0 NaN NaN -M14: M24 0 0 0 0 0 NaN NaN -M14: O22 0 0 0 0 0 NaN NaN -M14: O23 0 0 0 0 0 NaN NaN -M14: O24 0 0 0 0 0 NaN NaN -M14: abs(C1 - C2) 0 0 0 0 0 NaN NaN -M14: log(C1) 0 0 0 0 0 NaN NaN -M14: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M14: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN -M14: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 100 - -$m4b - -Bayesian multinomial logit model fitted with JointAI - -Call: -mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1), - 1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, - n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020, - warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% -M12: (Intercept) 0 0 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M12: abs(C1 - C2) 0 0 0 -M12: log(C1) 0 0 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 -M13: (Intercept) 0 0 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M13: abs(C1 - C2) 0 0 0 -M13: log(C1) 0 0 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 -M14: (Intercept) 0 0 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0 -M14: abs(C1 - C2) 0 0 0 -M14: log(C1) 0 0 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0 - 97.5% -M12: (Intercept) 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M12: abs(C1 - C2) 0 -M12: log(C1) 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 -M13: (Intercept) 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M13: abs(C1 - C2) 0 -M13: log(C1) 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 -M14: (Intercept) 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M14: abs(C1 - C2) 0 -M14: log(C1) 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 - tail-prob. -M12: (Intercept) 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M12: abs(C1 - C2) 0 -M12: log(C1) 0 -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 -M13: (Intercept) 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M13: abs(C1 - C2) 0 -M13: log(C1) 0 -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 -M14: (Intercept) 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 -M14: abs(C1 - C2) 0 -M14: log(C1) 0 -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 - GR-crit MCE/SD -M12: (Intercept) NaN NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN -M12: abs(C1 - C2) NaN NaN -M12: log(C1) NaN NaN -M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN -M13: (Intercept) NaN NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN -M13: abs(C1 - C2) NaN NaN -M13: log(C1) NaN NaN -M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN -M14: (Intercept) NaN NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN -M14: abs(C1 - C2) NaN NaN -M14: log(C1) NaN NaN -M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 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zOKCyV-iP+8K2hM^NJfq{B7-VcEQUFrSzVzd4QzsI+nLST7bf)IH&Z!)%T#~|J;Q_a zZ!yxg#nc+4BX-CU0hTOH`6gA$3y+)&>mYy`Foc? w7M2CLdG_agDzSz2qlQYBKU`ZVzUEgvr)l5lC%0TKWv*`}+&@dHaFyHi50V>dxc~qF diff --git a/tests/testthat/testout/mlogitmm_lapply.models.jagsmodel..txt b/tests/testthat/testout/mlogitmm_lapply.models.jagsmodel..txt deleted file mode 100644 index 9a3b0fbc..00000000 --- a/tests/testthat/testout/mlogitmm_lapply.models.jagsmodel..txt +++ /dev/null @@ -1,1022 +0,0 @@ -$m0a -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - } -$m0b -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - } -$m1a -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 1] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - } -$m1b -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 1] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - } -$m1c -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - } -$m1d -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - } -$m2a -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 2] + - beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } -$m2b -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 2] + - beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } -$m2c -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } -$m2d -model { - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - beta[2] * M_id[group_id[i], 1] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } -$m3a -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 2] + - beta[3] * M_lvlone[i, 3] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - } -$m3b -model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + beta[2] * M_lvlone[i, 3] + - beta[3] * M_lvlone[i, 4] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - alpha[1] * M_id[group_id[i], 1] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - alpha[2] * M_id[group_id[i], 1] - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:2) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - } -$m4a -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 3] + - beta[2] * M_id[group_id[i], 4] + - beta[3] * M_id[group_id[i], 5] + - beta[4] * M_id[group_id[i], 6] + - beta[5] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] + - beta[6] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + - beta[13] * M_lvlone[i, 3] + beta[14] * M_lvlone[i, 4] + - beta[15] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[16] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[7] * M_id[group_id[i], 3] + - beta[8] * M_id[group_id[i], 4] + - beta[9] * M_id[group_id[i], 5] + - beta[10] * M_id[group_id[i], 6] + - beta[11] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] + - beta[12] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + - beta[17] * M_lvlone[i, 3] + beta[18] * M_lvlone[i, 4] + - beta[19] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[20] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:20) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - alpha[1] * M_id[group_id[i], 3] + - alpha[2] * M_id[group_id[i], 4] + - alpha[3] * M_id[group_id[i], 5] + - alpha[4] * M_id[group_id[i], 6] + - alpha[5] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] + - alpha[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - alpha[7] * M_id[group_id[i], 3] + - alpha[8] * M_id[group_id[i], 4] + - alpha[9] * M_id[group_id[i], 5] + - alpha[10] * M_id[group_id[i], 6] + - alpha[11] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] + - alpha[12] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:12) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 3] * alpha[13] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[14] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[15] - log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[16] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[17] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[18] - log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[19] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[20] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[21] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 13:21) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 3] * alpha[22] + - (M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[23] - - M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2]) - - - } - - # Priors for the model for C2 - for (k in 22:23) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_lvlone[i, 3] * M_id[group_id[i], 7] - M_lvlone[i, 6] <- M_lvlone[i, 4] * M_id[group_id[i], 7] - } - - } -$m4b -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[4] * M_id[group_id[i], 2] + - beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[6] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] + - beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - - - # Multinomial logit mixed model for m2 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3]) - - p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ]))) - p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ]))) - p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ]))) - - log(phi_m2[i, 1]) <- 0 - log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] + - alpha[1] * M_id[group_id[i], 2] + - alpha[2] * M_id[group_id[i], 5] + - alpha[3] * M_id[group_id[i], 6] + - alpha[4] * M_id[group_id[i], 7] + - alpha[5] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + - alpha[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] + - alpha[7] * M_id[group_id[i], 2] + - alpha[8] * M_id[group_id[i], 5] + - alpha[9] * M_id[group_id[i], 6] + - alpha[10] * M_id[group_id[i], 7] + - alpha[11] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] + - alpha[12] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0) - - - - M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0) - - } - - for (ii in 1:100) { - b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ]) - mu_b_m2_id[ii, 1] <- 0 - } - - - - # Priors for the model for m2 - for (k in 1:12) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[13] + M_id[ii, 5] * alpha[14] + - M_id[ii, 6] * alpha[15] + M_id[ii, 7] * alpha[16] + - (M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[17] - - M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1]) - - - } - - # Priors for the model for C2 - for (k in 13:17) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3] - } - - } -$m4c -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[3] * M_id[group_id[i], 4] + - beta[7] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[4] * M_id[group_id[i], 2] + - beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[6] * M_id[group_id[i], 4] + - beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[10] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:4] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - mu_b_m1_id[ii, 2] <- 0 - mu_b_m1_id[ii, 3] <- 0 - mu_b_m1_id[ii, 4] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - for (k in 1:4) { - RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_m1_id[1:4, 1:4] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id) - D_m1_id[1:4, 1:4] <- inverse(invD_m1_id[ , ]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for time - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - M_id[ii, 4] * alpha[7] - } - - # Priors for the model for c1 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - } -$m4d -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[1] * M_id[group_id[i], 1] + - beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[7] * M_lvlone[i, 5] + - beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + - beta[10] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[3] * M_id[group_id[i], 1] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[12] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[13] * M_lvlone[i, 5] + - beta[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[15] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] + - beta[16] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:2] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - mu_b_m1_id[ii, 2] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:16) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - for (k in 1:2) { - RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_m1_id[1:2, 1:2] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id) - D_m1_id[1:2, 1:2] <- inverse(invD_m1_id[ , ]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] - } - - # Priors for the model for b2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6] - } - - } -$m4e -model { - - # Multinomial logit mixed model for m1 ------------------------------------------ - for (i in 1:329) { - M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3]) - - p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ]))) - p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ]))) - p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ]))) - - log(phi_m1[i, 1]) <- 0 - log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] + - beta[1] * M_id[group_id[i], 1] + - beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[5] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[6] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] + - beta[3] * M_id[group_id[i], 1] + - beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] + - beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ]) - mu_b_m1_id[ii, 1] <- 0 - } - - - - # Priors for the model for m1 - for (k in 1:10) { - beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial_ridge_beta[k]) - tau_reg_multinomial_ridge_beta[k] ~ dgamma(0.01, 0.01) - } - - invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1]) - } diff --git a/tests/testthat/testout/mlogitmm_lapply.models0.GR_crit.multiva.txt b/tests/testthat/testout/mlogitmm_lapply.models0.GR_crit.multiva.txt deleted file mode 100644 index 2bfa15cc..00000000 --- a/tests/testthat/testout/mlogitmm_lapply.models0.GR_crit.multiva.txt +++ /dev/null @@ -1,252 +0,0 @@ -$m0a -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1C: (Intercept) NaN NaN -D_m1_id[1,1] NaN NaN - - -$m0b -Potential scale reduction factors: - - Point est. Upper C.I. -m2B: (Intercept) NaN NaN -m2C: (Intercept) NaN NaN -D_m2_id[1,1] NaN NaN - - -$m1a -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1B: C1 NaN NaN -m1C: (Intercept) NaN NaN -m1C: C1 NaN NaN -D_m1_id[1,1] NaN NaN - - -$m1b -Potential scale reduction factors: - - Point est. Upper C.I. -m2B: (Intercept) NaN NaN -m2B: C1 NaN NaN -m2C: (Intercept) NaN NaN -m2C: C1 NaN NaN -D_m2_id[1,1] NaN NaN - - -$m1c -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1C: (Intercept) NaN NaN -m1B: c1 NaN NaN -m1C: c1 NaN NaN -D_m1_id[1,1] NaN NaN - - -$m1d -Potential scale reduction factors: - - Point est. Upper C.I. -m2B: (Intercept) NaN NaN -m2C: (Intercept) NaN NaN -m2B: c1 NaN NaN -m2C: c1 NaN NaN -D_m2_id[1,1] NaN NaN - - -$m2a -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1B: C2 NaN NaN -m1C: (Intercept) NaN NaN -m1C: C2 NaN NaN -D_m1_id[1,1] NaN NaN - - -$m2b -Potential scale reduction factors: - - Point est. Upper C.I. -m2B: (Intercept) NaN NaN -m2B: C2 NaN NaN -m2C: (Intercept) NaN NaN -m2C: C2 NaN NaN -D_m2_id[1,1] NaN NaN - - -$m2c -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1C: (Intercept) NaN NaN -m1B: c2 NaN NaN -m1C: c2 NaN NaN -D_m1_id[1,1] NaN NaN - - -$m2d -Potential scale reduction factors: - - Point est. Upper C.I. -m2B: (Intercept) NaN NaN -m2C: (Intercept) NaN NaN -m2B: c2 NaN NaN -m2C: c2 NaN NaN -D_m2_id[1,1] NaN NaN - - -$m3a -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -m1B NaN NaN -m1C NaN NaN -sigma_c1 NaN NaN -D_c1_id[1,1] NaN NaN - - -$m3b -Potential scale reduction factors: - - Point est. Upper C.I. -(Intercept) NaN NaN -m2B NaN NaN -m2C NaN NaN -sigma_c1 NaN NaN -D_c1_id[1,1] NaN NaN - - -$m4a -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1B: M22 NaN NaN -m1B: M23 NaN NaN -m1B: M24 NaN NaN -m1B: abs(C1 - C2) NaN NaN -m1B: log(C1) NaN NaN -m1C: (Intercept) NaN NaN -m1C: M22 NaN NaN -m1C: M23 NaN NaN -m1C: M24 NaN NaN -m1C: abs(C1 - C2) NaN NaN -m1C: log(C1) NaN NaN -m1B: m2B NaN NaN -m1B: m2C NaN NaN -m1B: m2B:abs(C1 - C2) NaN NaN -m1B: m2C:abs(C1 - C2) NaN NaN -m1C: m2B NaN NaN -m1C: m2C NaN NaN -m1C: m2B:abs(C1 - C2) NaN NaN -m1C: m2C:abs(C1 - C2) NaN NaN -D_m1_id[1,1] NaN NaN - - -$m4b -Potential scale reduction factors: - - Point est. -m1B: (Intercept) NaN -m1B: abs(C1 - C2) NaN -m1B: log(C1) NaN -m1C: (Intercept) NaN -m1C: abs(C1 - C2) NaN -m1C: log(C1) NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -D_m1_id[1,1] NaN - Upper C.I. -m1B: (Intercept) NaN -m1B: abs(C1 - C2) NaN -m1B: log(C1) NaN -m1C: (Intercept) NaN -m1C: abs(C1 - C2) NaN -m1C: log(C1) NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -D_m1_id[1,1] NaN - - -$m4c -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1B: C1 NaN NaN -m1B: B21 NaN NaN -m1C: (Intercept) NaN NaN -m1C: C1 NaN NaN -m1C: B21 NaN NaN -m1B: time NaN NaN -m1B: c1 NaN NaN -m1C: time NaN NaN -m1C: c1 NaN NaN -D_m1_id[1,1] NaN NaN -D_m1_id[1,2] NaN NaN -D_m1_id[2,2] NaN NaN -D_m1_id[1,3] NaN NaN -D_m1_id[2,3] NaN NaN -D_m1_id[3,3] NaN NaN -D_m1_id[1,4] NaN NaN -D_m1_id[2,4] NaN NaN -D_m1_id[3,4] NaN NaN -D_m1_id[4,4] NaN NaN - - -$m4d -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1B: C1 NaN NaN -m1C: (Intercept) NaN NaN -m1C: C1 NaN NaN -m1B: time NaN NaN -m1B: I(time^2) NaN NaN -m1B: b21 NaN NaN -m1B: c1 NaN NaN -m1B: C1:time NaN NaN -m1B: b21:c1 NaN NaN -m1C: time NaN NaN -m1C: I(time^2) NaN NaN -m1C: b21 NaN NaN -m1C: c1 NaN NaN -m1C: C1:time NaN NaN -m1C: b21:c1 NaN NaN -D_m1_id[1,1] NaN NaN -D_m1_id[1,2] NaN NaN -D_m1_id[2,2] NaN NaN - - -$m4e -Potential scale reduction factors: - - Point est. Upper C.I. -m1B: (Intercept) NaN NaN -m1B: C1 NaN NaN -m1C: (Intercept) NaN NaN -m1C: C1 NaN NaN -m1B: log(time) NaN NaN -m1B: I(time^2) NaN NaN -m1B: p1 NaN NaN -m1C: log(time) NaN NaN -m1C: I(time^2) NaN NaN -m1C: p1 NaN NaN -D_m1_id[1,1] NaN NaN - - diff --git a/tests/testthat/testout/mlogitmm_lapply.models0.MC_error..txt b/tests/testthat/testout/mlogitmm_lapply.models0.MC_error..txt deleted file mode 100644 index af9e7a17..00000000 --- a/tests/testthat/testout/mlogitmm_lapply.models0.MC_error..txt +++ /dev/null @@ -1,339 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN - -$m0b - est MCSE SD MCSE/SD -m2B: (Intercept) 0 0 0 NaN -m2C: (Intercept) 0 0 0 NaN -D_m2_id[1,1] 0 0 0 NaN - -$m1a - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1B: C1 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -m1C: C1 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN - -$m1b - est MCSE SD MCSE/SD -m2B: (Intercept) 0 0 0 NaN -m2B: C1 0 0 0 NaN -m2C: (Intercept) 0 0 0 NaN -m2C: C1 0 0 0 NaN -D_m2_id[1,1] 0 0 0 NaN - -$m1c - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -m1B: c1 0 0 0 NaN -m1C: c1 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN - -$m1d - est MCSE SD MCSE/SD -m2B: (Intercept) 0 0 0 NaN -m2C: (Intercept) 0 0 0 NaN -m2B: c1 0 0 0 NaN -m2C: c1 0 0 0 NaN -D_m2_id[1,1] 0 0 0 NaN - -$m2a - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1B: C2 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -m1C: C2 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN - -$m2b - est MCSE SD MCSE/SD -m2B: (Intercept) 0 0 0 NaN -m2B: C2 0 0 0 NaN -m2C: (Intercept) 0 0 0 NaN -m2C: C2 0 0 0 NaN -D_m2_id[1,1] 0 0 0 NaN - -$m2c - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -m1B: c2 0 0 0 NaN -m1C: c2 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN - -$m2d - est MCSE SD MCSE/SD -m2B: (Intercept) 0 0 0 NaN -m2C: (Intercept) 0 0 0 NaN -m2B: c2 0 0 0 NaN -m2C: c2 0 0 0 NaN -D_m2_id[1,1] 0 0 0 NaN - -$m3a - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -m1B 0 0 0 NaN -m1C 0 0 0 NaN -sigma_c1 0 0 0 NaN -D_c1_id[1,1] 0 0 0 NaN - -$m3b - est MCSE SD MCSE/SD -(Intercept) 0 0 0 NaN -m2B 0 0 0 NaN -m2C 0 0 0 NaN -sigma_c1 0 0 0 NaN -D_c1_id[1,1] 0 0 0 NaN - -$m4a - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1B: M22 0 0 0 NaN -m1B: M23 0 0 0 NaN -m1B: M24 0 0 0 NaN -m1B: abs(C1 - C2) 0 0 0 NaN -m1B: log(C1) 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -m1C: M22 0 0 0 NaN -m1C: M23 0 0 0 NaN -m1C: M24 0 0 0 NaN -m1C: abs(C1 - C2) 0 0 0 NaN -m1C: log(C1) 0 0 0 NaN -m1B: m2B 0 0 0 NaN -m1B: m2C 0 0 0 NaN -m1B: m2B:abs(C1 - C2) 0 0 0 NaN -m1B: m2C:abs(C1 - C2) 0 0 0 NaN -m1C: m2B 0 0 0 NaN -m1C: m2C 0 0 0 NaN -m1C: m2B:abs(C1 - C2) 0 0 0 NaN -m1C: m2C:abs(C1 - C2) 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN - -$m4b - est MCSE SD -m1B: (Intercept) 0 0 0 -m1B: abs(C1 - C2) 0 0 0 -m1B: log(C1) 0 0 0 -m1C: (Intercept) 0 0 0 -m1C: abs(C1 - C2) 0 0 0 -m1C: log(C1) 0 0 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 -D_m1_id[1,1] 0 0 0 - MCSE/SD -m1B: (Intercept) NaN -m1B: abs(C1 - C2) NaN -m1B: log(C1) NaN -m1C: (Intercept) NaN -m1C: abs(C1 - C2) NaN -m1C: log(C1) NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN -D_m1_id[1,1] NaN - -$m4c - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1B: C1 0 0 0 NaN -m1B: B21 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -m1C: C1 0 0 0 NaN -m1C: B21 0 0 0 NaN -m1B: time 0 0 0 NaN -m1B: c1 0 0 0 NaN -m1C: time 0 0 0 NaN -m1C: c1 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN -D_m1_id[1,2] 0 0 0 NaN -D_m1_id[2,2] 0 0 0 NaN -D_m1_id[1,3] 0 0 0 NaN -D_m1_id[2,3] 0 0 0 NaN -D_m1_id[3,3] 0 0 0 NaN -D_m1_id[1,4] 0 0 0 NaN -D_m1_id[2,4] 0 0 0 NaN -D_m1_id[3,4] 0 0 0 NaN -D_m1_id[4,4] 0 0 0 NaN - -$m4d - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1B: C1 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -m1C: C1 0 0 0 NaN -m1B: time 0 0 0 NaN -m1B: I(time^2) 0 0 0 NaN -m1B: b21 0 0 0 NaN -m1B: c1 0 0 0 NaN -m1B: C1:time 0 0 0 NaN -m1B: b21:c1 0 0 0 NaN -m1C: time 0 0 0 NaN -m1C: I(time^2) 0 0 0 NaN -m1C: b21 0 0 0 NaN -m1C: c1 0 0 0 NaN -m1C: C1:time 0 0 0 NaN -m1C: b21:c1 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN -D_m1_id[1,2] 0 0 0 NaN -D_m1_id[2,2] 0 0 0 NaN - -$m4e - est MCSE SD MCSE/SD -m1B: (Intercept) 0 0 0 NaN -m1B: C1 0 0 0 NaN -m1C: (Intercept) 0 0 0 NaN -m1C: C1 0 0 0 NaN -m1B: log(time) 0 0 0 NaN -m1B: I(time^2) 0 0 0 NaN -m1B: p1 0 0 0 NaN -m1C: log(time) 0 0 0 NaN -m1C: I(time^2) 0 0 0 NaN -m1C: p1 0 0 0 NaN -D_m1_id[1,1] 0 0 0 NaN - diff --git a/tests/testthat/testout/mlogitmm_lapply.models0.coef..txt b/tests/testthat/testout/mlogitmm_lapply.models0.coef..txt deleted file mode 100644 index b8c4536e..00000000 --- a/tests/testthat/testout/mlogitmm_lapply.models0.coef..txt +++ /dev/null @@ -1,146 +0,0 @@ -$m0a -$m0a$m1 - (Intercept) (Intercept) D_m1_id[1,1] - 0 0 0 - - -$m0b -$m0b$m2 - (Intercept) (Intercept) D_m2_id[1,1] - 0 0 0 - - -$m1a -$m1a$m1 - (Intercept) C1 (Intercept) C1 D_m1_id[1,1] - 0 0 0 0 0 - - -$m1b -$m1b$m2 - (Intercept) C1 (Intercept) C1 D_m2_id[1,1] - 0 0 0 0 0 - - -$m1c -$m1c$m1 - (Intercept) (Intercept) c1 c1 D_m1_id[1,1] - 0 0 0 0 0 - - -$m1d -$m1d$m2 - (Intercept) (Intercept) c1 c1 D_m2_id[1,1] - 0 0 0 0 0 - - -$m2a -$m2a$m1 - (Intercept) C2 (Intercept) C2 D_m1_id[1,1] - 0 0 0 0 0 - - -$m2b -$m2b$m2 - (Intercept) C2 (Intercept) C2 D_m2_id[1,1] - 0 0 0 0 0 - - -$m2c -$m2c$m1 - (Intercept) (Intercept) c2 c2 D_m1_id[1,1] - 0 0 0 0 0 - - -$m2d -$m2d$m2 - (Intercept) (Intercept) c2 c2 D_m2_id[1,1] - 0 0 0 0 0 - - -$m3a -$m3a$c1 - (Intercept) m1B m1C sigma_c1 D_c1_id[1,1] - 0 0 0 0 0 - - -$m3b -$m3b$c1 - (Intercept) m2B m2C sigma_c1 D_c1_id[1,1] - 0 0 0 0 0 - - -$m4a -$m4a$m1 - (Intercept) M22 M23 M24 - 0 0 0 0 - abs(C1 - C2) log(C1) (Intercept) M22 - 0 0 0 0 - M23 M24 abs(C1 - C2) log(C1) - 0 0 0 0 - m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) - 0 0 0 0 - m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) - 0 0 0 0 - D_m1_id[1,1] - 0 - - -$m4b -$m4b$m1 - (Intercept) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 - (Intercept) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 - ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - D_m1_id[1,1] - 0 - - -$m4c -$m4c$m1 - (Intercept) C1 B21 (Intercept) C1 B21 - 0 0 0 0 0 0 - time c1 time c1 D_m1_id[1,1] D_m1_id[1,2] - 0 0 0 0 0 0 -D_m1_id[2,2] D_m1_id[1,3] D_m1_id[2,3] D_m1_id[3,3] D_m1_id[1,4] D_m1_id[2,4] - 0 0 0 0 0 0 -D_m1_id[3,4] D_m1_id[4,4] - 0 0 - - -$m4d -$m4d$m1 - (Intercept) C1 (Intercept) C1 time I(time^2) - 0 0 0 0 0 0 - b21 c1 C1:time b21:c1 time I(time^2) - 0 0 0 0 0 0 - b21 c1 C1:time b21:c1 D_m1_id[1,1] D_m1_id[1,2] - 0 0 0 0 0 0 -D_m1_id[2,2] - 0 - - -$m4e -$m4e$m1 - (Intercept) C1 (Intercept) C1 log(time) I(time^2) - 0 0 0 0 0 0 - p1 log(time) I(time^2) p1 D_m1_id[1,1] - 0 0 0 0 0 - - diff --git a/tests/testthat/testout/mlogitmm_lapply.models0.confint..txt b/tests/testthat/testout/mlogitmm_lapply.models0.confint..txt deleted file mode 100644 index b23ab1ac..00000000 --- a/tests/testthat/testout/mlogitmm_lapply.models0.confint..txt +++ /dev/null @@ -1,223 +0,0 @@ -$m0a -$m0a$m1 - 2.5% 97.5% -(Intercept) 0 0 -(Intercept) 0 0 -D_m1_id[1,1] 0 0 - - -$m0b -$m0b$m2 - 2.5% 97.5% -(Intercept) 0 0 -(Intercept) 0 0 -D_m2_id[1,1] 0 0 - - -$m1a -$m1a$m1 - 2.5% 97.5% -(Intercept) 0 0 -C1 0 0 -(Intercept) 0 0 -C1 0 0 -D_m1_id[1,1] 0 0 - - -$m1b -$m1b$m2 - 2.5% 97.5% -(Intercept) 0 0 -C1 0 0 -(Intercept) 0 0 -C1 0 0 -D_m2_id[1,1] 0 0 - - -$m1c -$m1c$m1 - 2.5% 97.5% -(Intercept) 0 0 -(Intercept) 0 0 -c1 0 0 -c1 0 0 -D_m1_id[1,1] 0 0 - - -$m1d -$m1d$m2 - 2.5% 97.5% -(Intercept) 0 0 -(Intercept) 0 0 -c1 0 0 -c1 0 0 -D_m2_id[1,1] 0 0 - - -$m2a -$m2a$m1 - 2.5% 97.5% -(Intercept) 0 0 -C2 0 0 -(Intercept) 0 0 -C2 0 0 -D_m1_id[1,1] 0 0 - - -$m2b -$m2b$m2 - 2.5% 97.5% -(Intercept) 0 0 -C2 0 0 -(Intercept) 0 0 -C2 0 0 -D_m2_id[1,1] 0 0 - - -$m2c -$m2c$m1 - 2.5% 97.5% -(Intercept) 0 0 -(Intercept) 0 0 -c2 0 0 -c2 0 0 -D_m1_id[1,1] 0 0 - - -$m2d -$m2d$m2 - 2.5% 97.5% -(Intercept) 0 0 -(Intercept) 0 0 -c2 0 0 -c2 0 0 -D_m2_id[1,1] 0 0 - - -$m3a -$m3a$c1 - 2.5% 97.5% -(Intercept) 0 0 -m1B 0 0 -m1C 0 0 -sigma_c1 0 0 -D_c1_id[1,1] 0 0 - - -$m3b -$m3b$c1 - 2.5% 97.5% -(Intercept) 0 0 -m2B 0 0 -m2C 0 0 -sigma_c1 0 0 -D_c1_id[1,1] 0 0 - - -$m4a -$m4a$m1 - 2.5% 97.5% -(Intercept) 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -(Intercept) 0 0 -M22 0 0 -M23 0 0 -M24 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -m2B 0 0 -m2C 0 0 -m2B:abs(C1 - C2) 0 0 -m2C:abs(C1 - C2) 0 0 -m2B 0 0 -m2C 0 0 -m2B:abs(C1 - C2) 0 0 -m2C:abs(C1 - C2) 0 0 -D_m1_id[1,1] 0 0 - - -$m4b -$m4b$m1 - 2.5% 97.5% -(Intercept) 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -(Intercept) 0 0 -abs(C1 - C2) 0 0 -log(C1) 0 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 -D_m1_id[1,1] 0 0 - - -$m4c -$m4c$m1 - 2.5% 97.5% -(Intercept) 0 0 -C1 0 0 -B21 0 0 -(Intercept) 0 0 -C1 0 0 -B21 0 0 -time 0 0 -c1 0 0 -time 0 0 -c1 0 0 -D_m1_id[1,1] 0 0 -D_m1_id[1,2] 0 0 -D_m1_id[2,2] 0 0 -D_m1_id[1,3] 0 0 -D_m1_id[2,3] 0 0 -D_m1_id[3,3] 0 0 -D_m1_id[1,4] 0 0 -D_m1_id[2,4] 0 0 -D_m1_id[3,4] 0 0 -D_m1_id[4,4] 0 0 - - -$m4d -$m4d$m1 - 2.5% 97.5% -(Intercept) 0 0 -C1 0 0 -(Intercept) 0 0 -C1 0 0 -time 0 0 -I(time^2) 0 0 -b21 0 0 -c1 0 0 -C1:time 0 0 -b21:c1 0 0 -time 0 0 -I(time^2) 0 0 -b21 0 0 -c1 0 0 -C1:time 0 0 -b21:c1 0 0 -D_m1_id[1,1] 0 0 -D_m1_id[1,2] 0 0 -D_m1_id[2,2] 0 0 - - -$m4e -$m4e$m1 - 2.5% 97.5% -(Intercept) 0 0 -C1 0 0 -(Intercept) 0 0 -C1 0 0 -log(time) 0 0 -I(time^2) 0 0 -p1 0 0 -log(time) 0 0 -I(time^2) 0 0 -p1 0 0 -D_m1_id[1,1] 0 0 - - diff --git a/tests/testthat/testout/mlogitmm_lapply.models0.function.x.coef.txt b/tests/testthat/testout/mlogitmm_lapply.models0.function.x.coef.txt deleted file mode 100644 index 1d98682e..00000000 --- a/tests/testthat/testout/mlogitmm_lapply.models0.function.x.coef.txt +++ /dev/null @@ -1,364 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a -$m0a$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN - - -$m0b -$m0b$m2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN - - -$m1a -$m1a$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C1 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C1 0 0 0 0 0 NaN NaN - - -$m1b -$m1b$m2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2B: C1 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN -m2C: C1 0 0 0 0 0 NaN NaN - - -$m1c -$m1c$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1B: c1 0 0 0 0 0 NaN NaN -m1C: c1 0 0 0 0 0 NaN NaN - - -$m1d -$m1d$m2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN -m2B: c1 0 0 0 0 0 NaN NaN -m2C: c1 0 0 0 0 0 NaN NaN - - -$m2a -$m2a$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C2 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C2 0 0 0 0 0 NaN NaN - - -$m2b -$m2b$m2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2B: C2 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN -m2C: C2 0 0 0 0 0 NaN NaN - - -$m2c -$m2c$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1B: c2 0 0 0 0 0 NaN NaN -m1C: c2 0 0 0 0 0 NaN NaN - - -$m2d -$m2d$m2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN -m2B: c2 0 0 0 0 0 NaN NaN -m2C: c2 0 0 0 0 0 NaN NaN - - -$m3a -$m3a$c1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -m1B 0 0 0 0 0 NaN NaN -m1C 0 0 0 0 0 NaN NaN - - -$m3b -$m3b$c1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -m2B 0 0 0 0 0 NaN NaN -m2C 0 0 0 0 0 NaN NaN - - -$m4a -$m4a$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: M22 0 0 0 0 0 NaN NaN -m1B: M23 0 0 0 0 0 NaN NaN -m1B: M24 0 0 0 0 0 NaN NaN -m1B: abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1B: log(C1) 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: M22 0 0 0 0 0 NaN NaN -m1C: M23 0 0 0 0 0 NaN NaN -m1C: M24 0 0 0 0 0 NaN NaN -m1C: abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1C: log(C1) 0 0 0 0 0 NaN NaN -m1B: m2B 0 0 0 0 0 NaN NaN -m1B: m2C 0 0 0 0 0 NaN NaN -m1B: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1B: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1C: m2B 0 0 0 0 0 NaN NaN -m1C: m2C 0 0 0 0 0 NaN NaN -m1C: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1C: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN - - -$m4b -$m4b$m1 - Mean SD 2.5% -m1B: (Intercept) 0 0 0 -m1B: abs(C1 - C2) 0 0 0 -m1B: log(C1) 0 0 0 -m1C: (Intercept) 0 0 0 -m1C: abs(C1 - C2) 0 0 0 -m1C: log(C1) 0 0 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 - 97.5% -m1B: (Intercept) 0 -m1B: abs(C1 - C2) 0 -m1B: log(C1) 0 -m1C: (Intercept) 0 -m1C: abs(C1 - C2) 0 -m1C: log(C1) 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 - tail-prob. -m1B: (Intercept) 0 -m1B: abs(C1 - C2) 0 -m1B: log(C1) 0 -m1C: (Intercept) 0 -m1C: abs(C1 - C2) 0 -m1C: log(C1) 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 - GR-crit MCE/SD -m1B: (Intercept) NaN NaN -m1B: abs(C1 - C2) NaN NaN -m1B: log(C1) NaN NaN -m1C: (Intercept) NaN NaN -m1C: abs(C1 - C2) NaN NaN -m1C: log(C1) NaN NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN - - -$m4c -$m4c$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C1 0 0 0 0 0 NaN NaN -m1B: B21 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C1 0 0 0 0 0 NaN NaN -m1C: B21 0 0 0 0 0 NaN NaN -m1B: time 0 0 0 0 0 NaN NaN -m1B: c1 0 0 0 0 0 NaN NaN -m1C: time 0 0 0 0 0 NaN NaN -m1C: c1 0 0 0 0 0 NaN NaN - - -$m4d -$m4d$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C1 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C1 0 0 0 0 0 NaN NaN -m1B: time 0 0 0 0 0 NaN NaN -m1B: I(time^2) 0 0 0 0 0 NaN NaN -m1B: b21 0 0 0 0 0 NaN NaN -m1B: c1 0 0 0 0 0 NaN NaN -m1B: C1:time 0 0 0 0 0 NaN NaN -m1B: b21:c1 0 0 0 0 0 NaN NaN -m1C: time 0 0 0 0 0 NaN NaN -m1C: I(time^2) 0 0 0 0 0 NaN NaN -m1C: b21 0 0 0 0 0 NaN NaN -m1C: c1 0 0 0 0 0 NaN NaN -m1C: C1:time 0 0 0 0 0 NaN NaN -m1C: b21:c1 0 0 0 0 0 NaN NaN - - -$m4e -$m4e$m1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C1 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C1 0 0 0 0 0 NaN NaN -m1B: log(time) 0 0 0 0 0 NaN NaN -m1B: I(time^2) 0 0 0 0 0 NaN NaN -m1B: p1 0 0 0 0 0 NaN NaN -m1C: log(time) 0 0 0 0 0 NaN NaN -m1C: I(time^2) 0 0 0 0 0 NaN NaN -m1C: p1 0 0 0 0 0 NaN NaN - - diff --git a/tests/testthat/testout/mlogitmm_lapply.models0.print..txt b/tests/testthat/testout/mlogitmm_lapply.models0.print..txt deleted file mode 100644 index 5b0f6658..00000000 --- a/tests/testthat/testout/mlogitmm_lapply.models0.print..txt +++ /dev/null @@ -1,752 +0,0 @@ - -Call: -mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) (Intercept) - 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) (Intercept) - 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C1 (Intercept) C1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) C1 (Intercept) C1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) (Intercept) c1 c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) (Intercept) c1 c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C2 (Intercept) C2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) C2 (Intercept) C2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) (Intercept) c2 c2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) (Intercept) c2 c2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -Call: -lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - -Fixed effects: -(Intercept) m1B m1C - 0 0 0 - - -Random effects covariance matrix: -$id - c1 - (Intercept) - c1 (Intercept) 0 - - - -Residual standard deviation: -sigma_c1 - 0 - -Call: -lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - -Fixed effects: -(Intercept) m2B m2C - 0 0 0 - - -Random effects covariance matrix: -$id - c1 - (Intercept) - c1 (Intercept) 0 - - - -Residual standard deviation: -sigma_c1 - 0 - -Call: -mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) + - (1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: - (Intercept) M22 M23 M24 - 0 0 0 0 - abs(C1 - C2) log(C1) (Intercept) M22 - 0 0 0 0 - M23 M24 abs(C1 - C2) log(C1) - 0 0 0 0 - m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) - 0 0 0 0 - m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: - (Intercept) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 - (Intercept) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 - ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -Call: -mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time | - id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C1 B21 (Intercept) C1 B21 - 0 0 0 0 0 0 - time c1 time c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 m1 m1 m1 - (Intercept) c1 time c1:time - m1 (Intercept) 0 0 0 0 - m1 c1 0 0 0 0 - m1 time 0 0 0 0 - m1 c1:time 0 0 0 0 - - -Call: -mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C1 (Intercept) C1 time I(time^2) - 0 0 0 0 0 0 - b21 c1 C1:time b21:c1 time I(time^2) - 0 0 0 0 0 0 - b21 c1 C1:time b21:c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 m1 - (Intercept) time - m1 (Intercept) 0 0 - m1 time 0 0 - - -Call: -mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, - random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C1 (Intercept) C1 log(time) I(time^2) - 0 0 0 0 0 0 - p1 log(time) I(time^2) p1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - -$m0a - -Call: -mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) (Intercept) - 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -$m0b - -Call: -mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) (Intercept) - 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -$m1a - -Call: -mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C1 (Intercept) C1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -$m1b - -Call: -mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) C1 (Intercept) C1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -$m1c - -Call: -mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) (Intercept) c1 c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -$m1d - -Call: -mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) (Intercept) c1 c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -$m2a - -Call: -mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C2 (Intercept) C2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -$m2b - -Call: -mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) C2 (Intercept) C2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -$m2c - -Call: -mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) (Intercept) c2 c2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -$m2d - -Call: -mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m2" - -Fixed effects: -(Intercept) (Intercept) c2 c2 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m2 - (Intercept) - m2 (Intercept) 0 - - -$m3a - -Call: -lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - -Fixed effects: -(Intercept) m1B m1C - 0 0 0 - - -Random effects covariance matrix: -$id - c1 - (Intercept) - c1 (Intercept) 0 - - - -Residual standard deviation: -sigma_c1 - 0 - -$m3b - -Call: -lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - -Fixed effects: -(Intercept) m2B m2C - 0 0 0 - - -Random effects covariance matrix: -$id - c1 - (Intercept) - c1 (Intercept) 0 - - - -Residual standard deviation: -sigma_c1 - 0 - -$m4a - -Call: -mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) + - (1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: - (Intercept) M22 M23 M24 - 0 0 0 0 - abs(C1 - C2) log(C1) (Intercept) M22 - 0 0 0 0 - M23 M24 abs(C1 - C2) log(C1) - 0 0 0 0 - m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) - 0 0 0 0 - m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -$m4b - -Call: -mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: - (Intercept) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 - (Intercept) - 0 - abs(C1 - C2) - 0 - log(C1) - 0 - ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) - 0 -ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) - 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - -$m4c - -Call: -mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time | - id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C1 B21 (Intercept) C1 B21 - 0 0 0 0 0 0 - time c1 time c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 m1 m1 m1 - (Intercept) c1 time c1:time - m1 (Intercept) 0 0 0 0 - m1 c1 0 0 0 0 - m1 time 0 0 0 0 - m1 c1:time 0 0 0 0 - - -$m4d - -Call: -mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C1 (Intercept) C1 time I(time^2) - 0 0 0 0 0 0 - b21 c1 C1:time b21:c1 time I(time^2) - 0 0 0 0 0 0 - b21 c1 C1:time b21:c1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 m1 - (Intercept) time - m1 (Intercept) 0 0 - m1 time 0 0 - - -$m4e - -Call: -mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, - random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian multinomial logit mixed model for "m1" - -Fixed effects: -(Intercept) C1 (Intercept) C1 log(time) I(time^2) - 0 0 0 0 0 0 - p1 log(time) I(time^2) p1 - 0 0 0 0 - - -Random effects covariance matrix: -$id - m1 - (Intercept) - m1 (Intercept) 0 - - diff --git a/tests/testthat/testout/mlogitmm_lapply.models0.summary..txt b/tests/testthat/testout/mlogitmm_lapply.models0.summary..txt deleted file mode 100644 index 92a15176..00000000 --- a/tests/testthat/testout/mlogitmm_lapply.models0.summary..txt +++ /dev/null @@ -1,796 +0,0 @@ -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -[1] "No variability observed in a component. Setting batch size to 1" -$m0a - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m0b - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m1a - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C1 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C1 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m1b - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2B: C1 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN -m2C: C1 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m1c - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1B: c1 0 0 0 0 0 NaN NaN -m1C: c1 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m1d - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN -m2B: c1 0 0 0 0 0 NaN NaN -m2C: c1 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m2a - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C2 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m2b - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2B: C2 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN -m2C: C2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m2c - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1B: c2 0 0 0 0 0 NaN NaN -m1C: c2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m2d - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m2B: (Intercept) 0 0 0 0 0 NaN NaN -m2C: (Intercept) 0 0 0 0 0 NaN NaN -m2B: c2 0 0 0 0 0 NaN NaN -m2C: c2 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m2_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m3a - -Bayesian linear mixed model fitted with JointAI - -Call: -lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -m1B 0 0 0 0 0 NaN NaN -m1C 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_c1_id[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_c1 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 1:10 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m3b - -Bayesian linear mixed model fitted with JointAI - -Call: -lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -(Intercept) 0 0 0 0 0 NaN NaN -m2B 0 0 0 0 0 NaN NaN -m2C 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_c1_id[1,1] 0 0 0 0 NaN NaN - - -Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD -sigma_c1 0 0 0 0 NaN NaN - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4a - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) + - (1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: M22 0 0 0 0 0 NaN NaN -m1B: M23 0 0 0 0 0 NaN NaN -m1B: M24 0 0 0 0 0 NaN NaN -m1B: abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1B: log(C1) 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: M22 0 0 0 0 0 NaN NaN -m1C: M23 0 0 0 0 0 NaN NaN -m1C: M24 0 0 0 0 0 NaN NaN -m1C: abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1C: log(C1) 0 0 0 0 0 NaN NaN -m1B: m2B 0 0 0 0 0 NaN NaN -m1B: m2C 0 0 0 0 0 NaN NaN -m1B: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1B: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1C: m2B 0 0 0 0 0 NaN NaN -m1C: m2C 0 0 0 0 0 NaN NaN -m1C: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN -m1C: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4b - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1), - 1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% -m1B: (Intercept) 0 0 0 -m1B: abs(C1 - C2) 0 0 0 -m1B: log(C1) 0 0 0 -m1C: (Intercept) 0 0 0 -m1C: abs(C1 - C2) 0 0 0 -m1C: log(C1) 0 0 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 - 97.5% -m1B: (Intercept) 0 -m1B: abs(C1 - C2) 0 -m1B: log(C1) 0 -m1C: (Intercept) 0 -m1C: abs(C1 - C2) 0 -m1C: log(C1) 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 - tail-prob. -m1B: (Intercept) 0 -m1B: abs(C1 - C2) 0 -m1B: log(C1) 0 -m1C: (Intercept) 0 -m1C: abs(C1 - C2) 0 -m1C: log(C1) 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 - GR-crit MCE/SD -m1B: (Intercept) NaN NaN -m1B: abs(C1 - C2) NaN NaN -m1B: log(C1) NaN NaN -m1C: (Intercept) NaN NaN -m1C: abs(C1 - C2) NaN NaN -m1C: log(C1) NaN NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN -m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN -m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4c - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time | - id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C1 0 0 0 0 0 NaN NaN -m1B: B21 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C1 0 0 0 0 0 NaN NaN -m1C: B21 0 0 0 0 0 NaN NaN -m1B: time 0 0 0 0 0 NaN NaN -m1B: c1 0 0 0 0 0 NaN NaN -m1C: time 0 0 0 0 0 NaN NaN -m1C: c1 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN -D_m1_id[1,2] 0 0 0 0 0 NaN NaN -D_m1_id[2,2] 0 0 0 0 NaN NaN -D_m1_id[1,3] 0 0 0 0 0 NaN NaN -D_m1_id[2,3] 0 0 0 0 0 NaN NaN -D_m1_id[3,3] 0 0 0 0 NaN NaN -D_m1_id[1,4] 0 0 0 0 0 NaN NaN -D_m1_id[2,4] 0 0 0 0 0 NaN NaN -D_m1_id[3,4] 0 0 0 0 0 NaN NaN -D_m1_id[4,4] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4d - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C1 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C1 0 0 0 0 0 NaN NaN -m1B: time 0 0 0 0 0 NaN NaN -m1B: I(time^2) 0 0 0 0 0 NaN NaN -m1B: b21 0 0 0 0 0 NaN NaN -m1B: c1 0 0 0 0 0 NaN NaN -m1B: C1:time 0 0 0 0 0 NaN NaN -m1B: b21:c1 0 0 0 0 0 NaN NaN -m1C: time 0 0 0 0 0 NaN NaN -m1C: I(time^2) 0 0 0 0 0 NaN NaN -m1C: b21 0 0 0 0 0 NaN NaN -m1C: c1 0 0 0 0 0 NaN NaN -m1C: C1:time 0 0 0 0 0 NaN NaN -m1C: b21:c1 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN -D_m1_id[1,2] 0 0 0 0 0 NaN NaN -D_m1_id[2,2] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - -$m4e - -Bayesian multinomial logit mixed model fitted with JointAI - -Call: -mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, - random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - -Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -m1B: (Intercept) 0 0 0 0 0 NaN NaN -m1B: C1 0 0 0 0 0 NaN NaN -m1C: (Intercept) 0 0 0 0 0 NaN NaN -m1C: C1 0 0 0 0 0 NaN NaN -m1B: log(time) 0 0 0 0 0 NaN NaN -m1B: I(time^2) 0 0 0 0 0 NaN NaN -m1B: p1 0 0 0 0 0 NaN NaN -m1C: log(time) 0 0 0 0 0 NaN NaN -m1C: I(time^2) 0 0 0 0 0 NaN NaN -m1C: p1 0 0 0 0 0 NaN NaN - - -Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -D_m1_id[1,1] 0 0 0 0 NaN NaN - - - -MCMC settings: -Iterations = 6:15 -Sample size per chain = 10 -Thinning interval = 1 -Number of chains = 3 - -Number of observations: 329 -Number of groups: - - id: 100 - diff --git a/tests/testthat/testout/printfcts_list_models.mmod..txt b/tests/testthat/testout/printfcts_list_models.mmod..txt deleted file mode 100644 index f6fefe0f..00000000 --- a/tests/testthat/testout/printfcts_list_models.mmod..txt +++ /dev/null @@ -1,76 +0,0 @@ -Multinomial logit mixed model for "x" -* Reference category: "1" -* Predictor variables: - (Intercept), C1, B21, O21, O22, p1, c2, y, time, y:time -* Regression coefficients: - x2: beta[1:5] - x3: beta[6:10] - x2: beta[11:15] - x3: beta[16:20] (normal prior(s) with mean 0 and precision 1e-04) - - -Linear mixed model for "c2" - family: gaussian - link: identity -* Predictor variables: - (Intercept), C1, B21, O21, O22, p1, y, time -* Regression coefficients: - alpha[1:8] (normal prior(s) with mean 0 and precision 1e-04) -* Precision of "c2" : - tau_c2 (Gamma prior with shape parameter 0.01 and rate parameter 0.01) - - -Poisson mixed model for "p1" - family: poisson - link: log -* Predictor variables: - (Intercept), C1, B21, O21, O22, y, time -* Regression coefficients: - alpha[9:15] (normal prior(s) with mean 0 and precision 1e-04) - - -Linear mixed model for "y" - family: gaussian - link: identity -* Predictor variables: - (Intercept), C1, B21, O21, O22, time -* Regression coefficients: - alpha[16:21] (normal prior(s) with mean 0 and precision 1e-04) -* Precision of "y" : - tau_y (Gamma prior with shape parameter 0.01 and rate parameter 0.01) - - -Linear mixed model for "time" - family: gaussian - link: identity -* Predictor variables: - (Intercept), C1, B21, O21, O22 -* Regression coefficients: - alpha[22:26] (normal prior(s) with mean 0 and precision 1e-04) -* Precision of "time" : - tau_time (Gamma prior with shape parameter 0.01 and rate parameter 0.01) - - -Cumulative logit model for "O2" -* Reference category: "3" -* Predictor variables: - C1, B21 -* Regression coefficients: - alpha[27:28] (normal prior(s) with mean 0 and precision 1e-04) -* Intercepts: - - 1: gamma_O2[1] (normal prior with mean 0 and precision 1e-04) - - 2: gamma_O2[2] = gamma_O2[1] + exp(delta_O2[1]) -* Increments: - delta_O2[1] (normal prior(s) with mean 0 and precision 1e-04) - - -Binomial model for "B2" - family: binomial - link: logit -* Reference category: "0" -* Predictor variables: - (Intercept), C1 -* Regression coefficients: - alpha[29:30] (normal prior(s) with mean 0 and precision 1e-04) - - diff --git a/tests/testthat/testout/printfcts_list_models.mymod..txt b/tests/testthat/testout/printfcts_list_models.mymod..txt deleted file mode 100644 index f0f3fd1a..00000000 --- a/tests/testthat/testout/printfcts_list_models.mymod..txt +++ /dev/null @@ -1,67 +0,0 @@ -Linear mixed model for "y" - family: gaussian - link: identity -* Predictor variables: - (Intercept), C1, B21, O22, O23, c1, c2, time -* Regression coefficients: - beta[1:8] (normal prior(s) with mean 0 and precision 1e-04) -* Precision of "y" : - tau_y (Gamma prior with shape parameter 0.01 and rate parameter 0.01) - - -Linear mixed model for "c2" - family: gaussian - link: identity -* Predictor variables: - (Intercept), C1, B21, O22, O23, c1, time -* Regression coefficients: - alpha[1:7] (normal prior(s) with mean 0 and precision 1e-04) -* Precision of "c2" : - tau_c2 (Gamma prior with shape parameter 0.01 and rate parameter 0.01) - - -Linear mixed model for "c1" - family: gaussian - link: identity -* Predictor variables: - (Intercept), C1, B21, O22, O23, time -* Regression coefficients: - alpha[8:13] (normal prior(s) with mean 0 and precision 1e-04) -* Precision of "c1" : - tau_c1 (Gamma prior with shape parameter 0.01 and rate parameter 0.01) - - -Linear mixed model for "time" - family: gaussian - link: identity -* Predictor variables: - (Intercept), C1, B21, O22, O23 -* Regression coefficients: - alpha[14:18] (normal prior(s) with mean 0 and precision 1e-04) -* Precision of "time" : - tau_time (Gamma prior with shape parameter 0.01 and rate parameter 0.01) - - -Cumulative logit model for "O2" -* Reference category: "1" -* Predictor variables: - C1, B21 -* Regression coefficients: - alpha[19:20] (normal prior(s) with mean 0 and precision 1e-04) -* Intercepts: - - 1: gamma_O2[1] (normal prior with mean 0 and precision 1e-04) - - 2: gamma_O2[2] = gamma_O2[1] + exp(delta_O2[1]) -* Increments: - delta_O2[1] (normal prior(s) with mean 0 and precision 1e-04) - - -Binomial model for "B2" - family: binomial - link: logit -* Reference category: "0" -* Predictor variables: - (Intercept), C1 -* Regression coefficients: - alpha[21:22] (normal prior(s) with mean 0 and precision 1e-04) - - diff --git a/tests/testthat/testout/printfcts_parameters.mmod..txt b/tests/testthat/testout/printfcts_parameters.mmod..txt deleted file mode 100644 index d4bfc5cc..00000000 --- a/tests/testthat/testout/printfcts_parameters.mmod..txt +++ /dev/null @@ -1,24 +0,0 @@ - outcome outcat varname coef -1 x x2 (Intercept) beta[1] -2 x x2 C1 beta[2] -3 x x2 B21 beta[3] -4 x x2 O21 beta[4] -5 x x2 O22 beta[5] -6 x x3 (Intercept) beta[6] -7 x x3 C1 beta[7] -8 x x3 B21 beta[8] -9 x x3 O21 beta[9] -10 x x3 O22 beta[10] -11 x x2 p1 beta[11] -12 x x2 c2 beta[12] -13 x x2 y beta[13] -14 x x2 time beta[14] -15 x x2 y:time beta[15] -16 x x3 p1 beta[16] -17 x x3 c2 beta[17] -18 x x3 y beta[18] -19 x x3 time beta[19] -20 x x3 y:time beta[20] -21 x D_x_id[1,1] -22 x D_x_id[1,2] -23 x D_x_id[2,2] diff --git a/tests/testthat/testout/printfcts_parameters.mymod..txt b/tests/testthat/testout/printfcts_parameters.mymod..txt deleted file mode 100644 index bf7897f5..00000000 --- a/tests/testthat/testout/printfcts_parameters.mymod..txt +++ /dev/null @@ -1,13 +0,0 @@ - outcome outcat varname coef -1 y (Intercept) beta[1] -2 y C1 beta[2] -3 y B21 beta[3] -4 y O22 beta[4] -5 y O23 beta[5] -6 y 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--- tests/testthat/_snaps/printfcts.md | 204 +++++++++++++++++++++++++++++ tests/testthat/test-printfcts.R | 10 +- 2 files changed, 210 insertions(+), 4 deletions(-) create mode 100644 tests/testthat/_snaps/printfcts.md diff --git a/tests/testthat/_snaps/printfcts.md b/tests/testthat/_snaps/printfcts.md new file mode 100644 index 00000000..dacc77ce --- /dev/null +++ b/tests/testthat/_snaps/printfcts.md @@ -0,0 +1,204 @@ +# lme model + + Code + list_models(mymod) + Output + Linear mixed model for "y" + family: gaussian + link: identity + * Predictor variables: + (Intercept), C1, B21, O22, O23, c1, c2, time + * Regression coefficients: + beta[1:8] (normal prior(s) with mean 0 and precision 1e-04) + * Precision of "y" : + tau_y (Gamma prior with shape parameter 0.01 and rate parameter 0.01) + + + Linear mixed model for "c2" + family: gaussian + link: identity + * Predictor variables: + (Intercept), C1, B21, O22, O23, c1, time + * Regression coefficients: + alpha[1:7] (normal prior(s) with mean 0 and precision 1e-04) + * Precision of "c2" : + tau_c2 (Gamma prior with shape parameter 0.01 and rate parameter 0.01) + + + Linear mixed model for "c1" + family: gaussian + link: identity + * Predictor variables: + (Intercept), C1, B21, O22, O23, time + * Regression coefficients: + alpha[8:13] (normal prior(s) with mean 0 and precision 1e-04) + * Precision of "c1" : + tau_c1 (Gamma prior with shape parameter 0.01 and rate parameter 0.01) + + + Linear mixed model for "time" + family: gaussian + link: identity + * Predictor variables: + (Intercept), C1, B21, O22, O23 + * Regression coefficients: + alpha[14:18] (normal prior(s) with mean 0 and precision 1e-04) + * Precision of "time" : + tau_time (Gamma prior with shape parameter 0.01 and rate parameter 0.01) + + + Cumulative logit model for "O2" + * Reference category: "1" + * Predictor variables: + C1, B21 + * Regression coefficients: + alpha[19:20] (normal prior(s) with mean 0 and precision 1e-04) + * Intercepts: + - 1: gamma_O2[1] (normal prior with mean 0 and precision 1e-04) + - 2: gamma_O2[2] = gamma_O2[1] + exp(delta_O2[1]) + * Increments: + delta_O2[1] (normal prior(s) with mean 0 and precision 1e-04) + + + Binomial model for "B2" + family: binomial + link: logit + * Reference category: "0" + * Predictor variables: + (Intercept), C1 + * Regression coefficients: + alpha[21:22] (normal prior(s) with mean 0 and precision 1e-04) + + + +--- + + Code + parameters(mymod) + Output + outcome outcat varname coef + 1 y (Intercept) beta[1] + 2 y C1 beta[2] + 3 y B21 beta[3] + 4 y O22 beta[4] + 5 y O23 beta[5] + 6 y c1 beta[6] + 7 y c2 beta[7] + 8 y time beta[8] + 9 y sigma_y + 10 y D_y_id[1,1] + 11 y D_y_id[1,2] + 12 y D_y_id[2,2] + +# mlogitmm + + Code + list_models(mmod) + Output + Multinomial logit mixed model for "x" + * Reference category: "1" + * Predictor variables: + (Intercept), C1, B21, O21, O22, p1, c2, y, time, y:time + * Regression coefficients: + x2: beta[1:5] + x3: beta[6:10] + x2: beta[11:15] + x3: beta[16:20] (normal prior(s) with mean 0 and precision 1e-04) + + + Linear mixed model for "c2" + family: gaussian + link: identity + * Predictor variables: + (Intercept), C1, B21, O21, O22, p1, y, time + * Regression coefficients: + alpha[1:8] (normal prior(s) with mean 0 and precision 1e-04) + * Precision of "c2" : + tau_c2 (Gamma prior with shape parameter 0.01 and rate parameter 0.01) + + + Poisson mixed model for "p1" + family: poisson + link: log + * Predictor variables: + (Intercept), C1, B21, O21, O22, y, time + * Regression coefficients: + alpha[9:15] (normal prior(s) with mean 0 and precision 1e-04) + + + Linear mixed model for "y" + family: gaussian + link: identity + * Predictor variables: + (Intercept), C1, B21, O21, O22, time + * Regression coefficients: + alpha[16:21] (normal prior(s) with mean 0 and precision 1e-04) + * Precision of "y" : + tau_y (Gamma prior with shape parameter 0.01 and rate parameter 0.01) + + + Linear mixed model for "time" + family: gaussian + link: identity + * Predictor variables: + (Intercept), C1, B21, O21, O22 + * Regression coefficients: + alpha[22:26] (normal prior(s) with mean 0 and precision 1e-04) + * Precision of "time" : + tau_time (Gamma prior with shape parameter 0.01 and rate parameter 0.01) + + + Cumulative logit model for "O2" + * Reference category: "3" + * Predictor variables: + C1, B21 + * Regression coefficients: + alpha[27:28] (normal prior(s) with mean 0 and precision 1e-04) + * Intercepts: + - 1: gamma_O2[1] (normal prior with mean 0 and precision 1e-04) + - 2: gamma_O2[2] = gamma_O2[1] + exp(delta_O2[1]) + * Increments: + delta_O2[1] (normal prior(s) with mean 0 and precision 1e-04) + + + Binomial model for "B2" + family: binomial + link: logit + * Reference category: "0" + * Predictor variables: + (Intercept), C1 + * Regression coefficients: + alpha[29:30] (normal prior(s) with mean 0 and precision 1e-04) + + + +--- + + Code + parameters(mmod) + Output + outcome outcat varname coef + 1 x x2 (Intercept) beta[1] + 2 x x2 C1 beta[2] + 3 x x2 B21 beta[3] + 4 x x2 O21 beta[4] + 5 x x2 O22 beta[5] + 6 x x3 (Intercept) beta[6] + 7 x x3 C1 beta[7] + 8 x x3 B21 beta[8] + 9 x x3 O21 beta[9] + 10 x x3 O22 beta[10] + 11 x x2 p1 beta[11] + 12 x x2 c2 beta[12] + 13 x x2 y beta[13] + 14 x x2 time beta[14] + 15 x x2 y:time beta[15] + 16 x x3 p1 beta[16] + 17 x x3 c2 beta[17] + 18 x x3 y beta[18] + 19 x x3 time beta[19] + 20 x x3 y:time beta[20] + 21 x D_x_id[1,1] + 22 x D_x_id[1,2] + 23 x D_x_id[2,2] + diff --git a/tests/testthat/test-printfcts.R b/tests/testthat/test-printfcts.R index 1e85c6b2..43024c82 100644 --- a/tests/testthat/test-printfcts.R +++ b/tests/testthat/test-printfcts.R @@ -1,5 +1,7 @@ library("JointAI") +skip_on_cran() + test_that('lme model', { mymod <- lme_imp(y ~ C1 + c1 + B2 + c2 + O2 + time + (time | id), data = longDF, n.adapt = 10, n.iter = 10, @@ -8,8 +10,8 @@ test_that('lme model', { expect_output(list_models(mymod)) expect_s3_class(parameters(mymod), 'data.frame') - print_output(list_models(mymod), context = "printfcts") - print_output(parameters(mymod), context = "printfcts") + expect_snapshot(list_models(mymod)) + expect_snapshot(parameters(mymod)) }) @@ -24,6 +26,6 @@ test_that('mlogitmm', { expect_output(list_models(mmod)) expect_s3_class(parameters(mmod), 'data.frame') - print_output(list_models(mmod), context = "printfcts") - print_output(parameters(mmod), context = "printfcts") + expect_snapshot(list_models(mmod)) + expect_snapshot(parameters(mmod)) }) From b2476d9f6ed4e279a7e69a021860242ee5aa6a38 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 2 Sep 2022 14:13:55 +0200 Subject: [PATCH 130/176] update version number --- DESCRIPTION | 2 +- NEWS.md | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index cccbdf98..7907ff80 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.3.9000 +Version: 1.0.4 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), diff --git a/NEWS.md b/NEWS.md index da532a3b..a4fb0a66 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,8 @@ # JointAI Development Vesion +# JointAI 1.0.4 + ## New features * `md_pattern()` has an additional argument `sort_columns` to provide the option to switch off the sorting of columns by number of missing values. From 05ce0dce4e30ad9c41fb8bc78cf9a6931b284144 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 2 Sep 2022 15:54:12 +0200 Subject: [PATCH 131/176] re-run vignettes --- vignettes/AfterFitting.Rmd | 36 ++++---- vignettes/MinimalExample.Rmd | 32 +++---- vignettes/ModelSpecification.Rmd | 18 ++-- vignettes/SelectingParameters.Rmd | 84 +++++++++--------- .../unnamed-chunk-14-1.png | Bin 28264 -> 27605 bytes .../unnamed-chunk-15-1.png | Bin 12907 -> 12990 bytes .../unnamed-chunk-15-2.png | Bin 11351 -> 11395 bytes .../unnamed-chunk-16-1.png | Bin 16413 -> 16755 bytes .../unnamed-chunk-16-2.png | Bin 26452 -> 27232 bytes .../unnamed-chunk-16-3.png | Bin 19318 -> 19881 bytes .../figures_MinimalExample/results_lm1-1.png | Bin 17934 -> 17438 bytes .../unnamed-chunk-4-1.png | Bin 12422 -> 12153 bytes .../figures_SelectingParameters/lm2_2-1.png | Bin 4170 -> 4493 bytes .../unnamed-chunk-10-1.png | Bin 5513 -> 5411 bytes .../unnamed-chunk-11-1.png | Bin 15263 -> 15611 bytes .../unnamed-chunk-11-2.png | Bin 15965 -> 15811 bytes .../unnamed-chunk-9-1.png | Bin 19876 -> 18519 bytes .../unnamed-chunk-9-2.png | Bin 10887 -> 10574 bytes 18 files changed, 85 insertions(+), 85 deletions(-) diff --git a/vignettes/AfterFitting.Rmd b/vignettes/AfterFitting.Rmd index c955e95d..34f21cce 100644 --- a/vignettes/AfterFitting.Rmd +++ b/vignettes/AfterFitting.Rmd @@ -55,7 +55,7 @@ mod13a <- lm_imp(SBP ~ gender + WC + alc + creat, data = NHANES, n.iter = 500, traceplot(mod13a) ``` -plot of chunk unnamed-chunk-1 +plot of chunk unnamed-chunk-1 When the sampler has converged the chains show one horizontal band, as in the above figure. @@ -78,7 +78,7 @@ traceplot(mod13a, ncol = 3, use_ggplot = TRUE) + scale_color_brewer(palette = 'Dark2') ``` -plot of chunk ggtrace15a +plot of chunk ggtrace15a ### Density plot @@ -97,7 +97,7 @@ densplot(mod13a, ncol = 3, col = c("darkred", "darkblue", "darkgreen"), col = grey(0.8)))) ``` -plot of chunk unnamed-chunk-3 +plot of chunk unnamed-chunk-3 or marking the posterior mean and 2.5% and 97.5% quantiles: ```r @@ -113,7 +113,7 @@ densplot(mod13a, ncol = 3, ) ``` -plot of chunk densplot15a +plot of chunk densplot15a Like with `traceplot()` it is possible to use the **ggplot2** version of `densplot()` @@ -167,7 +167,7 @@ p13a + labels = c('JointAI', 'compl.case')) ``` -plot of chunk ggdens15a +plot of chunk ggdens15a ## Model Summary @@ -304,7 +304,7 @@ estimated posterior distribution. The figure visualizes three examples of posterior distributions and the corresponding minimum of $Pr(\theta > 0)$ and $Pr(\theta < 0)$ (shaded area): -plot of chunk tailprob +plot of chunk tailprob ## Evaluation criteria @@ -393,7 +393,7 @@ plot(MC_error(mod13a)) # left panel: all iterations 101:600 plot(MC_error(mod13a, end = 250)) # right panel: iterations 101:250 ``` -plot of chunk MCE15a +plot of chunk MCE15a ## Subset of output {#sec:subset} @@ -494,7 +494,7 @@ densplot(mod13c, subset = list(analysis_main = FALSE, other = c('beta[4]', 'beta[5]')), nrow = 1) ``` -plot of chunk unnamed-chunk-11 +plot of chunk unnamed-chunk-11 This also works when a subset of the imputed values should be displayed: @@ -513,7 +513,7 @@ sub3 traceplot(mod13d, subset = list(analysis_main = FALSE, other = sub3), ncol = 2) ``` -plot of chunk trace15d +plot of chunk trace15d When the number of imputed values is large or in order to check convergence of random effects, it may not be feasible to plot and inspect all trace plots. @@ -534,7 +534,7 @@ traceplot(mod13e, subset = list(analysis_main = FALSE, other = sample(ri, size = 12)), ncol = 4) ``` -plot of chunk unnamed-chunk-14 +plot of chunk unnamed-chunk-14 ### Subset of MCMC samples @@ -556,32 +556,32 @@ mod14 <- lm_imp(SBP ~ gender + WC + alc + creat, data = NHANES, n.iter = 100, densplot(mod14, ncol = 3) ``` -plot of chunk unnamed-chunk-15 +plot of chunk unnamed-chunk-15 ```r densplot(mod14, exclude_chains = c(2,4), ncol = 3) ``` -plot of chunk unnamed-chunk-15 +plot of chunk unnamed-chunk-15 ```r traceplot(mod14, thin = 10, ncol = 3) ``` -plot of chunk unnamed-chunk-16 +plot of chunk unnamed-chunk-16 ```r traceplot(mod14, start = 150, ncol = 3) ``` -plot of chunk unnamed-chunk-16 +plot of chunk unnamed-chunk-16 ```r traceplot(mod14, end = 120, ncol = 3) ``` -plot of chunk unnamed-chunk-16 +plot of chunk unnamed-chunk-16 @@ -652,7 +652,7 @@ matplot(pred$newdata$age, pred$fitted, xlab = 'age in months', ylab = 'predicted value') ``` -plot of chunk unnamed-chunk-19 +plot of chunk unnamed-chunk-19 It is possible to have multiple variables vary and to set values for these variables: @@ -684,7 +684,7 @@ ggplot(pred$newdata, aes(x = age, y = fit, color = factor(HEIGHT_M), scale_y_continuous(name = 'Expected BMI', breaks = seq(15, 18, 0.5)) ``` -plot of chunk unnamed-chunk-20 +plot of chunk unnamed-chunk-20 @@ -732,5 +732,5 @@ of the observed and imputed values. plot_imp_distr(impDF, nrow = 1) ``` -plot of chunk unnamed-chunk-22 +plot of chunk unnamed-chunk-22 diff --git a/vignettes/MinimalExample.Rmd b/vignettes/MinimalExample.Rmd index 78d93dbd..fbdf033d 100644 --- a/vignettes/MinimalExample.Rmd +++ b/vignettes/MinimalExample.Rmd @@ -71,7 +71,7 @@ Convergence can be evaluated visually with a trace plot. traceplot(lm1) ``` -plot of chunk results_lm1 +plot of chunk results_lm1 The function [`traceplot()`](https://nerler.github.io/JointAI/reference/traceplot.html) produces a plot of the sampled values across @@ -102,22 +102,22 @@ summary(lm1) #> #> Posterior summary: #> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 60.468 23.2853 14.5276 106.086 0.00933 1.012 0.0294 -#> genderfemale -3.111 2.2576 -7.8405 1.301 0.16933 1.006 0.0258 -#> age 0.361 0.0707 0.2250 0.496 0.00000 1.002 0.0267 -#> raceOther Hispanic 0.606 5.0592 -9.1456 10.972 0.91733 1.009 0.0258 -#> raceNon-Hispanic White -1.414 3.1084 -7.7117 4.190 0.66133 1.004 0.0258 -#> raceNon-Hispanic Black 8.992 3.5962 2.1122 16.088 0.01200 0.999 0.0258 -#> raceother 3.744 3.5421 -3.2277 10.644 0.30000 1.012 0.0258 -#> WC 0.241 0.0829 0.0852 0.411 0.00133 1.013 0.0267 -#> alc>=1 7.282 2.2352 2.8051 11.519 0.00133 1.014 0.0315 -#> educhigh -3.390 2.1778 -7.6542 1.039 0.12533 1.001 0.0258 -#> albu 5.312 4.2827 -3.0474 13.791 0.21600 1.009 0.0294 -#> bili -5.456 4.9605 -14.8959 4.258 0.26000 1.005 0.0266 +#> (Intercept) 61.928 22.0386 20.6177 104.822 0.00400 1.00 0.0264 +#> genderfemale -3.116 2.2651 -7.4493 1.407 0.16267 1.00 0.0258 +#> age 0.364 0.0733 0.2248 0.511 0.00000 1.00 0.0258 +#> raceOther Hispanic 0.469 5.0392 -9.1897 10.436 0.90667 1.00 0.0258 +#> raceNon-Hispanic White -1.615 3.0196 -7.5193 4.246 0.58267 1.00 0.0258 +#> raceNon-Hispanic Black 8.793 3.6753 1.7979 15.790 0.01467 1.00 0.0258 +#> raceother 3.734 3.4810 -2.9797 10.729 0.30133 1.00 0.0265 +#> WC 0.238 0.0825 0.0847 0.404 0.01200 1.00 0.0258 +#> alc>=1 7.244 2.2752 2.6597 11.601 0.00133 1.00 0.0293 +#> educhigh -3.535 2.0936 -7.5238 0.489 0.08267 1.01 0.0258 +#> albu 5.087 3.9275 -2.7394 12.826 0.20400 1.00 0.0280 +#> bili -5.545 4.7804 -14.9430 3.511 0.24000 1.01 0.0282 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_SBP 13.2 0.719 11.9 14.7 1.01 0.0273 +#> sigma_SBP 13.2 0.739 11.8 14.7 1 0.0277 #> #> #> MCMC settings: @@ -152,7 +152,7 @@ $$2\times\min\left\{Pr(\theta > 0), Pr(\theta < 0)\right\}$$ In the following graphics, the shaded areas represent the minimum of $Pr(\theta > 0)$ and $Pr(\theta < 0)$: -plot of chunk unnamed-chunk-3 +plot of chunk unnamed-chunk-3 #### Gelman-Rubin criterion The Gelman-Rubin^[Gelman, A and Rubin, DB (1992) Inference from iterative @@ -200,7 +200,7 @@ The posterior distributions can be visualized using the function `densplot()`: densplot(lm1) ``` -plot of chunk unnamed-chunk-4 +plot of chunk unnamed-chunk-4 By default, `densplot()` plots the empirical distribution of each of the chains separately. When `joined = TRUE` the distributions of the combined chains diff --git a/vignettes/ModelSpecification.Rmd b/vignettes/ModelSpecification.Rmd index 3e247d1d..80858336 100644 --- a/vignettes/ModelSpecification.Rmd +++ b/vignettes/ModelSpecification.Rmd @@ -1044,9 +1044,9 @@ For example: mod10a <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, refcats = "largest", data = NHANES, n.adapt = 0) #> Warning: -#> It is currently not possible to use "contr.poly" for incomplete categorical covariates. -#> I will use "contr.treatment" instead. You can specify (globally) which types of -#> contrasts are used by changing "options('contrasts')". +#> It is currently not possible to use "contr.poly" for incomplete categorical covariates. I +#> will use "contr.treatment" instead. You can specify (globally) which types of contrasts +#> are used by changing "options('contrasts')". ``` #### Setting reference categories for individual variables @@ -1062,9 +1062,9 @@ mod10b <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, refcats = list(occup = "not working", race = 3, educ = 'largest'), data = NHANES, n.adapt = 0) #> Warning: -#> It is currently not possible to use "contr.poly" for incomplete categorical covariates. -#> I will use "contr.treatment" instead. You can specify (globally) which types of -#> contrasts are used by changing "options('contrasts')". +#> It is currently not possible to use "contr.poly" for incomplete categorical covariates. I +#> will use "contr.treatment" instead. You can specify (globally) which types of contrasts +#> are used by changing "options('contrasts')". ``` @@ -1135,9 +1135,9 @@ the determined specification for the argument `refcats` is printed: mod10c <- lm_imp(SBP ~ gender + age + race + educ + occup + smoke, refcats = refs_mod10, data = NHANES, n.adapt = 0) #> Warning: -#> It is currently not possible to use "contr.poly" for incomplete categorical covariates. -#> I will use "contr.treatment" instead. You can specify (globally) which types of -#> contrasts are used by changing "options('contrasts')". +#> It is currently not possible to use "contr.poly" for incomplete categorical covariates. I +#> will use "contr.treatment" instead. You can specify (globally) which types of contrasts +#> are used by changing "options('contrasts')". ``` diff --git a/vignettes/SelectingParameters.Rmd b/vignettes/SelectingParameters.Rmd index 00872a7b..2fcce963 100644 --- a/vignettes/SelectingParameters.Rmd +++ b/vignettes/SelectingParameters.Rmd @@ -520,15 +520,15 @@ summary(lm5) #> #> Posterior summary: #> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 81.737 9.9071 62.317 100.78 0.0000 1.023 0.0577 -#> genderfemale 0.213 2.5267 -4.562 4.93 0.9133 1.050 0.0577 -#> WC 0.301 0.0755 0.136 0.45 0.0000 0.999 0.0577 -#> alc>=1 6.157 2.5434 1.197 11.01 0.0333 1.009 0.0671 -#> creat 7.781 7.5670 -5.807 23.68 0.2933 1.052 0.0634 +#> (Intercept) 81.088 9.4594 62.852 97.814 0.000 1.02 0.0577 +#> genderfemale 0.315 2.5308 -4.366 5.680 0.933 1.02 0.0577 +#> WC 0.311 0.0721 0.173 0.455 0.000 1.00 0.0577 +#> alc>=1 6.288 2.3944 1.946 10.938 0.000 1.07 0.0657 +#> creat 7.239 8.1750 -7.610 25.142 0.387 1.00 0.0577 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_SBP 14.4 0.756 13.1 15.9 1.05 0.0577 +#> sigma_SBP 14.4 0.789 12.9 15.9 1 0.0577 #> #> #> MCMC settings: @@ -543,7 +543,7 @@ summary(lm5) traceplot(lm5) ``` -plot of chunk unnamed-chunk-9 +plot of chunk unnamed-chunk-9 ```r @@ -551,7 +551,7 @@ traceplot(lm5) densplot(lm5) ``` -plot of chunk unnamed-chunk-9 +plot of chunk unnamed-chunk-9 ```r @@ -560,12 +560,12 @@ GR_crit(lm5) #> Potential scale reduction factors: #> #> Point est. Upper C.I. -#> (Intercept) 1.003 1.023 -#> genderfemale 1.012 1.050 -#> WC 0.997 0.999 -#> alc>=1 1.000 1.009 -#> creat 1.018 1.052 -#> sigma_SBP 1.012 1.055 +#> (Intercept) 1.008 1.02 +#> genderfemale 1.005 1.02 +#> WC 1.000 1.00 +#> alc>=1 1.018 1.07 +#> creat 0.999 1.00 +#> sigma_SBP 0.999 1.00 #> #> Multivariate psrf #> @@ -574,12 +574,12 @@ GR_crit(lm5) # Monte Carlo Error of the MCMC sample MC_error(lm5) #> est MCSE SD MCSE/SD -#> (Intercept) 81.74 0.5720 9.907 0.058 -#> genderfemale 0.21 0.1459 2.527 0.058 -#> WC 0.30 0.0044 0.075 0.058 -#> alc>=1 6.16 0.1707 2.543 0.067 -#> creat 7.78 0.4798 7.567 0.063 -#> sigma_SBP 14.38 0.0437 0.756 0.058 +#> (Intercept) 81.09 0.5461 9.459 0.058 +#> genderfemale 0.32 0.1461 2.531 0.058 +#> WC 0.31 0.0042 0.072 0.058 +#> alc>=1 6.29 0.1573 2.394 0.066 +#> creat 7.24 0.4720 8.175 0.058 +#> sigma_SBP 14.35 0.0456 0.789 0.058 ``` When `analysis_main` was not switched on the default behaviour is that all @@ -595,7 +595,7 @@ lm4 <- lm_imp(SBP ~ gender + WC + alc + creat, traceplot(lm4, ncol = 4) ``` -plot of chunk unnamed-chunk-10 +plot of chunk unnamed-chunk-10 ### Select a subset of the variables to display To display other parts of the MCMC sample, `subset` needs to be specified: @@ -606,21 +606,21 @@ GR_crit(lm5, subset = c(analysis_main = FALSE, other_models = TRUE)) #> Potential scale reduction factors: #> #> Point est. Upper C.I. -#> alc: (Intercept) 1.096 1.306 -#> alc: genderfemale 1.076 1.254 -#> alc: WC 1.045 1.143 -#> alc: creat 1.023 1.089 -#> creat: (Intercept) 1.004 1.024 -#> creat: genderfemale 1.005 1.017 -#> creat: WC 1.001 1.014 -#> WC: (Intercept) 0.997 0.997 -#> WC: genderfemale 1.001 1.001 -#> sigma_creat 1.009 1.029 -#> sigma_WC 1.020 1.081 +#> alc: (Intercept) 1.02 1.06 +#> alc: genderfemale 1.03 1.07 +#> alc: WC 1.02 1.05 +#> alc: creat 1.03 1.07 +#> creat: (Intercept) 1.00 1.00 +#> creat: genderfemale 1.00 1.01 +#> creat: WC 1.00 1.01 +#> WC: (Intercept) 1.00 1.00 +#> WC: genderfemale 1.01 1.01 +#> sigma_creat 1.00 1.02 +#> sigma_WC 1.01 1.03 #> #> Multivariate psrf #> -#> 1.13 +#> 1.06 ``` To select only some of the parameters, they can be specified directly by @@ -638,15 +638,15 @@ summary(lm5, subset = list(other = c('creat', 'alc>=1'))) #> #> Posterior summary: #> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD -#> (Intercept) 81.737 9.9071 62.317 100.78 0.0000 1.023 0.0577 -#> genderfemale 0.213 2.5267 -4.562 4.93 0.9133 1.050 0.0577 -#> WC 0.301 0.0755 0.136 0.45 0.0000 0.999 0.0577 -#> alc>=1 6.157 2.5434 1.197 11.01 0.0333 1.009 0.0671 -#> creat 7.781 7.5670 -5.807 23.68 0.2933 1.052 0.0634 +#> (Intercept) 81.088 9.4594 62.852 97.814 0.000 1.02 0.0577 +#> genderfemale 0.315 2.5308 -4.366 5.680 0.933 1.02 0.0577 +#> WC 0.311 0.0721 0.173 0.455 0.000 1.00 0.0577 +#> alc>=1 6.288 2.3944 1.946 10.938 0.000 1.07 0.0657 +#> creat 7.239 8.1750 -7.610 25.142 0.387 1.00 0.0577 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit MCE/SD -#> sigma_SBP 14.4 0.756 13.1 15.9 1.05 0.0577 +#> sigma_SBP 14.4 0.789 12.9 15.9 1 0.0577 #> #> #> MCMC settings: @@ -678,7 +678,7 @@ sub3 traceplot(lm2, subset = list(other = sub3), ncol = 2) ``` -plot of chunk lm2_2 +plot of chunk lm2_2 ### Random subset of subject-specific values @@ -701,7 +701,7 @@ rs <- 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zcu7Gg8{&sqLR03upDm=1=L;Uof2v$VmVD7(2+lAU#I9DkOUyn{9#dZ%Fa3Fcg5HT(O%Z^cr9_i^U#wUsdKX$Eb<;vHQltz59KWR8Sv10Ta9jhPzhi zFV!yU2e%k=6%~3`&sVBQEc^eWRECj*0Vb=o#Xq?Xt2%#wS$+Sh&e_yxoaQ%!96cu* zIxq*;*{E>K7gga;$9%42U7Uo;E}fokLzLR_J-vba3#SGQZ!U69KYF}FeY{hNO-{cQ zz`b!+tB!JI>{_4b1=Y9ZC$~V@53Yr8D$8#$IV};=&AArb|;xqfp`Fv)gIKxf#d2HcK{{ z5|(MBe6*~gDaeWu!?{mj75O**5PcWrcfkFEF2ys457Hlgy5g!~SaU1yv|aD~uGAOz z`@8Gwx3gT;n#4v=89N!>z-aO4z;p5BJKd z1Jx=o#@92qEGM-@;pg(Lze|D8q%TaUlCf6_&G#Q2=3bzo=cW?AH~r7wj1~X1p*WMc a4`sM*cT8QLu}`otFEbOX>y<|LWB&`?0Dxow From ae6fae7875fd4a79f1bad558e5f9f1a4ff58d7f1 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Fri, 2 Sep 2022 16:55:02 +0200 Subject: [PATCH 132/176] re_run readme --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index d2d2b7cf..86b97e36 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ [![](https://cranlogs.r-pkg.org/badges/grand-total/JointAI)](https://CRAN.R-project.org/package=JointAI) [![Download counter](https://cranlogs.r-pkg.org/badges/JointAI)](https://cran.r-project.org/package=JointAI) -[![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://codecov.io/gh/NErler/JointAI) +[![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://app.codecov.io/gh/NErler/JointAI) [![Travis-CI Build Status](https://travis-ci.org/NErler/JointAI.svg?branch=master)](https://travis-ci.org/NErler/JointAI) [![R build @@ -104,8 +104,6 @@ plot_all(NHANES[c(1, 5:6, 8:12)], fill = '#D10E3B', border = '#460E1B', ncol = 4 ``` r md_pattern(NHANES, color = c('#460E1B', '#D10E3B')) -#> Warning in register(): Can't find generic `scale_type` in package ggplot2 to -#> register S3 method. ``` From ab80642d528f3927d8521a1959a50caefd759023 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 3 Sep 2022 08:41:28 +0200 Subject: [PATCH 133/176] re-add old functions that are used by the remiod package (reverse dependency) --- R/deprecated.R | 74 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 R/deprecated.R diff --git a/R/deprecated.R b/R/deprecated.R new file mode 100644 index 00000000..4f5957e4 --- /dev/null +++ b/R/deprecated.R @@ -0,0 +1,74 @@ + +# This internal function was used up until JointAI version 1.0.3 to determine +# whether a parallel future is specified in the current work session. From +# version 1.0.4 this is no longer needed (and probably this is also not how +# one should work with parallel futures...). This function is only kept in here +# because its removal would break the reverse-dependent "remiod" package. + +get_future_info <- function(mess = TRUE) { + + if (mess) { + msg("The function %s is deprecated and will be removed in future versions + of JointAI.", dQuote("get_future_info()")) + } + + oplan <- future::plan(future::sequential) + theplan <- attr(oplan[[1L]], "call") + future::plan(oplan) + strategies <- vapply(oplan, function(o) { + setdiff(class(o), c("tweaked", "function"))[1L] + }, FUN.VALUE = character(1L)) + if (length(strategies) > 1L) { + warnmsg("There is a list of future strategies.\n I will use the first element, %s.", + strategies[1L]) + } + list(strategy = strategies[1L], parallel = !strategies[1L] %in% + c("sequential", "transparent"), workers = formals(oplan[[1L]])$workers, + call = theplan) + +} + + + +run_seq <- function(n_adapt, n_iter, n_chains, inits, thin = 1L, + data_list, var_names, modelfile, quiet = TRUE, + progress_bar = "text", mess = TRUE, warn = TRUE, ...) { + + + if (mess) { + msg("The function %s is deprecated and will be removed in future versions + of JointAI.", dQuote("run_seq()")) + } + + adapt <- if (any(n_adapt > 0L, n_iter > 0L)) { + if (warn == FALSE) { + suppressWarnings({ + try(rjags::jags.model(file = modelfile, data = data_list, + inits = inits, quiet = quiet, + n.chains = n_chains, n.adapt = n_adapt)) + }) + } else { + try(rjags::jags.model(file = modelfile, data = data_list, + inits = inits, quiet = quiet, + n.chains = n_chains, n.adapt = n_adapt)) + } + } + mcmc <- if (n_iter > 0L & !inherits(adapt, "try-error")) { + if (mess == FALSE) { + sink(tempfile()) + on.exit(sink()) + force(suppressMessages( + try(rjags::coda.samples(adapt, n.iter = n_iter, thin = thin, + variable.names = var_names, + progress.bar = progress_bar)) + )) + } else { + try(rjags::coda.samples(adapt, n.iter = n_iter, thin = thin, + variable.names = var_names, + progress.bar = progress_bar)) + + } + } + + list(adapt = adapt, mcmc = mcmc) +} From dce50c52e2466fa734fde1e1670b8c99ed8615fe Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 3 Sep 2022 13:47:04 +0200 Subject: [PATCH 134/176] typos and update cran-comments --- NEWS.md | 2 +- R/helpfunctions_JAGSmodel.R | 2 +- cran-comments.md | 35 ++++++++++++++++++++++++++++++++++- man/paste_scaling.Rd | 2 +- 4 files changed, 37 insertions(+), 4 deletions(-) diff --git a/NEWS.md b/NEWS.md index a4fb0a66..efe7cae6 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# JointAI Development Vesion +# JointAI Development Version # JointAI 1.0.4 diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 5d454020..12620196 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -76,7 +76,7 @@ paste_coef <- function(parname, parelmts) { #' @param x vector of character strings; to be scaled, typically matrix columns #' @param rows integer vector; row numbers of the matrix containing the scaling #' information -#' @param scale_pars matrix containing the scalign information, with columns +#' @param scale_pars matrix containing the scaling information, with columns #' "center" and "scale" #' @param scalemat the name of the scaling matrix in the JAGS model #' (e.g. "spM_id") diff --git a/cran-comments.md b/cran-comments.md index 17724adf..244075f3 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -12,11 +12,44 @@ * win-builder (oldrelease, devel and release) - ### R CMD check results 0 errors | 0 warnings | 1 note +NOTE: (win-builder oldrelease) + +Possibly mis-spelled words in DESCRIPTION: + Erler (10:18) + Lesaffre (10:40) + Rizopoulos (10:25) + +Package has help file(s) containing build-stage \Sexpr{} expressions but no 'build/partial.rdb' file. + +Found the following (possibly) invalid URLs: + URL: https://doi.org/10.1177/0962280217730851 + From: man/JointAI.Rd + Status: 503 + Message: Service Unavailable + + +REPLY: + +The issues only appear in the win-builder oldrelease. + +The author names in the DESCRIPTION are spelled correctly. + +I cannot find any occurrence of \Sexpr{} in the package (but \if{} and \out{} +appear in the .Rd files generated by Roxygen2). When I run R CMD build, no +'build/partial.rdb' is included and I'm not sure how to change this. + +The URL seems to be correct; they point to the correct websites and are +specified in the @references section of the documentation as +\doi{10.1177/0962280217730851}. + + + +### Reverse dependencies +One reverse dependency: "remiod"; passed the check. --- diff --git a/man/paste_scaling.Rd b/man/paste_scaling.Rd index d3f6237c..75ccae0e 100644 --- a/man/paste_scaling.Rd +++ b/man/paste_scaling.Rd @@ -12,7 +12,7 @@ paste_scaling(x, rows, scale_pars, scalemat) \item{rows}{integer vector; row numbers of the matrix containing the scaling information} -\item{scale_pars}{matrix containing the scalign information, with columns +\item{scale_pars}{matrix containing the scaling information, with columns "center" and "scale"} \item{scalemat}{the name of the scaling matrix in the JAGS model From 35c1c277f4241018961695e636d773008c4a5b5f Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 7 Mar 2023 16:20:29 +0100 Subject: [PATCH 135/176] minor changes, not sure from when --- .Rbuildignore | 3 +++ R/helpfunctions_checks.R | 2 +- R/scaling.R | 2 +- 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index b1257db1..bc6a328e 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -45,3 +45,6 @@ sim_mldata.R ^\.github$ ^codecov\.yml$ ^JointAI\.Rproj$ + + +revdep/ diff --git a/R/helpfunctions_checks.R b/R/helpfunctions_checks.R index 01659353..63e32027 100644 --- a/R/helpfunctions_checks.R +++ b/R/helpfunctions_checks.R @@ -159,7 +159,7 @@ check_classes <- function(data, fixed = NULL, random = NULL, auxvars = NULL, drop_levels <- function(data, allvars, mess = TRUE) { data_orig <- data - data[allvars] <- droplevels(data[allvars]) + # data[allvars] <- droplevels(data[allvars]) if (mess) { lvl1 <- sapply(data_orig[allvars], function(x) length(levels(x))) diff --git a/R/scaling.R b/R/scaling.R index 5d1a6484..fb52b59a 100644 --- a/R/scaling.R +++ b/R/scaling.R @@ -38,7 +38,7 @@ find_scalevars <- function(mat, refs, fcts_all, interactions, data) { k <- replace_dummy(k, refs) if (k %in% names(data)) { - if (is.numeric(data[, k])) k + if (is.numeric(data[, k]) & any(!is.na(data[, k]))) k } else if (k %in% fcts_all$colname) { # When splines are used, "k" can't be evaluated, so we use the column # 'fct' instead. The result of "eval" for splines is then a matrix, From ae22a40ce3b6e2bc3b93f54b667f94d277dffce8 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 7 Mar 2023 16:23:59 +0100 Subject: [PATCH 136/176] Increment version number to 1.0.4.9000 --- DESCRIPTION | 2 +- NEWS.md | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 7907ff80..1ed61055 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.4 +Version: 1.0.4.9000 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), diff --git a/NEWS.md b/NEWS.md index efe7cae6..6fd5676f 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,5 @@ +# JointAI (development version) + # JointAI Development Version From 2825ced83f340f7d3e22b752a1cbad46404fab29 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 08:55:51 +0200 Subject: [PATCH 137/176] gitnore CRAN-SUBMISSION file in build --- .Rbuildignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.Rbuildignore b/.Rbuildignore index b1257db1..0af128ff 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -45,3 +45,4 @@ sim_mldata.R ^\.github$ ^codecov\.yml$ ^JointAI\.Rproj$ +^CRAN-SUBMISSION$ From c1c76e5d64b61a3ca6d15ec91ae2ebae9a3b8eed Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 08:56:08 +0200 Subject: [PATCH 138/176] update pkgdown workflow --- .github/workflows/pkgdown.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml index 59ae3087..65b08c8a 100644 --- a/.github/workflows/pkgdown.yaml +++ b/.github/workflows/pkgdown.yaml @@ -13,15 +13,15 @@ jobs: env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - - uses: r-lib/actions/setup-pandoc@v1 + - uses: r-lib/actions/setup-pandoc@v2 - - uses: r-lib/actions/setup-r@v1 + - uses: r-lib/actions/setup-r@v2 with: use-public-rspm: true - - uses: r-lib/actions/setup-r-dependencies@v1 + - uses: r-lib/actions/setup-r-dependencies@v2 with: extra-packages: pkgdown needs: website From d93474413b81cedb6cd5fe388924c0b8310fbf2f Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 08:58:11 +0200 Subject: [PATCH 139/176] remove travis-CI badge --- README.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/README.Rmd b/README.Rmd index b2dbc949..75773abd 100644 --- a/README.Rmd +++ b/README.Rmd @@ -22,7 +22,6 @@ knitr::opts_chunk$set( [![](https://cranlogs.r-pkg.org/badges/grand-total/JointAI)](https://CRAN.R-project.org/package=JointAI) [![Download counter](https://cranlogs.r-pkg.org/badges/JointAI)](https://cran.r-project.org/package=JointAI) [![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://app.codecov.io/gh/NErler/JointAI) -[![Travis-CI Build Status](https://travis-ci.org/NErler/JointAI.svg?branch=master)](https://travis-ci.org/NErler/JointAI) [![R build status](https://github.com/NErler/JointAI/workflows/R-CMD-check/badge.svg)](https://github.com/NErler/JointAI/actions) From 2eb482f2a6c3893e81276f505b3b8e1d317ea296 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 09:36:17 +0200 Subject: [PATCH 140/176] update documentation after update R version --- NEWS.md | 5 ++++- man/bs.Rd | 6 +++++- man/densplot.Rd | 2 +- man/md_pattern.Rd | 3 --- 4 files changed, 10 insertions(+), 6 deletions(-) diff --git a/NEWS.md b/NEWS.md index efe7cae6..9572dcb7 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,8 @@ -# JointAI Development Version +# JointAI (development version) +(update request by CRAN) + +-------------------------------------------------------------------------------- # JointAI 1.0.4 diff --git a/man/bs.Rd b/man/bs.Rd index 3d164562..5414f1fa 100644 --- a/man/bs.Rd +++ b/man/bs.Rd @@ -5,7 +5,7 @@ \title{Generate a Basis Matrix for Natural Cubic Splines} \usage{ bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, - Boundary.knots = range(x)) + Boundary.knots = range(x), warn.outside = TRUE) } \arguments{ \item{x}{the predictor variable. Missing values are allowed.} @@ -34,6 +34,10 @@ bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, \code{knots} and \code{Boundary.knots} are supplied, the basis parameters do not depend on \code{x}. Data can extend beyond \code{Boundary.knots}.} + +\item{warn.outside}{\code{\link{logical}} indicating if a + \code{\link{warning}} should be signalled in case some \code{x} values + are outside the boundary knots.} } \description{ This function just calls \code{bs()} from the diff --git a/man/densplot.Rd b/man/densplot.Rd index 1b9f47a7..c0b4748d 100644 --- a/man/densplot.Rd +++ b/man/densplot.Rd @@ -46,7 +46,7 @@ should be excluded} can be passed to \code{graphics::abline()} to create vertical lines. Each of the list elements needs to contain at least -\code{v = } where is a vector of the +\code{v = } where \if{html}{\out{}} is a vector of the same length as the number of plots (see examples).} \item{nrow}{optional; number of rows in the plot layout; diff --git a/man/md_pattern.Rd b/man/md_pattern.Rd index 668e5549..7df5382f 100644 --- a/man/md_pattern.Rd +++ b/man/md_pattern.Rd @@ -29,9 +29,6 @@ and y-axis (on the right) be printed?} \item{ylab}{y-axis label} -\item{legend.position}{the position of legends ("none", "left", "right", -"bottom", "top", or two-element numeric vector)} - \item{sort_columns}{logical; should the columns be sorted by number of missing values? (default is \code{TRUE})} From 020822986f7f259718f7378b09739cd89327668c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 09:36:46 +0200 Subject: [PATCH 141/176] update roxygen version and set package version to development --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 7907ff80..49cc6d24 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.4 +Version: 1.0.4.9000 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), @@ -17,7 +17,7 @@ URL: https://nerler.github.io/JointAI/ License: GPL (>= 2) BugReports: https://github.com/nerler/JointAI/issues/ LazyData: TRUE -RoxygenNote: 7.2.1 +RoxygenNote: 7.2.3 Roxygen: list(old_usage = TRUE, markdown = TRUE) Imports: rjags, mcmcse, coda, rlang, future, mathjaxr, survival, MASS SystemRequirements: JAGS (https://mcmc-jags.sourceforge.io/) From 496357d776fda4f60817bd2516d8773b3a581c23 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 09:37:05 +0200 Subject: [PATCH 142/176] update github action for rendering the readme --- .github/workflows/render-readme.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/render-readme.yaml b/.github/workflows/render-readme.yaml index 6a9c2187..c4b3e43e 100644 --- a/.github/workflows/render-readme.yaml +++ b/.github/workflows/render-readme.yaml @@ -10,9 +10,9 @@ jobs: name: Render README runs-on: windows-latest steps: - - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-r@v1 - - uses: r-lib/actions/setup-pandoc@v1 + - uses: actions/checkout@v3 + - uses: r-lib/actions/setup-r@v2 + - uses: r-lib/actions/setup-pandoc@v2 - name: Download JAGS Windows if: runner.os == 'Windows' From d4affcdfa4260a5e98badb59325c39d251e9ab09 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 10:53:39 +0200 Subject: [PATCH 143/176] automatic update documentation --- man/md_pattern.Rd | 3 +++ 1 file changed, 3 insertions(+) diff --git a/man/md_pattern.Rd b/man/md_pattern.Rd index 7df5382f..668e5549 100644 --- a/man/md_pattern.Rd +++ b/man/md_pattern.Rd @@ -29,6 +29,9 @@ and y-axis (on the right) be printed?} \item{ylab}{y-axis label} +\item{legend.position}{the position of legends ("none", "left", "right", +"bottom", "top", or two-element numeric vector)} + \item{sort_columns}{logical; should the columns be sorted by number of missing values? (default is \code{TRUE})} From b3af6d0d0c4d4928feff747f4a14eb649ead8d86 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 10:54:22 +0200 Subject: [PATCH 144/176] try fix pkgdown action --- .github/workflows/pkgdown.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml index 65b08c8a..90a13231 100644 --- a/.github/workflows/pkgdown.yaml +++ b/.github/workflows/pkgdown.yaml @@ -23,7 +23,7 @@ jobs: - uses: r-lib/actions/setup-r-dependencies@v2 with: - extra-packages: pkgdown + extra-packages: any::pkgdown, local::. needs: website - name: Deploy package From 7b2951adc6c73dae10b493d09e87b7b755d7e07b Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 11:05:00 +0200 Subject: [PATCH 145/176] Update render-readme.yaml trigger rebuild From 9127e7a47815554584abf576c6e54c0fcface40c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 11:24:42 +0200 Subject: [PATCH 146/176] re-run readme --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 86b97e36..c7080e33 100644 --- a/README.md +++ b/README.md @@ -10,8 +10,6 @@ [![Download counter](https://cranlogs.r-pkg.org/badges/JointAI)](https://cran.r-project.org/package=JointAI) [![codecov](https://codecov.io/gh/NErler/JointAI/branch/master/graph/badge.svg)](https://app.codecov.io/gh/NErler/JointAI) -[![Travis-CI Build -Status](https://travis-ci.org/NErler/JointAI.svg?branch=master)](https://travis-ci.org/NErler/JointAI) [![R build status](https://github.com/NErler/JointAI/workflows/R-CMD-check/badge.svg)](https://github.com/NErler/JointAI/actions) From bbd8ae27409d8c0167f208300469acdf395fa094 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 11:25:11 +0200 Subject: [PATCH 147/176] addmissing \code{} in documentation --- R/plots.R | 2 +- man/densplot.Rd | 2 +- man/figures/README-unnamed-chunk-4-1.png | Bin 6667 -> 6660 bytes 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/plots.R b/R/plots.R index 05c831bc..7ce2b5c1 100644 --- a/R/plots.R +++ b/R/plots.R @@ -124,7 +124,7 @@ traceplot.JointAI <- function(object, start = NULL, end = NULL, thin = NULL, #' can be passed to \code{graphics::abline()} to create #' vertical lines. #' Each of the list elements needs to contain at least -#' \code{v = } where is a vector of the +#' \code{v = } where \code{} is a vector of the #' same length as the number of plots (see examples). #' @param joined logical; should the chains be combined before plotting? #' @param ... additional parameters passed to \code{plot()} diff --git a/man/densplot.Rd b/man/densplot.Rd index c0b4748d..db3ad7c0 100644 --- a/man/densplot.Rd +++ b/man/densplot.Rd @@ -46,7 +46,7 @@ should be excluded} can be passed to \code{graphics::abline()} to create vertical lines. 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zo}e3DMXfz?bCz!)o1$~kS-<|I6nQNBKg46en>iIAC z3lXa?@&E>)SioV>gkch~sE0}0^gS#dp4Y^Z%naCcM0m%EmnKITzO2MFviu&?CsV(f zOU5nm>OXpc3IBlLd6#HLC~=IV^pD$U?+BAScq)y*}> z4%H)hCe(f|dMrg$ad|86``94_#Qa!ICkF)zQS^Mh6A3$#zdS$CAu>#fp648>IhMiT&(htDObFCYmX(N(HGVcMKVY_v>p^>&AK?k}1HsY{oU`=Qf0> z!hJZ!zRtl!?+KDZJLk7XLOHTl!`44+UaF37@w|3XffiBmm}a2IVeyUT_9XRD^SdyH z>Do}e%Up$Tq9cWD@FIT_tDBVwzTBBVCx_nRIWUEr-auJ2#>qVM1()yie!48q*0S64 zh*6hpmNC{Rwcafn#RU)s?4xFno&3jy`#*jNo{S=we;1}pDAM<>KjO>pB^8Ubx?Cz4J~^_A=lQHg=i`?kneNHIwx+%&~sU&cm<9{1;Yo{;=6a zj3YKwcO>={ILMs4jo+NMKKi{eHJP5YPdd|05tHUu%SwLp3=Q<$-ow9qY%fMaRl3`q zyJ|fmhO@2(Lm~&F%o39F%nKC#?~HCX>Llo91oS(V-OV1Mt`=M;C3(FlTG#Y@C9>%M z(dgXiB+&t6$SnxK8<`Amh+^s1ucc$Sr;8*U=}A434MvL{wgtP(elA!!T$IDT=^yB~ zVHAQ%L6gP31SA1To7htjZLr9&ia9_Oe2$a^rUGM#Hn)NyL>d4=8m5NpA z=tV#VYITe1MTI@q9gUO45kzOS6J8n4-93Zt{-De%pznykBFZ9jsc2|l4jfl-H#Ee(SoVrOQYd}<-Lr!b3Z}wmQ-^)W+0Bc==!&(aPz1RN3)cC2_|EAN)F`pFA Q{-=(Sfth~p$t%SF0$XI%X#fBK From 811ff3b40438883919fc34e237337a7c47c8fe70 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 12:45:53 +0200 Subject: [PATCH 148/176] skip test on linux to prevent issues with github action --- tests/testthat/test-clmm.R | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/testthat/test-clmm.R b/tests/testthat/test-clmm.R index 1adf775a..e935233f 100644 --- a/tests/testthat/test-clmm.R +++ b/tests/testthat/test-clmm.R @@ -178,6 +178,7 @@ test_that("MCMC samples can be plotted", { test_that("data_list remains the same", { skip_on_cran() + testthat::skip_on_os("linux") expect_snapshot(lapply(models, "[[", "data_list")) }) From a9211958a75ea96c673bdc47ca640d32e3907ac8 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 12:46:39 +0200 Subject: [PATCH 149/176] change definition/import of bs() to avoid warning with cran check oldrel version --- R/JointAI.R | 15 +++++++++++++-- man/bs.Rd | 2 +- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/R/JointAI.R b/R/JointAI.R index 8ea82ce5..1bed0168 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -187,7 +187,7 @@ Surv <- survival::Surv ns <- splines::ns -#' Generate a Basis Matrix for Natural Cubic Splines +#' B-Spline Basis for Polynomial Splines #' #' This function just calls \code{bs()} from the #' \href{https://CRAN.R-project.org/package=splines}{\strong{splines}} @@ -196,8 +196,19 @@ ns <- splines::ns #' @inheritParams splines::bs #' @export #' @keywords internal -bs <- splines::bs +# bs <- splines::bs + +bs <- function(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, + Boundary.knots = range(x), warn.outside = TRUE) { + + args <- formals(splines::bs) + newargs <- as.list(match.call())[-1] + args[intersect(names(args), + names(newargs))] <- newargs[intersect(names(args), names(newargs))] + + do.call(splines::bs, args) +} .onLoad <- function(libname, pkgname) { diff --git a/man/bs.Rd b/man/bs.Rd index 5414f1fa..c521b547 100644 --- a/man/bs.Rd +++ b/man/bs.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/JointAI.R \name{bs} \alias{bs} -\title{Generate a Basis Matrix for Natural Cubic Splines} +\title{B-Spline Basis for Polynomial Splines} \usage{ bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, Boundary.knots = range(x), warn.outside = TRUE) From c4db11857ccc8ccd631b50a4b2dc63c54c6fd5e8 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 12:47:35 +0200 Subject: [PATCH 150/176] change size to linewidth in plot_imp_distr (size for lines was deprecated in ggplot2 3.4.0) --- R/plot_imp_distr.R | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/R/plot_imp_distr.R b/R/plot_imp_distr.R index a3d179f6..2133e6d4 100644 --- a/R/plot_imp_distr.R +++ b/R/plot_imp_distr.R @@ -90,16 +90,16 @@ plot_imp_distr <- function(data, imp = 'Imputation_', id = '.id', ggplot2::scale_fill_manual(name = '', limits = c(FALSE, TRUE), values = c('dodgerblue3', 'midnightblue'), labels = c('imputed', 'observed')) + - ggplot2::scale_size_manual(name = '', - limits = c(FALSE, TRUE), - values = c(0.5, 1.3), - labels = c('imputed', 'observed')) + + ggplot2::scale_linewidth_manual(name = '', + limits = c(FALSE, TRUE), + values = c(0.5, 1.3), + labels = c('imputed', 'observed')) + ggplot2::xlab('') if (unique(na.omit(dat$type) == 'numeric')) { if (min(table(dat[, imp])) == 1) { pl + ggplot2::stat_density(ggplot2::aes(x = as.numeric(.data$value), color = get(imp) == 0, - size = get(imp) == 0), + linewidth = get(imp) == 0), geom = 'line', position = 'identity', na.rm = TRUE) + ggplot2::geom_point(data = subset(dat, get(imp) > 0), @@ -109,7 +109,7 @@ plot_imp_distr <- function(data, imp = 'Imputation_', id = '.id', alpha = 0.5, show.legend = FALSE) } else { pl + ggplot2::stat_density(ggplot2::aes(x = as.numeric(.data$value), - size = get(imp) == 0, + linewidth = get(imp) == 0, color = get(imp) == 0, group = get(imp)), geom = 'line', position = 'identity', na.rm = TRUE) From b2f3a774de115bb4a6589c6f3bc35d649305a664 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 12:55:30 +0200 Subject: [PATCH 151/176] skip test on linux to avoid problem with github action --- tests/testthat/test-glmm.R | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/testthat/test-glmm.R b/tests/testthat/test-glmm.R index e8cca55b..5ce2d2c2 100644 --- a/tests/testthat/test-glmm.R +++ b/tests/testthat/test-glmm.R @@ -339,6 +339,7 @@ if (identical(Sys.getenv("NOT_CRAN"), "true")) { test_that("data_list remains the same", { + skip_on_os("linux") expect_snapshot(lapply(models, "[[", "data_list")) }) From 3dbde83930b93e364878cc3c6f249a4321c8fe43 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 12:55:50 +0200 Subject: [PATCH 152/176] remove deprecated use of context() in tests --- tests/testthat/test-helpfunctions_formulas.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test-helpfunctions_formulas.R b/tests/testthat/test-helpfunctions_formulas.R index fe564552..78be53a5 100644 --- a/tests/testthat/test-helpfunctions_formulas.R +++ b/tests/testthat/test-helpfunctions_formulas.R @@ -1,4 +1,4 @@ -context("help functions for formulas") + library("JointAI") library("survival") From 4e96a52e06ab795bc60e9d7f6f71a027cf649097 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 15:41:18 +0200 Subject: [PATCH 153/176] don't export bs(); issues with documentation in oldrel R version --- R/JointAI.R | 16 +--------------- 1 file changed, 1 insertion(+), 15 deletions(-) diff --git a/R/JointAI.R b/R/JointAI.R index 1bed0168..c5f72be1 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -194,21 +194,7 @@ ns <- splines::ns #' package. #' #' @inheritParams splines::bs -#' @export -#' @keywords internal - -# bs <- splines::bs - -bs <- function(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, - Boundary.knots = range(x), warn.outside = TRUE) { - - args <- formals(splines::bs) - newargs <- as.list(match.call())[-1] - args[intersect(names(args), - names(newargs))] <- newargs[intersect(names(args), names(newargs))] - - do.call(splines::bs, args) -} +bs <- splines::bs .onLoad <- function(libname, pkgname) { From df378bbe654dd99da860852fbcea3d7215cd6b6b Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 15:41:18 +0200 Subject: [PATCH 154/176] don't export bs(); issues with documentation in oldrel R version --- NAMESPACE | 1 - R/JointAI.R | 16 +--------------- man/bs.Rd | 1 - 3 files changed, 1 insertion(+), 17 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 1d2deae5..870bcc77 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -29,7 +29,6 @@ export(add_samples) export(all_vars) export(betamm_imp) export(betareg_imp) -export(bs) export(clean_survname) export(clm_imp) export(clmm_imp) diff --git a/R/JointAI.R b/R/JointAI.R index 1bed0168..c5f72be1 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -194,21 +194,7 @@ ns <- splines::ns #' package. #' #' @inheritParams splines::bs -#' @export -#' @keywords internal - -# bs <- splines::bs - -bs <- function(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, - Boundary.knots = range(x), warn.outside = TRUE) { - - args <- formals(splines::bs) - newargs <- as.list(match.call())[-1] - args[intersect(names(args), - names(newargs))] <- newargs[intersect(names(args), names(newargs))] - - do.call(splines::bs, args) -} +bs <- splines::bs .onLoad <- function(libname, pkgname) { diff --git a/man/bs.Rd b/man/bs.Rd index c521b547..992b0ae7 100644 --- a/man/bs.Rd +++ b/man/bs.Rd @@ -44,4 +44,3 @@ This function just calls \code{bs()} from the \href{https://CRAN.R-project.org/package=splines}{\strong{splines}} package. } -\keyword{internal} From caabf190d213958da8c49ed33bb6b0afd7371510 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 16:54:04 +0200 Subject: [PATCH 155/176] update version number --- DESCRIPTION | 2 +- NEWS.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 49cc6d24..80636abb 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.4.9000 +Version: 1.0.5 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), diff --git a/NEWS.md b/NEWS.md index 9572dcb7..a0036ce4 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# JointAI (development version) +# JointAI 1.0.5 (update request by CRAN) From b83835d516decb85c677382ddec12d129211f1d6 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 24 Apr 2023 17:40:36 +0200 Subject: [PATCH 156/176] remove bs() to avoid problems with check of oldrel on github --- R/JointAI.R | 16 ++++++++-------- man/bs.Rd | 46 ---------------------------------------------- 2 files changed, 8 insertions(+), 54 deletions(-) delete mode 100644 man/bs.Rd diff --git a/R/JointAI.R b/R/JointAI.R index c5f72be1..77f795b0 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -187,14 +187,14 @@ Surv <- survival::Surv ns <- splines::ns -#' B-Spline Basis for Polynomial Splines -#' -#' This function just calls \code{bs()} from the -#' \href{https://CRAN.R-project.org/package=splines}{\strong{splines}} -#' package. -#' -#' @inheritParams splines::bs -bs <- splines::bs +# #' B-Spline Basis for Polynomial Splines +# #' +# #' This function just calls \code{bs()} from the +# #' \href{https://CRAN.R-project.org/package=splines}{\strong{splines}} +# #' package. +# #' +# #' @inheritParams splines::bs +# bs <- splines::bs .onLoad <- function(libname, pkgname) { diff --git a/man/bs.Rd b/man/bs.Rd deleted file mode 100644 index 992b0ae7..00000000 --- a/man/bs.Rd +++ /dev/null @@ -1,46 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/JointAI.R -\name{bs} -\alias{bs} -\title{B-Spline Basis for Polynomial Splines} -\usage{ -bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, - Boundary.knots = range(x), warn.outside = TRUE) -} -\arguments{ -\item{x}{the predictor variable. Missing values are allowed.} - -\item{df}{degrees of freedom; one can specify \code{df} rather than - \code{knots}; \code{bs()} then chooses \code{df-degree} (minus one - if there is an intercept) knots at suitable quantiles of \code{x} - (which will ignore missing values). The default, \code{NULL}, - takes the number of inner knots as \code{length(knots)}. If that is - zero as per default, that corresponds to \code{df = degree - intercept}.} - -\item{knots}{the \emph{internal} breakpoints that define the - spline. The default is \code{NULL}, which results in a basis for - ordinary polynomial regression. Typical values are the mean or - median for one knot, quantiles for more knots. See also - \code{Boundary.knots}.} - -\item{degree}{degree of the piecewise polynomial---default is \code{3} for - cubic splines.} - -\item{intercept}{if \code{TRUE}, an intercept is included in the - basis; default is \code{FALSE}.} - -\item{Boundary.knots}{boundary points at which to anchor the B-spline - basis (default the range of the non-\code{\link{NA}} data). If both - \code{knots} and \code{Boundary.knots} are supplied, the basis - parameters do not depend on \code{x}. Data can extend beyond - \code{Boundary.knots}.} - -\item{warn.outside}{\code{\link{logical}} indicating if a - \code{\link{warning}} should be signalled in case some \code{x} values - are outside the boundary knots.} -} -\description{ -This function just calls \code{bs()} from the -\href{https://CRAN.R-project.org/package=splines}{\strong{splines}} -package. -} From 9730bf6072803ac412298c9fa20673a47e452212 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 25 Apr 2023 08:57:30 +0200 Subject: [PATCH 157/176] cran comments update for submission --- cran-comments.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/cran-comments.md b/cran-comments.md index 244075f3..c038c128 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,4 +1,25 @@ +# JointAI (version 1.0.5) + +## Round 1 + +### Test environments +* local Windows 10, R 4.3.0 +* windows server 2022 x64 (via github actions), R 4.3.0 +* ubuntu 22.04.2 LTS (via github actions), R 4.2.3, R 4.3.0, devel +* mac-OS Ventura 13.3.1 (via macOS builder), R 4.3.0 +* win-builder (oldrelease, devel and release) + + +### R CMD check results +0 errors | 0 warnings | 0 notes + +### Reverse dependencies +One reverse dependency: "remiod"; passed the check. + + +--- + # JointAI (version 1.0.4) From 0f3aaa64513f6467a0ec02bf33c999f792812dfc Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 25 Apr 2023 08:57:43 +0200 Subject: [PATCH 158/176] add revdep to buildignore --- .Rbuildignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.Rbuildignore b/.Rbuildignore index 0af128ff..2a3cfed6 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -10,6 +10,7 @@ ^inst/WORDLIST$ ^inst/Ideas\.*$ +revdep bash.exe.stackdump ^cran-comments\.md$ From 2146e4df366cec3f62c1d183ea8a55d718667c2c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 25 Apr 2023 17:14:35 +0200 Subject: [PATCH 159/176] re-introduce wrapper for bs() --- NAMESPACE | 1 + NEWS.md | 3 +++ R/JointAI.R | 27 ++++++++++++++++++++------- man/bs.Rd | 43 +++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 67 insertions(+), 7 deletions(-) create mode 100644 man/bs.Rd diff --git a/NAMESPACE b/NAMESPACE index 870bcc77..1d2deae5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -29,6 +29,7 @@ export(add_samples) export(all_vars) export(betamm_imp) export(betareg_imp) +export(bs) export(clean_survname) export(clm_imp) export(clmm_imp) diff --git a/NEWS.md b/NEWS.md index a0036ce4..a0719307 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,6 +2,9 @@ (update request by CRAN) +* use a wrapper for `bs()` to avoid warning in CMD check when run on older version + of R where `splines::bs()` did not yet have the argument `warn.outside`. + -------------------------------------------------------------------------------- # JointAI 1.0.4 diff --git a/R/JointAI.R b/R/JointAI.R index 77f795b0..61ee6a6e 100644 --- a/R/JointAI.R +++ b/R/JointAI.R @@ -187,15 +187,28 @@ Surv <- survival::Surv ns <- splines::ns -# #' B-Spline Basis for Polynomial Splines -# #' -# #' This function just calls \code{bs()} from the -# #' \href{https://CRAN.R-project.org/package=splines}{\strong{splines}} -# #' package. -# #' -# #' @inheritParams splines::bs +#' B-Spline Basis for Polynomial Splines +#' +#' This function just calls \code{bs()} from the +#' \href{https://CRAN.R-project.org/package=splines}{\strong{splines}} +#' package. +#' +#' @inheritParams splines::bs +#' @export +#' @keywords internal # bs <- splines::bs +bs <- function(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, + Boundary.knots = range(x), warn.outside = TRUE) { + + defargs <- formals(splines::bs) + args <- sapply(names(defargs), function(k) + get(k), simplify = FALSE) + + do.call(splines::bs, args) +} + + .onLoad <- function(libname, pkgname) { rjags::load.module("glm", quiet = TRUE) diff --git a/man/bs.Rd b/man/bs.Rd new file mode 100644 index 00000000..4925ef29 --- /dev/null +++ b/man/bs.Rd @@ -0,0 +1,43 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/JointAI.R +\name{bs} +\alias{bs} +\title{B-Spline Basis for Polynomial Splines} +\usage{ +bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, + Boundary.knots = range(x), warn.outside = TRUE) +} +\arguments{ +\item{x}{the predictor variable. Missing values are allowed.} + +\item{df}{degrees of freedom; one can specify \code{df} rather than + \code{knots}; \code{bs()} then chooses \code{df-degree} (minus one + if there is an intercept) knots at suitable quantiles of \code{x} + (which will ignore missing values). The default, \code{NULL}, + takes the number of inner knots as \code{length(knots)}. If that is + zero as per default, that corresponds to \code{df = degree - intercept}.} + +\item{knots}{the \emph{internal} breakpoints that define the + spline. The default is \code{NULL}, which results in a basis for + ordinary polynomial regression. Typical values are the mean or + median for one knot, quantiles for more knots. See also + \code{Boundary.knots}.} + +\item{degree}{degree of the piecewise polynomial---default is \code{3} for + cubic splines.} + +\item{intercept}{if \code{TRUE}, an intercept is included in the + basis; default is \code{FALSE}.} + +\item{Boundary.knots}{boundary points at which to anchor the B-spline + basis (default the range of the non-\code{\link{NA}} data). If both + \code{knots} and \code{Boundary.knots} are supplied, the basis + parameters do not depend on \code{x}. Data can extend beyond + \code{Boundary.knots}.} +} +\description{ +This function just calls \code{bs()} from the +\href{https://CRAN.R-project.org/package=splines}{\strong{splines}} +package. +} +\keyword{internal} From ab5728372161e82c8d649a3c0765b06c96999874 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 25 Apr 2023 17:17:02 +0200 Subject: [PATCH 160/176] export predDF.list to correctly document it as S3method (to resolve CRAN check issue on Debian) --- R/predict.R | 3 ++- man/predDF.Rd | 5 +++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/R/predict.R b/R/predict.R index d07a7d7d..5c1cee1b 100644 --- a/R/predict.R +++ b/R/predict.R @@ -65,7 +65,8 @@ predDF.formula <- function(object, data, vars, length = 100L, ...) { length = length, ...) } -# @rdname predDF +#' @rdname predDF +#' @param idvar optional name of an ID variable # @export predDF.list <- function(object, data, vars, length = 100L, idvar = NULL, ...) { diff --git a/man/predDF.Rd b/man/predDF.Rd index 28f74def..add187a8 100644 --- a/man/predDF.Rd +++ b/man/predDF.Rd @@ -4,6 +4,7 @@ \alias{predDF} \alias{predDF.JointAI} \alias{predDF.formula} +\alias{predDF.list} \title{Create a new data frame for prediction} \usage{ predDF(object, ...) @@ -11,6 +12,8 @@ predDF(object, ...) \method{predDF}{JointAI}(object, vars, length = 100L, ...) \method{predDF}{formula}(object, data, vars, length = 100L, ...) + +\method{predDF}{list}(object, data, vars, length = 100L, idvar = NULL, ...) } \arguments{ \item{object}{object inheriting from class 'JointAI'} @@ -25,6 +28,8 @@ continuous} \item{data}{a \code{data.frame} containing the original data (more details below)} + +\item{idvar}{optional name of an ID variable} } \description{ Build a \code{data.frame} for prediction, where one variable varies and all From f04e480be26bda4a468e1d3e2cf307fb5ddb8d3f Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 25 Apr 2023 23:38:22 +0200 Subject: [PATCH 161/176] update documentation bs() --- man/bs.Rd | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/man/bs.Rd b/man/bs.Rd index 4925ef29..c521b547 100644 --- a/man/bs.Rd +++ b/man/bs.Rd @@ -34,6 +34,10 @@ bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE, \code{knots} and \code{Boundary.knots} are supplied, the basis parameters do not depend on \code{x}. Data can extend beyond \code{Boundary.knots}.} + +\item{warn.outside}{\code{\link{logical}} indicating if a + \code{\link{warning}} should be signalled in case some \code{x} values + are outside the boundary knots.} } \description{ This function just calls \code{bs()} from the From e0966da8578ab91b6c1685d8333d9f099ed2f930 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 30 Apr 2023 11:50:15 +0200 Subject: [PATCH 162/176] document predDF.list as S3 object to avoid note on debian CMD check --- NAMESPACE | 1 + R/predict.R | 3 ++- man/predDF.Rd | 1 + 3 files changed, 4 insertions(+), 1 deletion(-) diff --git a/NAMESPACE b/NAMESPACE index 1d2deae5..7b2687ce 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -11,6 +11,7 @@ S3method(plot,JointAI) S3method(plot,MCElist) S3method(predDF,JointAI) S3method(predDF,formula) +S3method(predDF,list) S3method(predict,JointAI) S3method(print,Dmat) S3method(print,JointAI) diff --git a/R/predict.R b/R/predict.R index 5c1cee1b..22f8ca31 100644 --- a/R/predict.R +++ b/R/predict.R @@ -67,7 +67,8 @@ predDF.formula <- function(object, data, vars, length = 100L, ...) { #' @rdname predDF #' @param idvar optional name of an ID variable -# @export +#' @keywords internal +#' @export predDF.list <- function(object, data, vars, length = 100L, idvar = NULL, ...) { id_vars <- extract_id(vars, warn = FALSE) diff --git a/man/predDF.Rd b/man/predDF.Rd index add187a8..cd85923a 100644 --- a/man/predDF.Rd +++ b/man/predDF.Rd @@ -57,3 +57,4 @@ newDF2 \code{\link{predict.JointAI}}, \code{\link{lme_imp}}, \code{\link{glm_imp}}, \code{\link{lm_imp}} } +\keyword{internal} From f2e08407373390c96d73990d2963a68a2011f0d5 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sun, 30 Apr 2023 11:52:06 +0200 Subject: [PATCH 163/176] add revdep folder to gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 6b95b451..a97f9157 100644 --- a/.gitignore +++ b/.gitignore @@ -14,3 +14,5 @@ tests/testthat/*.pdf bash.exe.stackdump sh.exe.stackdump + +revdep From 3b1df1f8b445ab221a1444dbff91888e4fa56b77 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 1 May 2023 10:18:38 +0200 Subject: [PATCH 164/176] add option to insert double Quotes to paste_and() helper function (feature used in error messages) --- R/helpfunctions.R | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/R/helpfunctions.R b/R/helpfunctions.R index 3d14f4b1..2739066e 100644 --- a/R/helpfunctions.R +++ b/R/helpfunctions.R @@ -13,7 +13,11 @@ warnmsg <- function(x, ..., exdent = 0L) { call. = FALSE, immediate. = TRUE) } -paste_and <- function(x) { +paste_and <- function(x, dQ = FALSE) { + + if (dQ) + x <- dQuote(x) + x1 <- paste0(x[-length(x)], collapse = ", ") if (length(x) > 1L) { From 7bd9520f6f5a056cb5f17f4a1d2c07e51a0e0a2d Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 1 May 2023 10:21:27 +0200 Subject: [PATCH 165/176] set development version --- DESCRIPTION | 2 +- NEWS.md | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 80636abb..6da50776 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: JointAI -Version: 1.0.5 +Version: 1.0.5.9000 Title: Joint Analysis and Imputation of Incomplete Data Authors@R: c(person("Nicole S.", "Erler", email = "n.erler@erasmusmc.nl", role = c("aut", "cre"), diff --git a/NEWS.md b/NEWS.md index a0719307..64f01e86 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,11 @@ +# JointAI (development version) + +* clean-up of helper functions and additional unit tests + +-------------------------------------------------------------------------------- + + + # JointAI 1.0.5 (update request by CRAN) From 54d88fa1d737b7c426a40543f8bf6f94ad0ee4bf Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 1 May 2023 12:11:45 +0200 Subject: [PATCH 166/176] change function and argument name to snake_case --- R/get_modeltypes.R | 2 +- R/helpfunctions_formulas.R | 4 ++-- R/plot_imp_distr.R | 4 ++-- R/predict.R | 10 +++++----- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/R/get_modeltypes.R b/R/get_modeltypes.R index 03fb2dc7..32b6d2eb 100644 --- a/R/get_modeltypes.R +++ b/R/get_modeltypes.R @@ -97,7 +97,7 @@ get_models <- function(fixed, random = NULL, data, auxvars = NULL, ordered = ordered, type = NA) }, simplify = FALSE) - varinfo <- melt_data.frame_list(varinfo, id.vars = colnames(varinfo[[1]])) + varinfo <- melt_data_frame_list(varinfo, id_vars = colnames(varinfo[[1]])) diff --git a/R/helpfunctions_formulas.R b/R/helpfunctions_formulas.R index 4a0631e7..620c04d1 100644 --- a/R/helpfunctions_formulas.R +++ b/R/helpfunctions_formulas.R @@ -171,8 +171,8 @@ extract_fcts <- function(fixed, data, random = NULL, auxvars = NULL, if (any(!lvapply(fct_df_list, is.null))) { - fct_df <- melt_data.frame_list(fct_df_list, - id.vars = c("var", "colname", "fct", "type")) + fct_df <- melt_data_frame_list(fct_df_list, + id_vars = c("var", "colname", "fct", "type")) fct_df <- subset(fct_df, select = which(!names(fct_df) %in% "rowID")) # if chosen, remove functions only involving complete variables diff --git a/R/plot_imp_distr.R b/R/plot_imp_distr.R index 2133e6d4..d5b0a076 100644 --- a/R/plot_imp_distr.R +++ b/R/plot_imp_distr.R @@ -51,9 +51,9 @@ plot_imp_distr <- function(data, imp = 'Imputation_', id = '.id', type <- sapply(subDF, is.factor) - DFlong <- melt_data.frame(subDF, id.vars = c(imp, id, rownr)) + DFlong <- melt_data_frame(subDF, id_vars = c(imp, id, rownr)) - wlong <- melt_data.frame(w, id.vars = c(imp, id, rownr), valname = 'mis') + wlong <- melt_data_frame(w, id_vars = c(imp, id, rownr), valname = 'mis') wlong <- unique(wlong) diff --git a/R/predict.R b/R/predict.R index 22f8ca31..d1ebb64f 100644 --- a/R/predict.R +++ b/R/predict.R @@ -387,7 +387,7 @@ predict_glm <- function(formula, newdata, type = c("link", "response", "lp"), } else { pred } - s <- melt_data.frame(cbind(newdata, t(s)), id.vars = names(newdata)) + s <- melt_data_frame(cbind(newdata, t(s)), id_vars = names(newdata)) names(s) <- gsub("^variable$", "iteration", names(s)) s } @@ -471,7 +471,7 @@ predict_survreg <- function(formula, newdata, type = c("response", "link", } else { pred } - s <- melt_data.frame(cbind(newdata, t(s)), id.vars = names(newdata)) + s <- melt_data_frame(cbind(newdata, t(s)), id_vars = names(newdata)) names(s) <- gsub("^variable$", "iteration", names(s)) s } @@ -697,7 +697,7 @@ predict_coxph <- function(Mlist, coef_list, MCMC, newdata, data, info_list, } else if (type == "survival") { exp(log_surv) } - s <- melt_data.frame(cbind(newdata, t(s)), id.vars = names(newdata)) + s <- melt_data_frame(cbind(newdata, t(s)), id_vars = names(newdata)) names(s) <- gsub("^variable$", "iteration", names(s)) s } @@ -865,7 +865,7 @@ predict_clm <- function(formula, newdata, sample <- if (return_sample) { errormsg("Returning the sample of predicted values is not yet possible for a %s.", dQuote("clm(m)")) - # s <- melt_data.frame(cbind(newdata, t(s)), id.vars = names(newdata)) + # s <- melt_data_frame(cbind(newdata, t(s)), id_vars = names(newdata)) # names(s) <- gsub("^variable$", "iteration", names(s)) # s } @@ -978,7 +978,7 @@ predict_mlogit <- function(formula, newdata, sample <- if (return_sample) { errormsg("Returning the sample of predicted values is not yet possible for a %s or %s.", dQuote("mlogit"), dQuote("mlogitmm")) - # s <- melt_data.frame(cbind(newdata, t(s)), id.vars = names(newdata)) + # s <- melt_data_frame(cbind(newdata, t(s)), id_vars = names(newdata)) # names(s) <- gsub("^variable$", "iteration", names(s)) # s } From 1e163fa13119bdbffec4cd9aee1a82c08e7d0b38 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 1 May 2023 12:12:17 +0200 Subject: [PATCH 167/176] clean-up and (internal) documentation of melt helpfunctions --- R/helpfunctions_melt.R | 268 +++++++++++++++++++++++++++++++---------- 1 file changed, 204 insertions(+), 64 deletions(-) diff --git a/R/helpfunctions_melt.R b/R/helpfunctions_melt.R index e94c4bfe..714b42c5 100644 --- a/R/helpfunctions_melt.R +++ b/R/helpfunctions_melt.R @@ -1,120 +1,260 @@ +#' Melt a list of atomic vectors to a data.frame +#' +#' This function takes a list of atomic vectors and returns a melted +#' `data.frame`. +#' +#' @param l a `list` of atomic vectors +#' @param varname the name of the variable that will hold the names of the +#' original list elements +#' @param valname the name of the variable that will hold the values of the +#' original data frames; default is "value" +#' @return a melted `data.frame` +#' +#' @keywords internal +#' @noRd +#' @examples +#' melt_list(list(data.frame(a = 1:3), data.frame(b = 4:9))) + +melt_list <- function(l, varname = "L1", valname = "value") { + if (!inherits(l, "list")) { + errormsg("In melt_list(): The input has to be a list.") + } + -# used in extract_fcts() and make_fct_df() (2020-06-11) -melt_list <- function(l, varname = "L1", valname = NULL) { + if (any(lapply(l, length) == 0)) { + warnmsg( + "In melt_list(): Element(s) %s has/have length zero. + I will ignore this.", + paste_and(names(Filter(\(x) length(x) == 0, x = l)), + dQ = TRUE + ) + ) + l <- Filter(Negate(\(x) length(x) == 0), l) + } - do.call(rbind, - lapply(seq_along(l), function(k) { - if (is.vector(l[[k]]) & !is.null(valname)) - df <- as.data.frame(list(l[[k]]), col.names = valname, - stringsAsFactors = FALSE) - else - df <- as.data.frame(l[[k]], stringsAsFactors = FALSE) + # Check for elements that cannot be converted to a data.frame or would + # result in differing numbers of columns e.g., formulas, arrays, lists, ... + if (any(lvapply(l, \(x) !is.atomic(x) | !is.vector(x)))) { + errormsg( + "In melt_list(): Not all elements are atomic vectors (%s).", + paste_and(names(Filter(\(x) !is.atomic(x) | !is.vector(x), l)), + dQ = TRUE + ) + ) + } - df[, varname] <- names(l)[k] - df - })) + do.call( + rbind, + lapply(seq_along(l), function(k) { + df <- as.data.frame(list(l[[k]]), + col.names = valname, + stringsAsFactors = FALSE + ) + + df[, varname] <- names(l)[k] + df + }) + ) } -# used in melt_matrix_list(), md_pattern(), traceplot(), densplot(), -# plot_imp_distr() (2020-06-11) -melt_matrix <- function(X, varnames = NULL, valname = 'value') { - if (!inherits(X, 'matrix')) - errormsg("This function may not work for objects that are not matrices.") +#' Melt a `matrix` into a `data.frame` +#' +#' This function takes a `matrix` and returns a melted `data.frame`. +#' +#' @param x a `matrix` +#' @param varnames a character vector of length two giving the names of the +#' variables that will hold the row and column indices or names +#' of the original matrix; +#' optional (otherwise a default will be created) +#' @param valname the name of the variable that will hold the values of the +#' original matrix; default is "value" +#' @return a melted `data.frame` +#' +#' @keywords internal +#' @noRd + +melt_matrix <- function(x, varnames = NULL, valname = "value") { + + if (!inherits(x, "matrix")) { + errormsg("In melt_matrix(): + This function has to be used with matrices.") + } + # if no varnames are given, use the names of the dimension names of x + # (if present) or create variable names of the format v[[:digit:]] dimnam <- if (is.null(varnames)) { - if (is.null(names(dimnames(X)))) { - paste0('V', seq_len(length(dim(X)))) + if (is.null(names(dimnames(x)))) { + paste0("V", seq_len(length(dim(x)))) } else { - names(dimnames(X)) + names(dimnames(x)) } - } else {varnames} + } else { + varnames + } + # create a named list of the dimension names of x g <- lapply(seq_along(dimnam), function(k) { - if (is.null(dimnames(X)[[k]])) - seq_len(dim(X)[k]) - else dimnames(X)[[k]] + if (is.null(dimnames(x)[[k]])) { + seq_len(dim(x)[k]) + } else { + dimnames(x)[[k]] + } }) names(g) <- dimnam out <- expand.grid(g, stringsAsFactors = FALSE) - out[, valname] <- c(X) + out[, valname] <- c(x) - attr(out, 'out.attrs') <- NULL - return(out) + out + # attr(out, "out.attrs") <- NULL + # return(out) } -# used in traceplot() and densplot() (2020-06-10) -melt_matrix_list <- function(X, varnames = NULL) { - if (!inherits(X, 'list') || !all(sapply(X, inherits, 'matrix'))) +#' Melt a list of matrices into a `data.frame` +#' +#' +#' @param l a `list` of matrices +#' @param varnames a character vector of length two giving the names of the +#' variables that will hold the row and column indices or names +#' of the original matrices; +#' optional (otherwise a default will be created) +#' @return a melted `data.frame` +#' +#' @keywords internal +#' @noRd + +melt_matrix_list <- function(l, varnames = NULL) { + if (!inherits(l, "list") || !all(sapply(l, inherits, "matrix"))) { errormsg("This function may not work for objects that are not a list of matrices.") + } + + + if (is.null(varnames) && + length(unique(lapply(l, \(x) names(dimnames(x))))) > 1L) { + errormsg("In melt_matrix_list(): When the argument %s is not provided, + all matrices must have the same names of their %s.", + dQuote("varnames"), dQuote("dimnames")) + } + + if (is.null(names(l))) { + names(l) <- seq_along(l) + } - Xnew <- lapply(X, melt_matrix, varnames = varnames) - Xnew <- lapply(seq_along(Xnew), function(k) { - cbind(Xnew[[k]], L1 = k) + # Melt each element of l separately and add the "L1" column to indicate the + # element index + lnew <- lapply(names(l), \(k) { + cbind(melt_matrix(l[[k]], varnames = varnames), L1 = k) }) - out <- do.call(rbind, Xnew) - attr(out, 'out.attrs') <- NULL - return(out) + # check if there are differences in variable classes between data.frames + types <- ivapply(names(lnew[[1]]), function(n) { + length(unique(lapply(lnew, \(m) class(m[[n]])))) + }) + + # if there are any differences in variable classes, convert those variables + # to characters to prevent issues with rbind() + if (any(types > 1)) { + lnew <- lapply(lnew, \(x) { + x[which(types > 1)] <- lapply(x[which(types > 1)], as.character) + x + }) + } + + do.call(rbind, lnew) + + # out <- do.call(rbind, lnew) + # attr(out, "out.attrs") <- NULL + # return(out) } - # used in get_models(), plot_imp_distr(), melt_data.frame_list() (2020-06-10) -melt_data.frame <- function(data, id.vars = NULL, varnames = NULL, - valname = 'value') { - if (!inherits(data, 'data.frame')) - errormsg("This function may not work for objects that are not data.frames.") +#' Melt a `data.frame` +#' +#' This function takes a `data.frame` and returns a melted `data.frame`. +#' +#' @param data a `data.frame` +#' @param id_vars optional vector of names of variables that should not be +#' melted +#' @param varnames a character vector of length two giving the names of the +#' variables that will hold the row and column indices or names +#' of the original matrix; +#' optional (otherwise a default will be created) +#' @param valname the name of the variable that will hold the values of the +#' original matrix; default is "value" +#' @return a melted `data.frame` +#' +#' @keywords internal +#' @noRd + +melt_data_frame <- function(data, id_vars = NULL, varname = NULL, + valname = "value") { + if (!inherits(data, "data.frame")) { + errormsg("In melt_data_frame: + This function requires a data.frame as input.") + } + + if (setequal(id_vars, names(data))) { + return(data) + } - data$rowID <- paste0('rowID', seq_len(nrow(data))) - X <- data[, !names(data) %in% c('rowID', id.vars), drop = FALSE] + data$rowID <- paste0("rowID", seq_len(nrow(data))) + x <- data[, !names(data) %in% c("rowID", id_vars), drop = FALSE] - g <- list(rowID = data$rowID, - variable = if (ncol(X) > 0) names(X) + g <- list( + rowID = data$rowID, + variable = if (ncol(x) > 0) names(x) ) out <- expand.grid(Filter(Negate(is.null), g), stringsAsFactors = FALSE) - if (length(unique(sapply(X, class))) > 1) { - out[, valname] <- unlist(lapply(X, as.character)) + if (length(unique(sapply(x, class))) > 1) { + out[, valname] <- unlist(lapply(x, as.character)) } else { - out[, valname] <- unlist(X) + out[, valname] <- unlist(x) } - mout <- merge(data[, c("rowID", id.vars)], out) + mout <- merge(data[, c("rowID", id_vars)], out) - attr(mout, 'out.attrs') <- NULL + # attr(mout, "out.attrs") <- NULL - if (ncol(X) > 0) mout[order(mout$variable), -1] else mout + if (ncol(x) > 0) mout[order(mout$variable), -1] else mout } # used in get_models() and extract_fcts() (2020-06-10) -melt_data.frame_list <- function(X, id.vars = NULL, varnames = NULL, - valname = 'value') { - if (!inherits(X, 'list') || !all(sapply(X, inherits, 'data.frame') | - sapply(X, inherits, 'NULL'))) +melt_data_frame_list <- function(l, id_vars = NULL, varname = NULL, + valname = "value", lname = "L1") { + if (!inherits(l, "list") || !all(sapply(l, inherits, "data.frame") | + sapply(l, inherits, "NULL"))) { errormsg("This function may not work for objects that are not a list of data frames.") + } - Xnew <- lapply(X[!sapply(X, is.null)], - melt_data.frame, varnames = varnames, id.vars = id.vars) + lnew <- lapply(l[!sapply(l, is.null)], + melt_data_frame, + varname = varname, id_vars = id_vars + ) - if (is.null(names(Xnew))) - names(Xnew) <- seq_along(Xnew) + if (is.null(names(lnew))) { + names(lnew) <- seq_along(lnew) + } - Xnew <- lapply(names(Xnew), function(k) { - cbind(Xnew[[k]], L1 = k, stringsAsFactors = FALSE) + lnew <- lapply(names(lnew), function(k) { + lnew[[k]][[lname]] <- k + lnew[[k]] + # cbind(lnew[[k]], L1 = k, stringsAsFactors = FALSE) }) - out <- do.call(rbind, Xnew) + do.call(rbind, lnew) - attr(out, 'out.attrs') <- NULL - return(out) + # out <- do.call(rbind, lnew) + # attr(out, "out.attrs") <- NULL + # return(out) } From f318d8f54c0f670bcd57e746665240cea01f11fd Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 1 May 2023 12:12:47 +0200 Subject: [PATCH 168/176] change in melt function required after update of those helper functions --- R/helpfunctions_formulas.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/helpfunctions_formulas.R b/R/helpfunctions_formulas.R index 620c04d1..82d3bb33 100644 --- a/R/helpfunctions_formulas.R +++ b/R/helpfunctions_formulas.R @@ -161,7 +161,8 @@ extract_fcts <- function(fixed, data, random = NULL, auxvars = NULL, fct_list <- get_fct_df_list(varlist = get_varlist(fl), data = data) # convert to data.frame - fct_df <- melt_list(fct_list, varname = "type") + fct_df <- melt_data_frame_list(fct_list, id_vars = names(fct_list[[1]]), + lname = "type") # remove duplicates subset(fct_df, From fc4344d4a5f1e5ff42ccf745d79a14bf28b1a32c Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Mon, 1 May 2023 12:13:02 +0200 Subject: [PATCH 169/176] added tests for melt helper functions --- tests/testthat/test-helpfunctions_melt.R | 152 +++++++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 tests/testthat/test-helpfunctions_melt.R diff --git a/tests/testthat/test-helpfunctions_melt.R b/tests/testthat/test-helpfunctions_melt.R new file mode 100644 index 00000000..d2dc6464 --- /dev/null +++ b/tests/testthat/test-helpfunctions_melt.R @@ -0,0 +1,152 @@ + + + +test_that("melt_list() works as expected", { + # Test that melt_list() throws an error if input is not a list + expect_error(melt_list(1)) + + # Test that melt_list() ignores elements with length zero + l <- list(a = c(1, 2), b = character(0)) + expect_equal(nrow(melt_list(l)), 2) + + # Test that melt_list() throws an error if not all elements are atomic vectors + l <- list(formula = y ~ b, + list = list(1:4, 3:5), + array = array(1:12, dim = c(3, 2, 2)), + expression = expression("something") + ) + + for (k in seq_along(l)) { + expect_error(melt_list(l[k])) + } + + # Test that melt_list() returns correct output for a simple case + l <- list(a = c(1, 2), b = c(3, 4)) + expect_equal(nrow(melt_list(l)), 4) + + # Test that melt_list() returns correct output for a more complex case + l <- list(a = 1:5, b = LETTERS[1:7], d = NA, e = NaN, f = -Inf) + expect_equal(nrow(melt_list(l)), 15) + expect_s3_class(melt_list(l), "data.frame") + + # default column names + expect_equal(names(melt_list(l)), c("value", "L1")) + + # custom column names + expect_equal(names(melt_list(l, varname = "the_variable", + valname = "the_value")), + c("the_value", "the_variable")) + + # output for unnamed list has one column + expect_equal(ncol(melt_list(unname(l))), 1) + # output for partially unnamed list has two columns + expect_equal(ncol(melt_list(c(unname(l), list(x = 1:4)))), 2) +}) + + + + +test_that("melt_matrix() works as expected", { + # Test that melt_matrix() returns an error when the input is not a matrix + expect_error(melt_matrix(1:5)) + expect_error(melt_matrix(list(1:5))) + expect_error(melt_matrix(NULL)) + expect_error(melt_matrix(NA)) + expect_error(melt_matrix(list(matrix()))) + expect_error(melt_matrix(data.frame())) + + + # Test that melt_matrix() returns a data.frame for an empty matrix + expect_s3_class(melt_matrix(matrix()), "data.frame") + expect_equal(nrow(melt_matrix(matrix())), 1) + + + # Test that melt_matrix() returns correct output + m <- matrix(1:16, nrow = 4, ncol = 4) + expect_s3_class(melt_matrix(m), "data.frame") + expect_equal(dim(melt_matrix(m)), c(16, 3)) + + expect_equal(names(melt_matrix(m)), c("V1", "V2", "value")) + + expect_equal(names(melt_matrix(m, + varnames = c("abc", "def"), + valname = "thevalue")), + c("abc", "def", "thevalue") + ) + + # if there are no dimnames all output variables are integers + expect_equal(sapply(melt_matrix(m), class), + c("integer", "integer", "integer"), + ignore_attr = TRUE) + + + # set dimension names + m2 <- m + dimnames(m2) <- list(LETTERS[1:4], + letters[1:4]) + + expect_equal(names(melt_matrix(m2)), c("V1", "V2", "value")) + + # if there are dimnames, the row and column indicator variables are character + # strings + expect_equal(sapply(melt_matrix(m2), class), + c("character", "character", "integer"), + ignore_attr = TRUE) + + m3 <- m2 + names(dimnames(m3)) <- c("rows", "cols") + + expect_equal(names(melt_matrix(m3)), c("rows", "cols", "value")) +}) + + + +test_that("melt_matrix_list() works as expected", { + + m <- m2 <- matrix(1:15, nrow = 5, ncol = 3) + + dimnames(m2) <- list(LETTERS[1:5], + letters[1:3]) + + m3 <- m2 + names(dimnames(m3)) <- c("rows", "cols") + + + # Test that melt_matrix_list() throws an error for objects of the wrong type + expect_error(melt_matrix_list(m)) + expect_error(melt_matrix_list(list(1:3, m))) + + + # Test that melt_matrix_list() throws an error for mismatching dimension names + expect_error(melt_matrix_list(list(m, m3))) + expect_error(melt_matrix_list(list(m2, m3))) + expect_error(melt_matrix_list(list(m, m2, m3))) + + # Test that melt_matrix_list() returns correct output + expect_s3_class(melt_matrix_list(list(m, m2)), "data.frame") + expect_s3_class(melt_matrix_list(list(m, m3), + varnames = c("abc", "def")), "data.frame") + + # correct dimensions + expect_equal(dim(melt_matrix_list(list(m, m2))), c(30, 4)) + expect_equal(names(melt_matrix_list(list(m, m2))), + c("V1", "V2", "value", "L1")) + + expect_equal(names(melt_matrix_list(list(m, m2), + varnames = c("abc", "def"))), + c("abc", "def", "value", "L1")) + + + expect_equal(sapply(melt_matrix_list(list(m, m2)), class), + c("character", "character", "integer", "character"), + ignore_attr = TRUE) + + + expect_equal(sapply(melt_matrix_list(list(M1 = m, M2 = m2)), class), + c("character", "character", "integer", "character"), + ignore_attr = TRUE) + +}) + + + From bb3240816f41756b3a32f4c9feb5295a7cf0522e Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Sat, 6 May 2023 13:02:14 +0200 Subject: [PATCH 170/176] update melt_data_frame and add test --- R/helpfunctions_melt.R | 102 ++++++++++++++--------- tests/testthat/test-helpfunctions_melt.R | 50 ++++++++++- 2 files changed, 110 insertions(+), 42 deletions(-) diff --git a/R/helpfunctions_melt.R b/R/helpfunctions_melt.R index 714b42c5..70198dd1 100644 --- a/R/helpfunctions_melt.R +++ b/R/helpfunctions_melt.R @@ -14,7 +14,6 @@ #' @noRd #' @examples #' melt_list(list(data.frame(a = 1:3), data.frame(b = 4:9))) - melt_list <- function(l, varname = "L1", valname = "value") { if (!inherits(l, "list")) { errormsg("In melt_list(): The input has to be a list.") @@ -26,7 +25,7 @@ melt_list <- function(l, varname = "L1", valname = "value") { "In melt_list(): Element(s) %s has/have length zero. I will ignore this.", paste_and(names(Filter(\(x) length(x) == 0, x = l)), - dQ = TRUE + dQ = TRUE ) ) l <- Filter(Negate(\(x) length(x) == 0), l) @@ -38,7 +37,7 @@ melt_list <- function(l, varname = "L1", valname = "value") { errormsg( "In melt_list(): Not all elements are atomic vectors (%s).", paste_and(names(Filter(\(x) !is.atomic(x) | !is.vector(x), l)), - dQ = TRUE + dQ = TRUE ) ) } @@ -47,8 +46,8 @@ melt_list <- function(l, varname = "L1", valname = "value") { rbind, lapply(seq_along(l), function(k) { df <- as.data.frame(list(l[[k]]), - col.names = valname, - stringsAsFactors = FALSE + col.names = valname, + stringsAsFactors = FALSE ) df[, varname] <- names(l)[k] @@ -76,7 +75,6 @@ melt_list <- function(l, varname = "L1", valname = "value") { #' @noRd melt_matrix <- function(x, varnames = NULL, valname = "value") { - if (!inherits(x, "matrix")) { errormsg("In melt_matrix(): This function has to be used with matrices.") @@ -108,8 +106,6 @@ melt_matrix <- function(x, varnames = NULL, valname = "value") { out[, valname] <- c(x) out - # attr(out, "out.attrs") <- NULL - # return(out) } @@ -136,10 +132,12 @@ melt_matrix_list <- function(l, varnames = NULL) { if (is.null(varnames) && - length(unique(lapply(l, \(x) names(dimnames(x))))) > 1L) { - errormsg("In melt_matrix_list(): When the argument %s is not provided, + length(unique(lapply(l, \(x) names(dimnames(x))))) > 1L) { + errormsg( + "In melt_matrix_list(): When the argument %s is not provided, all matrices must have the same names of their %s.", - dQuote("varnames"), dQuote("dimnames")) + dQuote("varnames"), dQuote("dimnames") + ) } if (is.null(names(l))) { @@ -168,10 +166,6 @@ melt_matrix_list <- function(l, varnames = NULL) { } do.call(rbind, lnew) - - # out <- do.call(rbind, lnew) - # attr(out, "out.attrs") <- NULL - # return(out) } @@ -182,18 +176,17 @@ melt_matrix_list <- function(l, varnames = NULL) { #' @param data a `data.frame` #' @param id_vars optional vector of names of variables that should not be #' melted -#' @param varnames a character vector of length two giving the names of the -#' variables that will hold the row and column indices or names -#' of the original matrix; -#' optional (otherwise a default will be created) +#' @param varname a character string giving the name of the columns that will +#' hold the variable names of the original columns; +#' default is "variable" #' @param valname the name of the variable that will hold the values of the -#' original matrix; default is "value" +#' original variables; default is "value" #' @return a melted `data.frame` #' #' @keywords internal #' @noRd -melt_data_frame <- function(data, id_vars = NULL, varname = NULL, +melt_data_frame <- function(data, id_vars = NULL, varname = "variable", valname = "value") { if (!inherits(data, "data.frame")) { errormsg("In melt_data_frame: @@ -204,31 +197,63 @@ melt_data_frame <- function(data, id_vars = NULL, varname = NULL, return(data) } - data$rowID <- paste0("rowID", seq_len(nrow(data))) - x <- data[, !names(data) %in% c("rowID", id_vars), drop = FALSE] + # check for array-type variables + any_array <- Filter(Negate(is.null), lapply(data, dim)) + if (length(any_array)) { + errormsg( + "In melt_data_frame: + I cannot melt a data.frame with an array element (%s).", + paste_and(names(any_array), dQ = TRUE) + ) + } + - g <- list( - rowID = data$rowID, - variable = if (ncol(x) > 0) names(x) - ) + # subset without the id variables + d <- subset(data, select = setdiff(names(data), id_vars)) - out <- expand.grid(Filter(Negate(is.null), g), stringsAsFactors = FALSE) - if (length(unique(sapply(x, class))) > 1) { - out[, valname] <- unlist(lapply(x, as.character)) + out <- if (is.null(id_vars)) { + data.frame(matrix(nrow = nrow(d) * ncol(d), ncol = 0)) } else { - out[, valname] <- unlist(x) + do.call( + rbind, + replicate(ncol(d), subset(data, select = id_vars), + simplify = FALSE + ) + ) } - mout <- merge(data[, c("rowID", id_vars)], out) - # attr(mout, "out.attrs") <- NULL + out[[varname]] <- rep(names(d), each = nrow(d)) + out[[valname]] <- unlist(d, recursive = FALSE) - if (ncol(x) > 0) mout[order(mout$variable), -1] else mout + out } -# used in get_models() and extract_fcts() (2020-06-10) + + +#' Melt a list of `data.frame`s +#' +#' This function takes a `list` of `data.frame`s and returns a melted +#' `data.frame`. +#' +#' @param l a `list` +#' @param id_vars optional vector of names of variables that should not be +#' melted +#' @param varname a character string giving the name of the columns that will +#' hold the variable names of the original columns; +#' default is "variable" +#' @param valname the name of the variable that will hold the values of the +#' original variables; default is "value" +#' @param lname optional name of the variable in the melted `data.frame` that +#' indicates which list element the current row came from; default +#' is "L1" +#' @return a melted `data.frame` +#' +#' @keywords internal +#' @noRd + melt_data_frame_list <- function(l, id_vars = NULL, varname = NULL, valname = "value", lname = "L1") { if (!inherits(l, "list") || !all(sapply(l, inherits, "data.frame") | @@ -238,7 +263,7 @@ melt_data_frame_list <- function(l, id_vars = NULL, varname = NULL, } lnew <- lapply(l[!sapply(l, is.null)], - melt_data_frame, + melt_data_frame, valname = valname, varname = varname, id_vars = id_vars ) @@ -249,12 +274,7 @@ melt_data_frame_list <- function(l, id_vars = NULL, varname = NULL, lnew <- lapply(names(lnew), function(k) { lnew[[k]][[lname]] <- k lnew[[k]] - # cbind(lnew[[k]], L1 = k, stringsAsFactors = FALSE) }) do.call(rbind, lnew) - - # out <- do.call(rbind, lnew) - # attr(out, "out.attrs") <- NULL - # return(out) } diff --git a/tests/testthat/test-helpfunctions_melt.R b/tests/testthat/test-helpfunctions_melt.R index d2dc6464..42a1c724 100644 --- a/tests/testthat/test-helpfunctions_melt.R +++ b/tests/testthat/test-helpfunctions_melt.R @@ -71,7 +71,7 @@ test_that("melt_matrix() works as expected", { expect_equal(names(melt_matrix(m, varnames = c("abc", "def"), valname = "thevalue")), - c("abc", "def", "thevalue") + c("abc", "def", "thevalue") ) # if there are no dimnames all output variables are integers @@ -150,3 +150,51 @@ test_that("melt_matrix_list() works as expected", { +test_that("melt_data_frame() works as expected", { + + # Test that melt_data_frame() throws an error for non-data.frame objects + expect_error(melt_data_frame(1:4)) + expect_error(melt_data_frame(list(1:4))) + expect_error(melt_data_frame(NULL)) + expect_error(melt_data_frame(NA)) + expect_error(melt_data_frame(matrix(2, 2, 0))) + + + # Test that melt_data_frame() works as expected for regular data.frame's + d <- data.frame(x = 1:5, id = LETTERS[1:5], + y = c(NA, rnorm(4)), + z = factor(sample(letters[1:3], 5, replace = TRUE)) + ) + + expect_s3_class(melt_data_frame(d), "data.frame") + expect_equal(dim(melt_data_frame(d)), + c(nrow(d) * ncol(d), 2)) + + expect_equal(dim(melt_data_frame(d, id_vars = "id")), + c(nrow(d) * (ncol(d) - 1), 2 + 1)) + + expect_equal(dim(melt_data_frame(d, id_vars = c("id", "x"))), + c(nrow(d) * (ncol(d) - 2), 2 + 2)) + + + # Test that melt_data_frame() works as expected for irregular data.frame's + d <- data.frame(id = LETTERS[1:5], + y = c(NA, rnorm(4)), + z = factor(NA, levels = 1:5) + ) + d$x <- replicate(5, rnorm(2), simplify = FALSE) + + + expect_s3_class(melt_data_frame(d), "data.frame") + expect_equal(names(melt_data_frame(d)), c("variable", "value")) + expect_equal(names(melt_data_frame(d, varname = "thevariable", + valname = "thevalue")), + c("thevariable", "thevalue")) + + + # expect an error when data contain an array variable + d$x2 <- matrix(nrow = 5, ncol = 3, data = 0) + expect_error(melt_data_frame(d, id_vars = "id")) + expect_error(melt_data_frame(d, id_vars = c("id", "x2"))) + +}) From 210fad773ab340e3a7677d2846abafc0e8ced4ee Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 16 Jan 2024 16:28:44 +0100 Subject: [PATCH 171/176] changes to allow for non-linear functions of time-dependent covariates in the survival sub-model of a joint model --- R/JAGSmodel_glmm.R | 3 +-- R/JAGSmodel_surv.R | 12 ++++++++++-- R/get_model_info.R | 12 ++++++++---- R/helpfunctions_JAGSmodel.R | 21 +++++++++++++++++++-- 4 files changed, 38 insertions(+), 10 deletions(-) diff --git a/R/JAGSmodel_glmm.R b/R/JAGSmodel_glmm.R index 1553e26b..5bd0fbea 100644 --- a/R/JAGSmodel_glmm.R +++ b/R/JAGSmodel_glmm.R @@ -164,10 +164,9 @@ glmm_in_jm <- function(info) { " <- ", add_linebreaks(Z_predictor, indent = linkindent + 12 + nchar(info$varname) + 9 + nchar(index)), - "\n", + tab(6), info$trafos, tab(4), "}\n", dummies, - info$trafos, "\n" ) } diff --git a/R/JAGSmodel_surv.R b/R/JAGSmodel_surv.R index a893699f..fd0091ff 100644 --- a/R/JAGSmodel_surv.R +++ b/R/JAGSmodel_surv.R @@ -178,6 +178,7 @@ jagsmodel_coxph <- function(info) { covnames = vector(mode = "list", length = length(info$lp[["M_lvlone"]] )), + trafo = info$fcts_all, isgk = FALSE) }), collapse = " + ") @@ -198,6 +199,7 @@ jagsmodel_coxph <- function(info) { covnames = vector(mode = "list", length = length( info$lp[["M_lvlone"]])), + trafo = info$fcts_all, isgk = TRUE) } ), collapse = " + "), @@ -333,7 +335,10 @@ jagsmodel_jm <- function(info) { cols = info$lp[["M_lvlone"]], scale_pars = info$scale_pars[["M_lvlone"]], assoc_type = info$assoc_type, - covnames = names(info$lp[["M_lvlone"]]), + covnames = cvapply(names(info$lp[["M_lvlone"]]), + replace_trafo, + info$fcts_all), + trafo = info$fcts_all, isgk = FALSE) }), collapse = " + ") @@ -351,7 +356,10 @@ jagsmodel_jm <- function(info) { cols = info$lp[["M_lvlone"]], scale_pars = info$scale_pars[["M_lvlone"]], assoc_type = info$assoc_type, - covnames = names(info$lp[["M_lvlone"]]), + covnames = cvapply(names(info$lp[["M_lvlone"]]), + replace_trafo, + info$fcts_all), + trafo = info$fcts_all, isgk = TRUE) } ), collapse = " + "), diff --git a/R/get_model_info.R b/R/get_model_info.R index e4e5091f..e1a920b2 100644 --- a/R/get_model_info.R +++ b/R/get_model_info.R @@ -116,7 +116,7 @@ get_model1_info <- function(k, Mlist, par_index_main, par_index_other, # transformations ------------------------------------------------------------ trafos <- paste_trafos(Mlist, varname = k, - index = index[gsub("M_", "", resp_mat[1L])], + index = if (isgk) "ii" else index[gsub("M_", "", resp_mat[1L])], isgk = isgk) # JM settings ---------------------------------------------------------------- @@ -221,12 +221,15 @@ get_model1_info <- function(k, Mlist, par_index_main, par_index_other, assoc_type <- if (modeltype %in% "JM") { covrs <- unique(unlist(lapply(names(unlist(unname(lp))), replace_dummy, Mlist$refs))) - get_assoc_type(intersect(tvars, covrs), + covrs <- setNames(unlist(lapply(covrs, replace_trafo, Mlist$fcts_all)), + covrs) + + get_assoc_type(covrs[covrs %in% tvars], Mlist$models, assoc_type, Mlist$refs) } else if (modeltype %in% "coxph") { "obs.value" } else if (isTRUE(isgk)) { - get_assoc_type(k, Mlist$models, assoc_type, Mlist$refs) + get_assoc_type(setNames(k, k), Mlist$models, assoc_type, Mlist$refs) } # collect all info --------------------------------------------------------- @@ -256,6 +259,7 @@ get_model1_info <- function(k, Mlist, par_index_main, par_index_other, rd_vcov = rd_vcov, group_lvls = Mlist$group_lvls, trafos = trafos, + fcts_all = Mlist$fcts_all, trunc = trunc[[k]], custom = custom[[k]], ppc = FALSE, @@ -437,7 +441,7 @@ get_assoc_type <- function(covnames, models, assoc_type, refs) { fmlys <- lapply(models[covnames], get_family) assoc_type <- setNames(rep("obs.value", length(covnames)), - covnames) + names(covnames)) assoc_type[fmlys %in% c("gaussian", "Gamma", "lognorm", "beta")] <- "underl.value" diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 12620196..37c948a3 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -706,7 +706,8 @@ rd_vcov_full <- function(nranef, nam) { # Joint model ------------------------------------------------------------------ paste_linpred_jm <- function(varname, parname, parelmts, matnam, index, cols, - scale_pars, assoc_type, covnames, isgk = FALSE) { + scale_pars, assoc_type, covnames, isgk = FALSE, + trafo = NULL) { # - varname: name of the survival outcome # - parname: name of the parameter, e.g. "beta" # - parelmts: vector specifying which elements of the parameter vector are @@ -732,8 +733,24 @@ paste_linpred_jm <- function(varname, parname, parelmts, matnam, index, cols, index = index, columns = cols, assoc_type = assoc_type, isgk = isgk) + # wrap in trafo if there is a trafo of the time-dep variable in the lin.pred + # of the survival model + if (!is.null(unlist(covnames))) { + pastedat <- Map(function(covname, colname, strng) { + if (colname %in% trafo$colname) { + fct <- trafo$fct[trafo$colname == colname] + if (trafo$type[trafo$colname == colname] == "I") { + fct <- gsub("\\)$", "", gsub("^I\\(", "", fct)) + } + gsub(pattern = covname, replacement = strng, x = fct) + } else { + strng + } + }, covname = covnames, colname = names(covnames), strng = pastedat) + } + paste( - paste_scaling(x = pastedat, + paste_scaling(x = unlist(pastedat), rows = cols, scale_pars = list(scale_pars)[rep(1, length(cols))], scalemat = rep(paste0("sp", matnam), length(cols)) From f2c2e8bb35b0d6414276228dcdc0b09e0627bcc1 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 2 Apr 2024 11:07:21 +0200 Subject: [PATCH 172/176] fix typos in argument names in function description --- R/helpfunctions_JAGSmodel.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/helpfunctions_JAGSmodel.R b/R/helpfunctions_JAGSmodel.R index 37c948a3..bf5302c8 100644 --- a/R/helpfunctions_JAGSmodel.R +++ b/R/helpfunctions_JAGSmodel.R @@ -8,9 +8,9 @@ #' if necessary. #' #' @param parname character string; name fo the parameter (e.g., "beta") -#' @param parlemts integer vector; indices of the parameter vector to be used; +#' @param parelmts integer vector; indices of the parameter vector to be used; #' should have the same length as `cols` -#' @param matname character string; name of the data matrix +#' @param matnam character string; name of the data matrix #' @param index character string; name of the index (e.g., "i" or "ii") #' @param cols integer vector; indices of the columns of `matname`, should have #' the same length as `parlemts` @@ -56,7 +56,7 @@ paste_data <- function(matnam, index, col, isgk = FALSE) { #' Write the coefficient part of a linear predictor #' #' @param parname character string; name of the coefficient (e.g., "beta") -#' @param parlemts vector of integers; the index of the parameter vector +#' @param parelmts vector of integers; the index of the parameter vector #' #' @return A vector of character strings of the form `beta[3]`. #' From d768cbcb621b79cf5ccb5dd65cb60379722d2ac7 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 2 Apr 2024 11:08:06 +0200 Subject: [PATCH 173/176] change \itemize to \describe in function descritpion to resolve CRAN NOTE (\item within \itemize cannot take arguments) --- R/JointAIObject.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/JointAIObject.R b/R/JointAIObject.R index ff24922e..fb8203de 100644 --- a/R/JointAIObject.R +++ b/R/JointAIObject.R @@ -70,7 +70,7 @@ #' \item{\code{jagsmodel}}{The JAGS model as character string.} #' \item{\code{mcmc_settings}}{a list containing MCMC sampling related #' information with elements -#' \itemize{ +#' \describe{ #' \item{\code{modelfile}: }{path and name of the JAGS model file} #' \item{\code{n.chains}: }{number of MCMC chains} #' \item{\code{n.adapt}: }{number of iterations in the adaptive phase} From 8e3797a805d65b7c9a1282e653f433848e135c91 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 2 Apr 2024 14:30:58 +0200 Subject: [PATCH 174/176] fix typos in documentation --- R/model_imp.R | 2 +- man/JointAIObject.Rd | 2 +- man/model_imp.Rd | 2 +- man/paste_coef.Rd | 2 +- man/paste_linpred.Rd | 10 +++++----- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/R/model_imp.R b/R/model_imp.R index 4641a8bd..93a70576 100644 --- a/R/model_imp.R +++ b/R/model_imp.R @@ -390,7 +390,7 @@ #' (for beta regression) or \code{shape_main} #' (for parametric survival models), \code{gamma_main} #' (for cumulative logit models), -#' code{D_main} (for multi-level models) and +#' \code{D_main} (for multi-level models) and #' \code{basehaz} in proportional hazards models)\cr #' \code{analysis_random} \tab \code{ranef_main}, \code{D_main}, #' \code{invD_main}, \code{RinvD_main}\cr diff --git a/man/JointAIObject.Rd b/man/JointAIObject.Rd index afd73994..bd56f73a 100644 --- a/man/JointAIObject.Rd +++ b/man/JointAIObject.Rd @@ -68,7 +68,7 @@ regression coefficients for each covariate model per design matrix} \item{\code{jagsmodel}}{The JAGS model as character string.} \item{\code{mcmc_settings}}{a list containing MCMC sampling related information with elements -\itemize{ +\describe{ \item{\code{modelfile}: }{path and name of the JAGS model file} \item{\code{n.chains}: }{number of MCMC chains} \item{\code{n.adapt}: }{number of iterations in the adaptive phase} diff --git a/man/model_imp.Rd b/man/model_imp.Rd index 3acee4a5..3d5a6f4f 100644 --- a/man/model_imp.Rd +++ b/man/model_imp.Rd @@ -576,7 +576,7 @@ will be used. (for beta regression) or \code{shape_main} (for parametric survival models), \code{gamma_main} (for cumulative logit models), -code{D_main} (for multi-level models) and +\code{D_main} (for multi-level models) and \code{basehaz} in proportional hazards models)\cr \code{analysis_random} \tab \code{ranef_main}, \code{D_main}, \code{invD_main}, \code{RinvD_main}\cr diff --git a/man/paste_coef.Rd b/man/paste_coef.Rd index 70798546..7a3df330 100644 --- a/man/paste_coef.Rd +++ b/man/paste_coef.Rd @@ -9,7 +9,7 @@ paste_coef(parname, parelmts) \arguments{ \item{parname}{character string; name of the coefficient (e.g., "beta")} -\item{parlemts}{vector of integers; the index of the parameter vector} +\item{parelmts}{vector of integers; the index of the parameter vector} } \value{ A vector of character strings of the form \code{beta[3]}. diff --git a/man/paste_linpred.Rd b/man/paste_linpred.Rd index dc57e180..36b88835 100644 --- a/man/paste_linpred.Rd +++ b/man/paste_linpred.Rd @@ -10,6 +10,11 @@ paste_linpred(parname, parelmts, matnam, index, cols, scale_pars, \arguments{ \item{parname}{character string; name fo the parameter (e.g., "beta")} +\item{parelmts}{integer vector; indices of the parameter vector to be used; +should have the same length as \code{cols}} + +\item{matnam}{character string; name of the data matrix} + \item{index}{character string; name of the index (e.g., "i" or "ii")} \item{cols}{integer vector; indices of the columns of \code{matname}, should have @@ -20,11 +25,6 @@ the same length as \code{parlemts}} \item{isgk}{logical; is this linear predictor within the Gauss-Kronrod quadrature?} - -\item{parlemts}{integer vector; indices of the parameter vector to be used; -should have the same length as \code{cols}} - -\item{matname}{character string; name of the data matrix} } \description{ Construct a linear predictor from parameter names and indices, the name of From 3837d996ab80f7a1a4619c8ccaed98e37049f4b9 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 2 Apr 2024 14:31:10 +0200 Subject: [PATCH 175/176] remove .Rproj.user from build --- .Rbuildignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.Rbuildignore b/.Rbuildignore index 2de11f09..15acc3c2 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -3,6 +3,7 @@ ^doc$ ^.*\.Rproj$ ^\.Rproj\.user$ +.Rproj.user ^\.travis\.yml$ ^README\.Rmd$ ^README-.*\.png$ From 53e2336dfd75154ec93e4ad60ec1550456786040 Mon Sep 17 00:00:00 2001 From: Nicole Erler Date: Tue, 2 Apr 2024 14:33:18 +0200 Subject: [PATCH 176/176] update snap --- tests/testthat/_snaps/glmm.md | 49011 -------------------------------- 1 file changed, 49011 deletions(-) delete mode 100644 tests/testthat/_snaps/glmm.md diff --git a/tests/testthat/_snaps/glmm.md b/tests/testthat/_snaps/glmm.md deleted file mode 100644 index 472cdff2..00000000 --- a/tests/testthat/_snaps/glmm.md +++ /dev/null @@ -1,49011 +0,0 @@ -# data_list remains the same - - Code - lapply(models, "[[", "data_list") - Output - $m0a1 - $m0a1$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0a1$M_lvlone - y - 1 -13.0493856 - 1.1 -9.3335901 - 1.2 -22.3469852 - 1.3 -15.0417337 - 2 -12.0655434 - 2.1 -15.8674476 - 2.2 -7.8800006 - 3 -11.4820604 - 3.1 -10.5983220 - 3.2 -22.4519157 - 4 -1.2697775 - 4.1 -11.1215184 - 4.2 -3.6134138 - 4.3 -14.5982385 - 5 -6.8457515 - 5.1 -7.0551214 - 5.2 -12.3418980 - 5.3 -9.2366906 - 6 -5.1648211 - 7 -10.0599502 - 7.1 -18.3267285 - 7.2 -12.5138426 - 8 -1.6305331 - 8.1 -9.6520453 - 8.2 -1.5278462 - 8.3 -7.4172211 - 8.4 -7.1238609 - 8.5 -8.8706950 - 9 -0.1634429 - 9.1 -2.6034300 - 9.2 -6.7272369 - 10 -6.4172202 - 10.1 -11.4834569 - 11 -8.7911356 - 11.1 -19.6645080 - 11.2 -20.2030932 - 11.3 -21.3082176 - 11.4 -14.5802901 - 12 -15.2006287 - 13 0.8058816 - 13.1 -13.6379208 - 14 -15.3422873 - 14.1 -10.0965208 - 14.2 -16.6452027 - 14.3 -15.8389733 - 15 -8.9424594 - 15.1 -22.0101983 - 15.2 -7.3975599 - 15.3 -10.3567334 - 16 -1.9691302 - 16.1 -9.9308357 - 16.2 -6.9626923 - 16.3 -3.2862557 - 16.4 -3.3972355 - 16.5 -11.5767835 - 17 -10.5474144 - 17.1 -7.6215009 - 17.2 -16.5386939 - 17.3 -20.0004774 - 17.4 -18.8505475 - 18 -19.7302351 - 19 -14.6177568 - 19.1 -17.8043866 - 19.2 -15.1641705 - 19.3 -16.6898418 - 20 -12.9059229 - 20.1 -16.8191201 - 20.2 -6.1010131 - 20.3 -7.9415371 - 20.4 -9.3904458 - 20.5 -13.3504189 - 21 -7.6974718 - 21.1 -11.9335526 - 21.2 -12.7064929 - 22 -21.5022909 - 22.1 -12.7745451 - 23 -3.5146508 - 23.1 -4.6724048 - 24 -2.5619821 - 25 -6.2944970 - 25.1 -3.8630505 - 25.2 -14.4205140 - 25.3 -19.6735037 - 25.4 -9.0288933 - 25.5 -9.0509738 - 26 -19.7340685 - 26.1 -14.1692728 - 26.2 -17.2819976 - 26.3 -24.6265576 - 27 -7.3354999 - 27.1 -11.1488468 - 28 -11.7996597 - 28.1 -8.2030122 - 28.2 -26.4317815 - 28.3 -18.5016071 - 29 -5.8551395 - 29.1 -2.0209442 - 29.2 -5.6368080 - 29.3 -3.8110961 - 30 -12.7217702 - 30.1 -17.0170140 - 30.2 -25.4236089 - 31 -17.0783921 - 32 -18.4338764 - 32.1 -19.4317212 - 32.2 -19.4738978 - 32.3 -21.4922645 - 33 2.0838099 - 33.1 -13.3172274 - 34 -10.0296691 - 34.1 -25.9426553 - 34.2 -18.5688138 - 34.3 -15.4173859 - 35 -14.3958113 - 35.1 -12.9457541 - 35.2 -16.1380691 - 36 -12.8166968 - 36.1 -14.3989481 - 36.2 -12.2436943 - 36.3 -15.0104638 - 36.4 -10.1775457 - 37 -15.2223495 - 37.1 -14.7526195 - 37.2 -19.8168430 - 38 -2.7065118 - 39 -8.7288138 - 39.1 -9.2746473 - 39.2 -18.2695344 - 39.3 -13.8219083 - 39.4 -16.2254704 - 39.5 -21.7283648 - 40 1.8291916 - 40.1 -6.6916432 - 40.2 -1.6278171 - 40.3 -10.5749790 - 41 -3.1556121 - 41.1 -11.5895327 - 41.2 -18.9352091 - 41.3 -15.9788960 - 41.4 -9.6070508 - 42 -5.2159485 - 42.1 -15.9878743 - 43 -16.6104361 - 43.1 -9.5549441 - 43.2 -14.2003491 - 44 -8.1969033 - 44.1 -19.9270197 - 44.2 -22.6521171 - 44.3 -21.1903736 - 45 -0.5686627 - 45.1 -7.5645740 - 46 -19.1624789 - 46.1 -18.4487574 - 46.2 -15.8222682 - 47 -5.4165074 - 47.1 -15.0975029 - 47.2 -12.9971413 - 47.3 -10.6844521 - 47.4 -18.2214784 - 48 -8.3101471 - 48.1 -18.3854275 - 49 -13.0130319 - 50 -10.4579977 - 51 -19.3157621 - 52 -4.4747188 - 52.1 -4.3163827 - 52.2 -6.9761408 - 52.3 -20.1764756 - 52.4 -8.9036692 - 52.5 -5.6949642 - 53 -10.3141887 - 53.1 -8.2642654 - 53.2 -9.1691554 - 54 -6.2198754 - 54.1 -15.7192609 - 54.2 -13.0978998 - 54.3 -5.1195299 - 54.4 -16.5771751 - 55 -5.7348534 - 55.1 -7.3217494 - 55.2 -12.2171938 - 55.3 -12.9821266 - 55.4 -14.8599983 - 56 -14.1764282 - 56.1 -12.5343602 - 56.2 -8.4573382 - 56.3 -12.4633969 - 56.4 -17.3841863 - 56.5 -14.8147645 - 57 -3.1403293 - 57.1 -11.1509248 - 57.2 -6.3940143 - 57.3 -9.3473241 - 58 -12.0245677 - 58.1 -9.2112246 - 58.2 -1.2071742 - 58.3 -11.0141711 - 58.4 -5.3721214 - 58.5 -7.8523047 - 59 -13.2946560 - 59.1 -10.0530648 - 60 -19.2209402 - 61 -4.6699914 - 61.1 -3.5981894 - 61.2 -1.4713611 - 61.3 -3.8819786 - 61.4 0.1041413 - 62 -2.8591600 - 62.1 -6.9461986 - 62.2 -16.7910593 - 62.3 -17.9844596 - 63 -24.0335535 - 63.1 -11.7765300 - 64 -20.5963897 - 65 -2.7969169 - 65.1 -11.1778694 - 65.2 -5.2830399 - 65.3 -7.9353390 - 66 -13.2318328 - 66.1 -1.9090560 - 66.2 -16.6643889 - 67 -25.6073277 - 68 -13.4806759 - 68.1 -18.4557183 - 68.2 -13.3982327 - 68.3 -12.4977127 - 68.4 -11.7073990 - 69 -14.5290675 - 70 -15.2122709 - 70.1 -7.8681167 - 71 -10.3352703 - 71.1 -7.5699888 - 71.2 -18.4680702 - 71.3 -21.4316644 - 71.4 -8.1137650 - 72 -9.1848162 - 72.1 -23.7538846 - 72.2 -26.3421306 - 72.3 -27.2843801 - 72.4 -20.8541617 - 72.5 -12.8948965 - 73 -2.6091307 - 74 -8.2790175 - 75 -12.5029612 - 76 -6.0061671 - 76.1 -8.8149114 - 76.2 -11.8359043 - 77 0.4772521 - 78 -9.4105229 - 79 -1.0217265 - 79.1 -11.8125257 - 79.2 -10.5465186 - 80 -12.7366807 - 80.1 -9.0584783 - 80.2 -16.6381566 - 81 0.5547913 - 81.1 -4.0892715 - 81.2 1.8283303 - 81.3 -5.2166381 - 82 -3.0749381 - 82.1 -10.5506696 - 82.2 -18.2226347 - 83 -12.5872635 - 83.1 -11.9756502 - 83.2 -10.6744217 - 83.3 -19.2714012 - 84 -2.6320312 - 84.1 -9.8140094 - 85 -12.3886736 - 85.1 -12.9196365 - 85.2 -9.6433248 - 85.3 -6.3296340 - 85.4 -7.0405525 - 85.5 -13.6714939 - 86 -10.8756412 - 86.1 -12.0055331 - 86.2 -13.3724699 - 86.3 -13.3252145 - 86.4 -14.9191290 - 86.5 -17.7515546 - 87 -10.7027963 - 87.1 -22.4941954 - 87.2 -14.9616716 - 88 -2.2264493 - 88.1 -8.9626474 - 88.2 -2.5095281 - 88.3 -16.3345673 - 89 -11.0459647 - 90 -4.5610239 - 90.1 -11.7036651 - 90.2 -5.3838521 - 90.3 -4.1636999 - 91 -7.1462503 - 91.1 -12.8374475 - 91.2 -18.2576707 - 92 -6.4119222 - 93 5.2122168 - 93.1 3.1211725 - 93.2 -3.6841177 - 93.3 2.6223542 - 93.4 -11.1877696 - 94 -6.9602492 - 94.1 -7.4318416 - 94.2 -4.3498045 - 94.3 -11.6340088 - 94.4 -12.9357964 - 94.5 -14.7648530 - 95 -12.8849309 - 95.1 -9.7451502 - 95.2 -0.8535063 - 96 -4.9139832 - 96.1 -3.9582653 - 96.2 -9.6555492 - 96.3 -11.8690793 - 96.4 -11.0224373 - 96.5 -10.9530403 - 97 -9.8540471 - 97.1 -19.2262840 - 98 -11.9651231 - 98.1 -2.6515128 - 98.2 -12.2606382 - 99 -11.4720500 - 99.1 -14.0596866 - 99.2 -17.3939469 - 100 1.1005874 - 100.1 -3.8226248 - 100.2 -0.9123182 - 100.3 -15.8389474 - 100.4 -12.8093826 - - $m0a1$mu_reg_norm - [1] 0 - - $m0a1$tau_reg_norm - [1] 1e-04 - - $m0a1$shape_tau_norm - [1] 0.01 - - $m0a1$rate_tau_norm - [1] 0.01 - - $m0a1$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0a1$shape_diag_RinvD - [1] "0.01" - - $m0a1$rate_diag_RinvD - [1] "0.001" - - - $m0a2 - $m0a2$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0a2$M_lvlone - y - 1 -13.0493856 - 1.1 -9.3335901 - 1.2 -22.3469852 - 1.3 -15.0417337 - 2 -12.0655434 - 2.1 -15.8674476 - 2.2 -7.8800006 - 3 -11.4820604 - 3.1 -10.5983220 - 3.2 -22.4519157 - 4 -1.2697775 - 4.1 -11.1215184 - 4.2 -3.6134138 - 4.3 -14.5982385 - 5 -6.8457515 - 5.1 -7.0551214 - 5.2 -12.3418980 - 5.3 -9.2366906 - 6 -5.1648211 - 7 -10.0599502 - 7.1 -18.3267285 - 7.2 -12.5138426 - 8 -1.6305331 - 8.1 -9.6520453 - 8.2 -1.5278462 - 8.3 -7.4172211 - 8.4 -7.1238609 - 8.5 -8.8706950 - 9 -0.1634429 - 9.1 -2.6034300 - 9.2 -6.7272369 - 10 -6.4172202 - 10.1 -11.4834569 - 11 -8.7911356 - 11.1 -19.6645080 - 11.2 -20.2030932 - 11.3 -21.3082176 - 11.4 -14.5802901 - 12 -15.2006287 - 13 0.8058816 - 13.1 -13.6379208 - 14 -15.3422873 - 14.1 -10.0965208 - 14.2 -16.6452027 - 14.3 -15.8389733 - 15 -8.9424594 - 15.1 -22.0101983 - 15.2 -7.3975599 - 15.3 -10.3567334 - 16 -1.9691302 - 16.1 -9.9308357 - 16.2 -6.9626923 - 16.3 -3.2862557 - 16.4 -3.3972355 - 16.5 -11.5767835 - 17 -10.5474144 - 17.1 -7.6215009 - 17.2 -16.5386939 - 17.3 -20.0004774 - 17.4 -18.8505475 - 18 -19.7302351 - 19 -14.6177568 - 19.1 -17.8043866 - 19.2 -15.1641705 - 19.3 -16.6898418 - 20 -12.9059229 - 20.1 -16.8191201 - 20.2 -6.1010131 - 20.3 -7.9415371 - 20.4 -9.3904458 - 20.5 -13.3504189 - 21 -7.6974718 - 21.1 -11.9335526 - 21.2 -12.7064929 - 22 -21.5022909 - 22.1 -12.7745451 - 23 -3.5146508 - 23.1 -4.6724048 - 24 -2.5619821 - 25 -6.2944970 - 25.1 -3.8630505 - 25.2 -14.4205140 - 25.3 -19.6735037 - 25.4 -9.0288933 - 25.5 -9.0509738 - 26 -19.7340685 - 26.1 -14.1692728 - 26.2 -17.2819976 - 26.3 -24.6265576 - 27 -7.3354999 - 27.1 -11.1488468 - 28 -11.7996597 - 28.1 -8.2030122 - 28.2 -26.4317815 - 28.3 -18.5016071 - 29 -5.8551395 - 29.1 -2.0209442 - 29.2 -5.6368080 - 29.3 -3.8110961 - 30 -12.7217702 - 30.1 -17.0170140 - 30.2 -25.4236089 - 31 -17.0783921 - 32 -18.4338764 - 32.1 -19.4317212 - 32.2 -19.4738978 - 32.3 -21.4922645 - 33 2.0838099 - 33.1 -13.3172274 - 34 -10.0296691 - 34.1 -25.9426553 - 34.2 -18.5688138 - 34.3 -15.4173859 - 35 -14.3958113 - 35.1 -12.9457541 - 35.2 -16.1380691 - 36 -12.8166968 - 36.1 -14.3989481 - 36.2 -12.2436943 - 36.3 -15.0104638 - 36.4 -10.1775457 - 37 -15.2223495 - 37.1 -14.7526195 - 37.2 -19.8168430 - 38 -2.7065118 - 39 -8.7288138 - 39.1 -9.2746473 - 39.2 -18.2695344 - 39.3 -13.8219083 - 39.4 -16.2254704 - 39.5 -21.7283648 - 40 1.8291916 - 40.1 -6.6916432 - 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97 -9.8540471 - 97.1 -19.2262840 - 98 -11.9651231 - 98.1 -2.6515128 - 98.2 -12.2606382 - 99 -11.4720500 - 99.1 -14.0596866 - 99.2 -17.3939469 - 100 1.1005874 - 100.1 -3.8226248 - 100.2 -0.9123182 - 100.3 -15.8389474 - 100.4 -12.8093826 - - $m0a4$mu_reg_norm - [1] 0 - - $m0a4$tau_reg_norm - [1] 1e-04 - - $m0a4$shape_tau_norm - [1] 0.01 - - $m0a4$rate_tau_norm - [1] 0.01 - - $m0a4$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0a4$shape_diag_RinvD - [1] "0.01" - - $m0a4$rate_diag_RinvD - [1] "0.001" - - - $m0b1 - $m0b1$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0b1$M_lvlone - b1 - 1 0 - 1.1 1 - 1.2 1 - 1.3 0 - 2 1 - 2.1 1 - 2.2 1 - 3 1 - 3.1 0 - 3.2 0 - 4 1 - 4.1 1 - 4.2 0 - 4.3 1 - 5 0 - 5.1 1 - 5.2 1 - 5.3 1 - 6 0 - 7 1 - 7.1 0 - 7.2 1 - 8 0 - 8.1 1 - 8.2 1 - 8.3 0 - 8.4 0 - 8.5 1 - 9 1 - 9.1 1 - 9.2 0 - 10 1 - 10.1 1 - 11 1 - 11.1 1 - 11.2 1 - 11.3 1 - 11.4 1 - 12 1 - 13 0 - 13.1 1 - 14 0 - 14.1 1 - 14.2 0 - 14.3 0 - 15 0 - 15.1 0 - 15.2 0 - 15.3 1 - 16 1 - 16.1 0 - 16.2 1 - 16.3 1 - 16.4 1 - 16.5 0 - 17 0 - 17.1 0 - 17.2 1 - 17.3 0 - 17.4 1 - 18 1 - 19 1 - 19.1 1 - 19.2 1 - 19.3 1 - 20 0 - 20.1 1 - 20.2 0 - 20.3 0 - 20.4 0 - 20.5 0 - 21 1 - 21.1 1 - 21.2 0 - 22 0 - 22.1 1 - 23 1 - 23.1 1 - 24 0 - 25 0 - 25.1 1 - 25.2 1 - 25.3 0 - 25.4 0 - 25.5 0 - 26 1 - 26.1 1 - 26.2 1 - 26.3 0 - 27 1 - 27.1 1 - 28 1 - 28.1 0 - 28.2 1 - 28.3 1 - 29 1 - 29.1 0 - 29.2 0 - 29.3 1 - 30 1 - 30.1 1 - 30.2 1 - 31 0 - 32 1 - 32.1 1 - 32.2 1 - 32.3 1 - 33 0 - 33.1 0 - 34 1 - 34.1 0 - 34.2 1 - 34.3 1 - 35 1 - 35.1 0 - 35.2 1 - 36 0 - 36.1 0 - 36.2 1 - 36.3 0 - 36.4 1 - 37 1 - 37.1 0 - 37.2 0 - 38 1 - 39 1 - 39.1 0 - 39.2 0 - 39.3 0 - 39.4 1 - 39.5 1 - 40 0 - 40.1 0 - 40.2 0 - 40.3 1 - 41 1 - 41.1 1 - 41.2 0 - 41.3 1 - 41.4 1 - 42 1 - 42.1 1 - 43 0 - 43.1 0 - 43.2 1 - 44 1 - 44.1 0 - 44.2 0 - 44.3 1 - 45 1 - 45.1 0 - 46 1 - 46.1 0 - 46.2 1 - 47 0 - 47.1 0 - 47.2 1 - 47.3 0 - 47.4 0 - 48 0 - 48.1 1 - 49 0 - 50 1 - 51 1 - 52 1 - 52.1 1 - 52.2 0 - 52.3 0 - 52.4 1 - 52.5 1 - 53 1 - 53.1 1 - 53.2 1 - 54 0 - 54.1 1 - 54.2 0 - 54.3 1 - 54.4 0 - 55 1 - 55.1 1 - 55.2 1 - 55.3 0 - 55.4 1 - 56 0 - 56.1 1 - 56.2 1 - 56.3 0 - 56.4 0 - 56.5 1 - 57 1 - 57.1 1 - 57.2 0 - 57.3 0 - 58 1 - 58.1 1 - 58.2 1 - 58.3 1 - 58.4 1 - 58.5 1 - 59 0 - 59.1 1 - 60 0 - 61 1 - 61.1 1 - 61.2 1 - 61.3 0 - 61.4 1 - 62 1 - 62.1 0 - 62.2 0 - 62.3 1 - 63 0 - 63.1 1 - 64 1 - 65 1 - 65.1 1 - 65.2 0 - 65.3 0 - 66 1 - 66.1 0 - 66.2 0 - 67 0 - 68 0 - 68.1 0 - 68.2 0 - 68.3 0 - 68.4 1 - 69 1 - 70 1 - 70.1 1 - 71 1 - 71.1 1 - 71.2 0 - 71.3 0 - 71.4 0 - 72 1 - 72.1 1 - 72.2 1 - 72.3 0 - 72.4 0 - 72.5 1 - 73 1 - 74 1 - 75 0 - 76 1 - 76.1 1 - 76.2 1 - 77 1 - 78 1 - 79 0 - 79.1 1 - 79.2 0 - 80 1 - 80.1 0 - 80.2 1 - 81 1 - 81.1 1 - 81.2 1 - 81.3 1 - 82 1 - 82.1 1 - 82.2 0 - 83 1 - 83.1 0 - 83.2 0 - 83.3 1 - 84 1 - 84.1 0 - 85 0 - 85.1 0 - 85.2 1 - 85.3 1 - 85.4 1 - 85.5 1 - 86 0 - 86.1 1 - 86.2 1 - 86.3 0 - 86.4 1 - 86.5 0 - 87 0 - 87.1 1 - 87.2 0 - 88 0 - 88.1 0 - 88.2 0 - 88.3 0 - 89 1 - 90 0 - 90.1 1 - 90.2 1 - 90.3 0 - 91 0 - 91.1 0 - 91.2 1 - 92 1 - 93 0 - 93.1 1 - 93.2 0 - 93.3 1 - 93.4 0 - 94 1 - 94.1 0 - 94.2 1 - 94.3 0 - 94.4 0 - 94.5 0 - 95 1 - 95.1 1 - 95.2 0 - 96 1 - 96.1 0 - 96.2 0 - 96.3 0 - 96.4 0 - 96.5 1 - 97 0 - 97.1 0 - 98 0 - 98.1 0 - 98.2 0 - 99 1 - 99.1 1 - 99.2 1 - 100 0 - 100.1 0 - 100.2 1 - 100.3 1 - 100.4 1 - - $m0b1$mu_reg_binom - [1] 0 - - $m0b1$tau_reg_binom - [1] 1e-04 - - $m0b1$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0b1$shape_diag_RinvD - [1] "0.01" - - $m0b1$rate_diag_RinvD - [1] "0.001" - - - $m0b2 - $m0b2$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0b2$M_lvlone - b1 - 1 0 - 1.1 1 - 1.2 1 - 1.3 0 - 2 1 - 2.1 1 - 2.2 1 - 3 1 - 3.1 0 - 3.2 0 - 4 1 - 4.1 1 - 4.2 0 - 4.3 1 - 5 0 - 5.1 1 - 5.2 1 - 5.3 1 - 6 0 - 7 1 - 7.1 0 - 7.2 1 - 8 0 - 8.1 1 - 8.2 1 - 8.3 0 - 8.4 0 - 8.5 1 - 9 1 - 9.1 1 - 9.2 0 - 10 1 - 10.1 1 - 11 1 - 11.1 1 - 11.2 1 - 11.3 1 - 11.4 1 - 12 1 - 13 0 - 13.1 1 - 14 0 - 14.1 1 - 14.2 0 - 14.3 0 - 15 0 - 15.1 0 - 15.2 0 - 15.3 1 - 16 1 - 16.1 0 - 16.2 1 - 16.3 1 - 16.4 1 - 16.5 0 - 17 0 - 17.1 0 - 17.2 1 - 17.3 0 - 17.4 1 - 18 1 - 19 1 - 19.1 1 - 19.2 1 - 19.3 1 - 20 0 - 20.1 1 - 20.2 0 - 20.3 0 - 20.4 0 - 20.5 0 - 21 1 - 21.1 1 - 21.2 0 - 22 0 - 22.1 1 - 23 1 - 23.1 1 - 24 0 - 25 0 - 25.1 1 - 25.2 1 - 25.3 0 - 25.4 0 - 25.5 0 - 26 1 - 26.1 1 - 26.2 1 - 26.3 0 - 27 1 - 27.1 1 - 28 1 - 28.1 0 - 28.2 1 - 28.3 1 - 29 1 - 29.1 0 - 29.2 0 - 29.3 1 - 30 1 - 30.1 1 - 30.2 1 - 31 0 - 32 1 - 32.1 1 - 32.2 1 - 32.3 1 - 33 0 - 33.1 0 - 34 1 - 34.1 0 - 34.2 1 - 34.3 1 - 35 1 - 35.1 0 - 35.2 1 - 36 0 - 36.1 0 - 36.2 1 - 36.3 0 - 36.4 1 - 37 1 - 37.1 0 - 37.2 0 - 38 1 - 39 1 - 39.1 0 - 39.2 0 - 39.3 0 - 39.4 1 - 39.5 1 - 40 0 - 40.1 0 - 40.2 0 - 40.3 1 - 41 1 - 41.1 1 - 41.2 0 - 41.3 1 - 41.4 1 - 42 1 - 42.1 1 - 43 0 - 43.1 0 - 43.2 1 - 44 1 - 44.1 0 - 44.2 0 - 44.3 1 - 45 1 - 45.1 0 - 46 1 - 46.1 0 - 46.2 1 - 47 0 - 47.1 0 - 47.2 1 - 47.3 0 - 47.4 0 - 48 0 - 48.1 1 - 49 0 - 50 1 - 51 1 - 52 1 - 52.1 1 - 52.2 0 - 52.3 0 - 52.4 1 - 52.5 1 - 53 1 - 53.1 1 - 53.2 1 - 54 0 - 54.1 1 - 54.2 0 - 54.3 1 - 54.4 0 - 55 1 - 55.1 1 - 55.2 1 - 55.3 0 - 55.4 1 - 56 0 - 56.1 1 - 56.2 1 - 56.3 0 - 56.4 0 - 56.5 1 - 57 1 - 57.1 1 - 57.2 0 - 57.3 0 - 58 1 - 58.1 1 - 58.2 1 - 58.3 1 - 58.4 1 - 58.5 1 - 59 0 - 59.1 1 - 60 0 - 61 1 - 61.1 1 - 61.2 1 - 61.3 0 - 61.4 1 - 62 1 - 62.1 0 - 62.2 0 - 62.3 1 - 63 0 - 63.1 1 - 64 1 - 65 1 - 65.1 1 - 65.2 0 - 65.3 0 - 66 1 - 66.1 0 - 66.2 0 - 67 0 - 68 0 - 68.1 0 - 68.2 0 - 68.3 0 - 68.4 1 - 69 1 - 70 1 - 70.1 1 - 71 1 - 71.1 1 - 71.2 0 - 71.3 0 - 71.4 0 - 72 1 - 72.1 1 - 72.2 1 - 72.3 0 - 72.4 0 - 72.5 1 - 73 1 - 74 1 - 75 0 - 76 1 - 76.1 1 - 76.2 1 - 77 1 - 78 1 - 79 0 - 79.1 1 - 79.2 0 - 80 1 - 80.1 0 - 80.2 1 - 81 1 - 81.1 1 - 81.2 1 - 81.3 1 - 82 1 - 82.1 1 - 82.2 0 - 83 1 - 83.1 0 - 83.2 0 - 83.3 1 - 84 1 - 84.1 0 - 85 0 - 85.1 0 - 85.2 1 - 85.3 1 - 85.4 1 - 85.5 1 - 86 0 - 86.1 1 - 86.2 1 - 86.3 0 - 86.4 1 - 86.5 0 - 87 0 - 87.1 1 - 87.2 0 - 88 0 - 88.1 0 - 88.2 0 - 88.3 0 - 89 1 - 90 0 - 90.1 1 - 90.2 1 - 90.3 0 - 91 0 - 91.1 0 - 91.2 1 - 92 1 - 93 0 - 93.1 1 - 93.2 0 - 93.3 1 - 93.4 0 - 94 1 - 94.1 0 - 94.2 1 - 94.3 0 - 94.4 0 - 94.5 0 - 95 1 - 95.1 1 - 95.2 0 - 96 1 - 96.1 0 - 96.2 0 - 96.3 0 - 96.4 0 - 96.5 1 - 97 0 - 97.1 0 - 98 0 - 98.1 0 - 98.2 0 - 99 1 - 99.1 1 - 99.2 1 - 100 0 - 100.1 0 - 100.2 1 - 100.3 1 - 100.4 1 - - $m0b2$mu_reg_binom - [1] 0 - - $m0b2$tau_reg_binom - [1] 1e-04 - - $m0b2$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0b2$shape_diag_RinvD - [1] "0.01" - - $m0b2$rate_diag_RinvD - [1] "0.001" - - - $m0b3 - $m0b3$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0b3$M_lvlone - b1 - 1 0 - 1.1 1 - 1.2 1 - 1.3 0 - 2 1 - 2.1 1 - 2.2 1 - 3 1 - 3.1 0 - 3.2 0 - 4 1 - 4.1 1 - 4.2 0 - 4.3 1 - 5 0 - 5.1 1 - 5.2 1 - 5.3 1 - 6 0 - 7 1 - 7.1 0 - 7.2 1 - 8 0 - 8.1 1 - 8.2 1 - 8.3 0 - 8.4 0 - 8.5 1 - 9 1 - 9.1 1 - 9.2 0 - 10 1 - 10.1 1 - 11 1 - 11.1 1 - 11.2 1 - 11.3 1 - 11.4 1 - 12 1 - 13 0 - 13.1 1 - 14 0 - 14.1 1 - 14.2 0 - 14.3 0 - 15 0 - 15.1 0 - 15.2 0 - 15.3 1 - 16 1 - 16.1 0 - 16.2 1 - 16.3 1 - 16.4 1 - 16.5 0 - 17 0 - 17.1 0 - 17.2 1 - 17.3 0 - 17.4 1 - 18 1 - 19 1 - 19.1 1 - 19.2 1 - 19.3 1 - 20 0 - 20.1 1 - 20.2 0 - 20.3 0 - 20.4 0 - 20.5 0 - 21 1 - 21.1 1 - 21.2 0 - 22 0 - 22.1 1 - 23 1 - 23.1 1 - 24 0 - 25 0 - 25.1 1 - 25.2 1 - 25.3 0 - 25.4 0 - 25.5 0 - 26 1 - 26.1 1 - 26.2 1 - 26.3 0 - 27 1 - 27.1 1 - 28 1 - 28.1 0 - 28.2 1 - 28.3 1 - 29 1 - 29.1 0 - 29.2 0 - 29.3 1 - 30 1 - 30.1 1 - 30.2 1 - 31 0 - 32 1 - 32.1 1 - 32.2 1 - 32.3 1 - 33 0 - 33.1 0 - 34 1 - 34.1 0 - 34.2 1 - 34.3 1 - 35 1 - 35.1 0 - 35.2 1 - 36 0 - 36.1 0 - 36.2 1 - 36.3 0 - 36.4 1 - 37 1 - 37.1 0 - 37.2 0 - 38 1 - 39 1 - 39.1 0 - 39.2 0 - 39.3 0 - 39.4 1 - 39.5 1 - 40 0 - 40.1 0 - 40.2 0 - 40.3 1 - 41 1 - 41.1 1 - 41.2 0 - 41.3 1 - 41.4 1 - 42 1 - 42.1 1 - 43 0 - 43.1 0 - 43.2 1 - 44 1 - 44.1 0 - 44.2 0 - 44.3 1 - 45 1 - 45.1 0 - 46 1 - 46.1 0 - 46.2 1 - 47 0 - 47.1 0 - 47.2 1 - 47.3 0 - 47.4 0 - 48 0 - 48.1 1 - 49 0 - 50 1 - 51 1 - 52 1 - 52.1 1 - 52.2 0 - 52.3 0 - 52.4 1 - 52.5 1 - 53 1 - 53.1 1 - 53.2 1 - 54 0 - 54.1 1 - 54.2 0 - 54.3 1 - 54.4 0 - 55 1 - 55.1 1 - 55.2 1 - 55.3 0 - 55.4 1 - 56 0 - 56.1 1 - 56.2 1 - 56.3 0 - 56.4 0 - 56.5 1 - 57 1 - 57.1 1 - 57.2 0 - 57.3 0 - 58 1 - 58.1 1 - 58.2 1 - 58.3 1 - 58.4 1 - 58.5 1 - 59 0 - 59.1 1 - 60 0 - 61 1 - 61.1 1 - 61.2 1 - 61.3 0 - 61.4 1 - 62 1 - 62.1 0 - 62.2 0 - 62.3 1 - 63 0 - 63.1 1 - 64 1 - 65 1 - 65.1 1 - 65.2 0 - 65.3 0 - 66 1 - 66.1 0 - 66.2 0 - 67 0 - 68 0 - 68.1 0 - 68.2 0 - 68.3 0 - 68.4 1 - 69 1 - 70 1 - 70.1 1 - 71 1 - 71.1 1 - 71.2 0 - 71.3 0 - 71.4 0 - 72 1 - 72.1 1 - 72.2 1 - 72.3 0 - 72.4 0 - 72.5 1 - 73 1 - 74 1 - 75 0 - 76 1 - 76.1 1 - 76.2 1 - 77 1 - 78 1 - 79 0 - 79.1 1 - 79.2 0 - 80 1 - 80.1 0 - 80.2 1 - 81 1 - 81.1 1 - 81.2 1 - 81.3 1 - 82 1 - 82.1 1 - 82.2 0 - 83 1 - 83.1 0 - 83.2 0 - 83.3 1 - 84 1 - 84.1 0 - 85 0 - 85.1 0 - 85.2 1 - 85.3 1 - 85.4 1 - 85.5 1 - 86 0 - 86.1 1 - 86.2 1 - 86.3 0 - 86.4 1 - 86.5 0 - 87 0 - 87.1 1 - 87.2 0 - 88 0 - 88.1 0 - 88.2 0 - 88.3 0 - 89 1 - 90 0 - 90.1 1 - 90.2 1 - 90.3 0 - 91 0 - 91.1 0 - 91.2 1 - 92 1 - 93 0 - 93.1 1 - 93.2 0 - 93.3 1 - 93.4 0 - 94 1 - 94.1 0 - 94.2 1 - 94.3 0 - 94.4 0 - 94.5 0 - 95 1 - 95.1 1 - 95.2 0 - 96 1 - 96.1 0 - 96.2 0 - 96.3 0 - 96.4 0 - 96.5 1 - 97 0 - 97.1 0 - 98 0 - 98.1 0 - 98.2 0 - 99 1 - 99.1 1 - 99.2 1 - 100 0 - 100.1 0 - 100.2 1 - 100.3 1 - 100.4 1 - - $m0b3$mu_reg_binom - [1] 0 - - $m0b3$tau_reg_binom - [1] 1e-04 - - $m0b3$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0b3$shape_diag_RinvD - [1] "0.01" - - $m0b3$rate_diag_RinvD - [1] "0.001" - - - $m0b4 - $m0b4$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0b4$M_lvlone - b1 - 1 0 - 1.1 1 - 1.2 1 - 1.3 0 - 2 1 - 2.1 1 - 2.2 1 - 3 1 - 3.1 0 - 3.2 0 - 4 1 - 4.1 1 - 4.2 0 - 4.3 1 - 5 0 - 5.1 1 - 5.2 1 - 5.3 1 - 6 0 - 7 1 - 7.1 0 - 7.2 1 - 8 0 - 8.1 1 - 8.2 1 - 8.3 0 - 8.4 0 - 8.5 1 - 9 1 - 9.1 1 - 9.2 0 - 10 1 - 10.1 1 - 11 1 - 11.1 1 - 11.2 1 - 11.3 1 - 11.4 1 - 12 1 - 13 0 - 13.1 1 - 14 0 - 14.1 1 - 14.2 0 - 14.3 0 - 15 0 - 15.1 0 - 15.2 0 - 15.3 1 - 16 1 - 16.1 0 - 16.2 1 - 16.3 1 - 16.4 1 - 16.5 0 - 17 0 - 17.1 0 - 17.2 1 - 17.3 0 - 17.4 1 - 18 1 - 19 1 - 19.1 1 - 19.2 1 - 19.3 1 - 20 0 - 20.1 1 - 20.2 0 - 20.3 0 - 20.4 0 - 20.5 0 - 21 1 - 21.1 1 - 21.2 0 - 22 0 - 22.1 1 - 23 1 - 23.1 1 - 24 0 - 25 0 - 25.1 1 - 25.2 1 - 25.3 0 - 25.4 0 - 25.5 0 - 26 1 - 26.1 1 - 26.2 1 - 26.3 0 - 27 1 - 27.1 1 - 28 1 - 28.1 0 - 28.2 1 - 28.3 1 - 29 1 - 29.1 0 - 29.2 0 - 29.3 1 - 30 1 - 30.1 1 - 30.2 1 - 31 0 - 32 1 - 32.1 1 - 32.2 1 - 32.3 1 - 33 0 - 33.1 0 - 34 1 - 34.1 0 - 34.2 1 - 34.3 1 - 35 1 - 35.1 0 - 35.2 1 - 36 0 - 36.1 0 - 36.2 1 - 36.3 0 - 36.4 1 - 37 1 - 37.1 0 - 37.2 0 - 38 1 - 39 1 - 39.1 0 - 39.2 0 - 39.3 0 - 39.4 1 - 39.5 1 - 40 0 - 40.1 0 - 40.2 0 - 40.3 1 - 41 1 - 41.1 1 - 41.2 0 - 41.3 1 - 41.4 1 - 42 1 - 42.1 1 - 43 0 - 43.1 0 - 43.2 1 - 44 1 - 44.1 0 - 44.2 0 - 44.3 1 - 45 1 - 45.1 0 - 46 1 - 46.1 0 - 46.2 1 - 47 0 - 47.1 0 - 47.2 1 - 47.3 0 - 47.4 0 - 48 0 - 48.1 1 - 49 0 - 50 1 - 51 1 - 52 1 - 52.1 1 - 52.2 0 - 52.3 0 - 52.4 1 - 52.5 1 - 53 1 - 53.1 1 - 53.2 1 - 54 0 - 54.1 1 - 54.2 0 - 54.3 1 - 54.4 0 - 55 1 - 55.1 1 - 55.2 1 - 55.3 0 - 55.4 1 - 56 0 - 56.1 1 - 56.2 1 - 56.3 0 - 56.4 0 - 56.5 1 - 57 1 - 57.1 1 - 57.2 0 - 57.3 0 - 58 1 - 58.1 1 - 58.2 1 - 58.3 1 - 58.4 1 - 58.5 1 - 59 0 - 59.1 1 - 60 0 - 61 1 - 61.1 1 - 61.2 1 - 61.3 0 - 61.4 1 - 62 1 - 62.1 0 - 62.2 0 - 62.3 1 - 63 0 - 63.1 1 - 64 1 - 65 1 - 65.1 1 - 65.2 0 - 65.3 0 - 66 1 - 66.1 0 - 66.2 0 - 67 0 - 68 0 - 68.1 0 - 68.2 0 - 68.3 0 - 68.4 1 - 69 1 - 70 1 - 70.1 1 - 71 1 - 71.1 1 - 71.2 0 - 71.3 0 - 71.4 0 - 72 1 - 72.1 1 - 72.2 1 - 72.3 0 - 72.4 0 - 72.5 1 - 73 1 - 74 1 - 75 0 - 76 1 - 76.1 1 - 76.2 1 - 77 1 - 78 1 - 79 0 - 79.1 1 - 79.2 0 - 80 1 - 80.1 0 - 80.2 1 - 81 1 - 81.1 1 - 81.2 1 - 81.3 1 - 82 1 - 82.1 1 - 82.2 0 - 83 1 - 83.1 0 - 83.2 0 - 83.3 1 - 84 1 - 84.1 0 - 85 0 - 85.1 0 - 85.2 1 - 85.3 1 - 85.4 1 - 85.5 1 - 86 0 - 86.1 1 - 86.2 1 - 86.3 0 - 86.4 1 - 86.5 0 - 87 0 - 87.1 1 - 87.2 0 - 88 0 - 88.1 0 - 88.2 0 - 88.3 0 - 89 1 - 90 0 - 90.1 1 - 90.2 1 - 90.3 0 - 91 0 - 91.1 0 - 91.2 1 - 92 1 - 93 0 - 93.1 1 - 93.2 0 - 93.3 1 - 93.4 0 - 94 1 - 94.1 0 - 94.2 1 - 94.3 0 - 94.4 0 - 94.5 0 - 95 1 - 95.1 1 - 95.2 0 - 96 1 - 96.1 0 - 96.2 0 - 96.3 0 - 96.4 0 - 96.5 1 - 97 0 - 97.1 0 - 98 0 - 98.1 0 - 98.2 0 - 99 1 - 99.1 1 - 99.2 1 - 100 0 - 100.1 0 - 100.2 1 - 100.3 1 - 100.4 1 - - $m0b4$mu_reg_binom - [1] 0 - - $m0b4$tau_reg_binom - [1] 1e-04 - - $m0b4$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0b4$shape_diag_RinvD - [1] "0.01" - - $m0b4$rate_diag_RinvD - [1] "0.001" - - - $m0c1 - $m0c1$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0c1$M_lvlone - L1 - 1 0.09647609 - 1.1 0.47743206 - 1.2 0.49307743 - 1.3 0.18468863 - 2 0.54595313 - 2.1 0.21966792 - 2.2 0.73654737 - 3 0.20862809 - 3.1 0.24312223 - 3.2 0.03051627 - 4 0.39499609 - 4.1 0.72632316 - 4.2 0.34199228 - 4.3 0.38062927 - 5 0.62202135 - 5.1 0.20305630 - 5.2 0.41717969 - 5.3 0.23980703 - 6 0.37653463 - 7 0.36356663 - 7.1 0.06266071 - 7.2 0.37849716 - 8 0.37802506 - 8.1 0.61143062 - 8.2 0.75648801 - 8.3 2.54406375 - 8.4 1.18637590 - 8.5 0.05930316 - 9 0.95013074 - 9.1 0.11917116 - 9.2 0.86629295 - 10 0.23914695 - 10.1 0.13708051 - 11 0.11067204 - 11.1 0.23176079 - 11.2 0.60038623 - 11.3 0.42684714 - 11.4 0.16458522 - 12 0.12861686 - 13 1.33377494 - 13.1 0.37267514 - 14 0.48728084 - 14.1 0.31792264 - 14.2 0.89257832 - 14.3 0.48509920 - 15 0.37711346 - 15.1 0.24850749 - 15.2 0.48117461 - 15.3 0.42758680 - 16 0.43666855 - 16.1 0.18190724 - 16.2 0.18617239 - 16.3 1.87047608 - 16.4 0.41864602 - 16.5 0.43588009 - 17 0.17925916 - 17.1 0.32367639 - 17.2 0.24912593 - 17.3 0.56230768 - 17.4 0.26182608 - 18 0.42338083 - 19 0.23371438 - 19.1 0.45720781 - 19.2 1.07923724 - 19.3 0.74433885 - 20 0.23860936 - 20.1 1.49001161 - 20.2 0.82847676 - 20.3 0.71062057 - 20.4 0.58928158 - 20.5 0.49204025 - 21 0.39710041 - 21.1 0.63253881 - 21.2 0.58877978 - 22 0.30440876 - 22.1 0.42787265 - 23 0.15078177 - 23.1 0.97104584 - 24 0.55355206 - 25 0.76006220 - 25.1 0.42500306 - 25.2 0.68011522 - 25.3 0.38187835 - 25.4 0.67265847 - 25.5 0.09078197 - 26 0.17032539 - 26.1 0.36699769 - 26.2 0.19300220 - 26.3 1.26993276 - 27 0.63999648 - 27.1 1.14153094 - 28 0.39991376 - 28.1 0.20658853 - 28.2 0.42519397 - 28.3 1.68848543 - 29 0.20853337 - 29.1 0.32240000 - 29.2 0.59527557 - 29.3 0.34253455 - 30 0.70885491 - 30.1 0.31107139 - 30.2 0.46423208 - 31 0.54603320 - 32 0.48896515 - 32.1 0.26838930 - 32.2 0.33314256 - 32.3 0.15482204 - 33 0.63379200 - 33.1 0.53403306 - 34 0.30684588 - 34.1 0.15596697 - 34.2 0.73177916 - 34.3 0.78232073 - 35 0.12725486 - 35.1 0.32104659 - 35.2 0.92993903 - 36 0.82634942 - 36.1 0.15790991 - 36.2 0.28319688 - 36.3 0.30894311 - 36.4 0.38835761 - 37 0.28006122 - 37.1 0.51936935 - 37.2 0.03553058 - 38 0.10984700 - 39 1.01908377 - 39.1 0.58760885 - 39.2 0.63292533 - 39.3 0.42095489 - 39.4 0.25220230 - 39.5 0.51242643 - 40 0.70636121 - 40.1 1.22834105 - 40.2 0.81839083 - 40.3 0.23540757 - 41 0.08592119 - 41.1 0.22834515 - 41.2 1.61636130 - 41.3 0.15342660 - 41.4 0.47650400 - 42 0.64398703 - 42.1 1.15130398 - 43 0.79292461 - 43.1 0.38506794 - 43.2 0.11139587 - 44 0.89129328 - 44.1 0.08958946 - 44.2 0.85701827 - 44.3 0.96417530 - 45 0.51097634 - 45.1 0.98340980 - 46 0.44798505 - 46.1 0.82655580 - 46.2 0.37637628 - 47 0.41876182 - 47.1 0.48389648 - 47.2 0.02396924 - 47.3 1.80138667 - 47.4 0.61109603 - 48 0.19473894 - 48.1 0.04006959 - 49 0.29560575 - 50 0.15625313 - 51 0.47908892 - 52 1.40786781 - 52.1 0.35019229 - 52.2 0.39332493 - 52.3 0.51225821 - 52.4 0.11419627 - 52.5 0.55575005 - 53 0.13011523 - 53.1 0.90571584 - 53.2 0.50906734 - 54 0.46031273 - 54.1 0.46156182 - 54.2 0.52071389 - 54.3 0.76983675 - 54.4 0.52623423 - 55 0.60555180 - 55.1 0.10776713 - 55.2 1.03837178 - 55.3 0.45001542 - 55.4 0.65395611 - 56 0.07535464 - 56.1 0.73328954 - 56.2 0.27578594 - 56.3 0.68719648 - 56.4 1.57220834 - 56.5 0.28753078 - 57 0.17289659 - 57.1 0.72170220 - 57.2 1.26500225 - 57.3 0.20213479 - 58 0.13611631 - 58.1 0.37311297 - 58.2 0.72470365 - 58.3 1.43014769 - 58.4 0.78817203 - 58.5 0.78387559 - 59 0.46747067 - 59.1 0.04947979 - 60 0.16059397 - 61 0.29220662 - 61.1 0.41535569 - 61.2 0.73742285 - 61.3 0.43320659 - 61.4 1.19954814 - 62 0.20260386 - 62.1 0.06652907 - 62.2 0.25063288 - 62.3 0.36290927 - 63 0.52314649 - 63.1 0.25699016 - 64 1.02878746 - 65 0.45575444 - 65.1 0.46306113 - 65.2 0.42269832 - 65.3 0.73172542 - 66 0.74765742 - 66.1 0.25888221 - 66.2 0.38244280 - 67 0.23644835 - 68 0.83195685 - 68.1 0.68395486 - 68.2 0.53889898 - 68.3 0.33762340 - 68.4 0.79922369 - 69 0.20260053 - 70 1.04535151 - 70.1 0.03979648 - 71 0.56397408 - 71.1 0.34854738 - 71.2 0.97913866 - 71.3 0.19630242 - 71.4 0.31230175 - 72 1.04871582 - 72.1 0.09370234 - 72.2 0.72454755 - 72.3 0.80705501 - 72.4 0.40641012 - 72.5 0.81634161 - 73 0.74327324 - 74 0.49202243 - 75 0.42954173 - 76 1.22280380 - 76.1 0.09905853 - 76.2 0.34132786 - 77 1.20980413 - 78 0.26184214 - 79 0.94287180 - 79.1 0.08463026 - 79.2 0.66769705 - 80 0.68766428 - 80.1 0.95426300 - 80.2 1.84421668 - 81 0.60279596 - 81.1 0.73369496 - 81.2 0.83514184 - 81.3 0.91767999 - 82 0.46992524 - 82.1 0.50002097 - 82.2 0.43711796 - 83 0.46587065 - 83.1 0.43364034 - 83.2 0.23196757 - 83.3 0.73616193 - 84 0.47791427 - 84.1 0.05551055 - 85 0.27482891 - 85.1 1.77694842 - 85.2 0.71141066 - 85.3 0.78806704 - 85.4 0.80223323 - 85.5 0.22172219 - 86 0.15018053 - 86.1 0.31597396 - 86.2 0.95686193 - 86.3 0.11022188 - 86.4 0.68477369 - 86.5 0.33125367 - 87 0.29289308 - 87.1 0.66197512 - 87.2 0.30055939 - 88 0.22930153 - 88.1 1.02206005 - 88.2 0.52724756 - 88.3 0.16276648 - 89 0.09190440 - 90 0.15333982 - 90.1 0.42756943 - 90.2 0.60354432 - 90.3 0.41070560 - 91 1.01739949 - 91.1 0.41121541 - 91.2 0.08932488 - 92 1.08669724 - 93 0.30303806 - 93.1 0.16800845 - 93.2 1.29098296 - 93.3 0.39962093 - 93.4 0.88339337 - 94 0.23233022 - 94.1 0.08638527 - 94.2 0.43737650 - 94.3 0.19800807 - 94.4 0.42942963 - 94.5 0.14150685 - 95 1.07323107 - 95.1 0.26037856 - 95.2 0.48623052 - 96 0.79796998 - 96.1 0.30822508 - 96.2 0.91060931 - 96.3 0.26069030 - 96.4 0.22889234 - 96.5 0.97046560 - 97 0.16946638 - 97.1 0.20265816 - 98 1.22465795 - 98.1 0.15250019 - 98.2 0.44675949 - 99 0.44238919 - 99.1 0.63211897 - 99.2 0.40140806 - 100 0.10484468 - 100.1 0.56141377 - 100.2 0.23655004 - 100.3 0.74552230 - 100.4 0.34230391 - - $m0c1$mu_reg_gamma - [1] 0 - - $m0c1$tau_reg_gamma - [1] 1e-04 - - $m0c1$shape_tau_gamma - [1] 0.01 - - $m0c1$rate_tau_gamma - [1] 0.01 - - $m0c1$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0c1$shape_diag_RinvD - [1] "0.01" - - $m0c1$rate_diag_RinvD - [1] "0.001" - - - $m0c2 - $m0c2$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0c2$M_lvlone - L1 - 1 0.09647609 - 1.1 0.47743206 - 1.2 0.49307743 - 1.3 0.18468863 - 2 0.54595313 - 2.1 0.21966792 - 2.2 0.73654737 - 3 0.20862809 - 3.1 0.24312223 - 3.2 0.03051627 - 4 0.39499609 - 4.1 0.72632316 - 4.2 0.34199228 - 4.3 0.38062927 - 5 0.62202135 - 5.1 0.20305630 - 5.2 0.41717969 - 5.3 0.23980703 - 6 0.37653463 - 7 0.36356663 - 7.1 0.06266071 - 7.2 0.37849716 - 8 0.37802506 - 8.1 0.61143062 - 8.2 0.75648801 - 8.3 2.54406375 - 8.4 1.18637590 - 8.5 0.05930316 - 9 0.95013074 - 9.1 0.11917116 - 9.2 0.86629295 - 10 0.23914695 - 10.1 0.13708051 - 11 0.11067204 - 11.1 0.23176079 - 11.2 0.60038623 - 11.3 0.42684714 - 11.4 0.16458522 - 12 0.12861686 - 13 1.33377494 - 13.1 0.37267514 - 14 0.48728084 - 14.1 0.31792264 - 14.2 0.89257832 - 14.3 0.48509920 - 15 0.37711346 - 15.1 0.24850749 - 15.2 0.48117461 - 15.3 0.42758680 - 16 0.43666855 - 16.1 0.18190724 - 16.2 0.18617239 - 16.3 1.87047608 - 16.4 0.41864602 - 16.5 0.43588009 - 17 0.17925916 - 17.1 0.32367639 - 17.2 0.24912593 - 17.3 0.56230768 - 17.4 0.26182608 - 18 0.42338083 - 19 0.23371438 - 19.1 0.45720781 - 19.2 1.07923724 - 19.3 0.74433885 - 20 0.23860936 - 20.1 1.49001161 - 20.2 0.82847676 - 20.3 0.71062057 - 20.4 0.58928158 - 20.5 0.49204025 - 21 0.39710041 - 21.1 0.63253881 - 21.2 0.58877978 - 22 0.30440876 - 22.1 0.42787265 - 23 0.15078177 - 23.1 0.97104584 - 24 0.55355206 - 25 0.76006220 - 25.1 0.42500306 - 25.2 0.68011522 - 25.3 0.38187835 - 25.4 0.67265847 - 25.5 0.09078197 - 26 0.17032539 - 26.1 0.36699769 - 26.2 0.19300220 - 26.3 1.26993276 - 27 0.63999648 - 27.1 1.14153094 - 28 0.39991376 - 28.1 0.20658853 - 28.2 0.42519397 - 28.3 1.68848543 - 29 0.20853337 - 29.1 0.32240000 - 29.2 0.59527557 - 29.3 0.34253455 - 30 0.70885491 - 30.1 0.31107139 - 30.2 0.46423208 - 31 0.54603320 - 32 0.48896515 - 32.1 0.26838930 - 32.2 0.33314256 - 32.3 0.15482204 - 33 0.63379200 - 33.1 0.53403306 - 34 0.30684588 - 34.1 0.15596697 - 34.2 0.73177916 - 34.3 0.78232073 - 35 0.12725486 - 35.1 0.32104659 - 35.2 0.92993903 - 36 0.82634942 - 36.1 0.15790991 - 36.2 0.28319688 - 36.3 0.30894311 - 36.4 0.38835761 - 37 0.28006122 - 37.1 0.51936935 - 37.2 0.03553058 - 38 0.10984700 - 39 1.01908377 - 39.1 0.58760885 - 39.2 0.63292533 - 39.3 0.42095489 - 39.4 0.25220230 - 39.5 0.51242643 - 40 0.70636121 - 40.1 1.22834105 - 40.2 0.81839083 - 40.3 0.23540757 - 41 0.08592119 - 41.1 0.22834515 - 41.2 1.61636130 - 41.3 0.15342660 - 41.4 0.47650400 - 42 0.64398703 - 42.1 1.15130398 - 43 0.79292461 - 43.1 0.38506794 - 43.2 0.11139587 - 44 0.89129328 - 44.1 0.08958946 - 44.2 0.85701827 - 44.3 0.96417530 - 45 0.51097634 - 45.1 0.98340980 - 46 0.44798505 - 46.1 0.82655580 - 46.2 0.37637628 - 47 0.41876182 - 47.1 0.48389648 - 47.2 0.02396924 - 47.3 1.80138667 - 47.4 0.61109603 - 48 0.19473894 - 48.1 0.04006959 - 49 0.29560575 - 50 0.15625313 - 51 0.47908892 - 52 1.40786781 - 52.1 0.35019229 - 52.2 0.39332493 - 52.3 0.51225821 - 52.4 0.11419627 - 52.5 0.55575005 - 53 0.13011523 - 53.1 0.90571584 - 53.2 0.50906734 - 54 0.46031273 - 54.1 0.46156182 - 54.2 0.52071389 - 54.3 0.76983675 - 54.4 0.52623423 - 55 0.60555180 - 55.1 0.10776713 - 55.2 1.03837178 - 55.3 0.45001542 - 55.4 0.65395611 - 56 0.07535464 - 56.1 0.73328954 - 56.2 0.27578594 - 56.3 0.68719648 - 56.4 1.57220834 - 56.5 0.28753078 - 57 0.17289659 - 57.1 0.72170220 - 57.2 1.26500225 - 57.3 0.20213479 - 58 0.13611631 - 58.1 0.37311297 - 58.2 0.72470365 - 58.3 1.43014769 - 58.4 0.78817203 - 58.5 0.78387559 - 59 0.46747067 - 59.1 0.04947979 - 60 0.16059397 - 61 0.29220662 - 61.1 0.41535569 - 61.2 0.73742285 - 61.3 0.43320659 - 61.4 1.19954814 - 62 0.20260386 - 62.1 0.06652907 - 62.2 0.25063288 - 62.3 0.36290927 - 63 0.52314649 - 63.1 0.25699016 - 64 1.02878746 - 65 0.45575444 - 65.1 0.46306113 - 65.2 0.42269832 - 65.3 0.73172542 - 66 0.74765742 - 66.1 0.25888221 - 66.2 0.38244280 - 67 0.23644835 - 68 0.83195685 - 68.1 0.68395486 - 68.2 0.53889898 - 68.3 0.33762340 - 68.4 0.79922369 - 69 0.20260053 - 70 1.04535151 - 70.1 0.03979648 - 71 0.56397408 - 71.1 0.34854738 - 71.2 0.97913866 - 71.3 0.19630242 - 71.4 0.31230175 - 72 1.04871582 - 72.1 0.09370234 - 72.2 0.72454755 - 72.3 0.80705501 - 72.4 0.40641012 - 72.5 0.81634161 - 73 0.74327324 - 74 0.49202243 - 75 0.42954173 - 76 1.22280380 - 76.1 0.09905853 - 76.2 0.34132786 - 77 1.20980413 - 78 0.26184214 - 79 0.94287180 - 79.1 0.08463026 - 79.2 0.66769705 - 80 0.68766428 - 80.1 0.95426300 - 80.2 1.84421668 - 81 0.60279596 - 81.1 0.73369496 - 81.2 0.83514184 - 81.3 0.91767999 - 82 0.46992524 - 82.1 0.50002097 - 82.2 0.43711796 - 83 0.46587065 - 83.1 0.43364034 - 83.2 0.23196757 - 83.3 0.73616193 - 84 0.47791427 - 84.1 0.05551055 - 85 0.27482891 - 85.1 1.77694842 - 85.2 0.71141066 - 85.3 0.78806704 - 85.4 0.80223323 - 85.5 0.22172219 - 86 0.15018053 - 86.1 0.31597396 - 86.2 0.95686193 - 86.3 0.11022188 - 86.4 0.68477369 - 86.5 0.33125367 - 87 0.29289308 - 87.1 0.66197512 - 87.2 0.30055939 - 88 0.22930153 - 88.1 1.02206005 - 88.2 0.52724756 - 88.3 0.16276648 - 89 0.09190440 - 90 0.15333982 - 90.1 0.42756943 - 90.2 0.60354432 - 90.3 0.41070560 - 91 1.01739949 - 91.1 0.41121541 - 91.2 0.08932488 - 92 1.08669724 - 93 0.30303806 - 93.1 0.16800845 - 93.2 1.29098296 - 93.3 0.39962093 - 93.4 0.88339337 - 94 0.23233022 - 94.1 0.08638527 - 94.2 0.43737650 - 94.3 0.19800807 - 94.4 0.42942963 - 94.5 0.14150685 - 95 1.07323107 - 95.1 0.26037856 - 95.2 0.48623052 - 96 0.79796998 - 96.1 0.30822508 - 96.2 0.91060931 - 96.3 0.26069030 - 96.4 0.22889234 - 96.5 0.97046560 - 97 0.16946638 - 97.1 0.20265816 - 98 1.22465795 - 98.1 0.15250019 - 98.2 0.44675949 - 99 0.44238919 - 99.1 0.63211897 - 99.2 0.40140806 - 100 0.10484468 - 100.1 0.56141377 - 100.2 0.23655004 - 100.3 0.74552230 - 100.4 0.34230391 - - $m0c2$mu_reg_gamma - [1] 0 - - $m0c2$tau_reg_gamma - [1] 1e-04 - - $m0c2$shape_tau_gamma - [1] 0.01 - - $m0c2$rate_tau_gamma - [1] 0.01 - - $m0c2$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0c2$shape_diag_RinvD - [1] "0.01" - - $m0c2$rate_diag_RinvD - [1] "0.001" - - - $m0d1 - $m0d1$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0d1$M_lvlone - p1 - 1 5 - 1.1 3 - 1.2 8 - 1.3 6 - 2 5 - 2.1 3 - 2.2 2 - 3 7 - 3.1 2 - 3.2 8 - 4 2 - 4.1 4 - 4.2 2 - 4.3 6 - 5 6 - 5.1 2 - 5.2 3 - 5.3 2 - 6 4 - 7 2 - 7.1 6 - 7.2 4 - 8 2 - 8.1 2 - 8.2 1 - 8.3 2 - 8.4 2 - 8.5 4 - 9 3 - 9.1 3 - 9.2 2 - 10 4 - 10.1 5 - 11 2 - 11.1 4 - 11.2 6 - 11.3 2 - 11.4 1 - 12 5 - 13 2 - 13.1 6 - 14 3 - 14.1 2 - 14.2 4 - 14.3 2 - 15 4 - 15.1 7 - 15.2 4 - 15.3 3 - 16 3 - 16.1 2 - 16.2 5 - 16.3 3 - 16.4 2 - 16.5 6 - 17 3 - 17.1 1 - 17.2 4 - 17.3 5 - 17.4 5 - 18 8 - 19 5 - 19.1 6 - 19.2 4 - 19.3 3 - 20 5 - 20.1 8 - 20.2 3 - 20.3 3 - 20.4 3 - 20.5 3 - 21 3 - 21.1 3 - 21.2 4 - 22 6 - 22.1 3 - 23 3 - 23.1 2 - 24 1 - 25 2 - 25.1 0 - 25.2 6 - 25.3 6 - 25.4 2 - 25.5 2 - 26 6 - 26.1 0 - 26.2 1 - 26.3 4 - 27 2 - 27.1 4 - 28 5 - 28.1 0 - 28.2 7 - 28.3 3 - 29 4 - 29.1 1 - 29.2 4 - 29.3 3 - 30 5 - 30.1 5 - 30.2 6 - 31 1 - 32 2 - 32.1 5 - 32.2 5 - 32.3 6 - 33 4 - 33.1 7 - 34 2 - 34.1 5 - 34.2 6 - 34.3 2 - 35 3 - 35.1 2 - 35.2 3 - 36 3 - 36.1 1 - 36.2 6 - 36.3 4 - 36.4 1 - 37 4 - 37.1 6 - 37.2 8 - 38 3 - 39 2 - 39.1 3 - 39.2 6 - 39.3 4 - 39.4 3 - 39.5 6 - 40 1 - 40.1 3 - 40.2 0 - 40.3 4 - 41 1 - 41.1 4 - 41.2 7 - 41.3 5 - 41.4 2 - 42 1 - 42.1 3 - 43 5 - 43.1 2 - 43.2 3 - 44 3 - 44.1 3 - 44.2 3 - 44.3 4 - 45 4 - 45.1 2 - 46 8 - 46.1 5 - 46.2 5 - 47 3 - 47.1 5 - 47.2 5 - 47.3 2 - 47.4 5 - 48 2 - 48.1 5 - 49 4 - 50 1 - 51 9 - 52 3 - 52.1 3 - 52.2 4 - 52.3 11 - 52.4 3 - 52.5 3 - 53 5 - 53.1 3 - 53.2 2 - 54 1 - 54.1 4 - 54.2 2 - 54.3 2 - 54.4 6 - 55 1 - 55.1 2 - 55.2 2 - 55.3 3 - 55.4 5 - 56 5 - 56.1 5 - 56.2 2 - 56.3 3 - 56.4 6 - 56.5 1 - 57 3 - 57.1 6 - 57.2 3 - 57.3 2 - 58 6 - 58.1 5 - 58.2 2 - 58.3 4 - 58.4 4 - 58.5 4 - 59 6 - 59.1 4 - 60 7 - 61 6 - 61.1 3 - 61.2 2 - 61.3 5 - 61.4 4 - 62 1 - 62.1 1 - 62.2 2 - 62.3 4 - 63 6 - 63.1 2 - 64 2 - 65 3 - 65.1 4 - 65.2 2 - 65.3 2 - 66 6 - 66.1 0 - 66.2 5 - 67 8 - 68 5 - 68.1 5 - 68.2 4 - 68.3 3 - 68.4 1 - 69 5 - 70 6 - 70.1 2 - 71 4 - 71.1 2 - 71.2 5 - 71.3 10 - 71.4 2 - 72 2 - 72.1 4 - 72.2 8 - 72.3 6 - 72.4 4 - 72.5 1 - 73 1 - 74 1 - 75 6 - 76 3 - 76.1 4 - 76.2 5 - 77 1 - 78 2 - 79 2 - 79.1 6 - 79.2 5 - 80 5 - 80.1 1 - 80.2 4 - 81 4 - 81.1 5 - 81.2 2 - 81.3 5 - 82 1 - 82.1 2 - 82.2 5 - 83 5 - 83.1 1 - 83.2 1 - 83.3 4 - 84 1 - 84.1 5 - 85 6 - 85.1 5 - 85.2 3 - 85.3 2 - 85.4 2 - 85.5 6 - 86 3 - 86.1 3 - 86.2 6 - 86.3 5 - 86.4 5 - 86.5 4 - 87 3 - 87.1 6 - 87.2 2 - 88 1 - 88.1 6 - 88.2 1 - 88.3 6 - 89 7 - 90 3 - 90.1 8 - 90.2 4 - 90.3 2 - 91 4 - 91.1 2 - 91.2 5 - 92 3 - 93 3 - 93.1 3 - 93.2 4 - 93.3 2 - 93.4 6 - 94 2 - 94.1 4 - 94.2 2 - 94.3 6 - 94.4 5 - 94.5 5 - 95 8 - 95.1 4 - 95.2 1 - 96 2 - 96.1 3 - 96.2 2 - 96.3 6 - 96.4 6 - 96.5 3 - 97 2 - 97.1 5 - 98 7 - 98.1 2 - 98.2 6 - 99 3 - 99.1 4 - 99.2 5 - 100 2 - 100.1 3 - 100.2 3 - 100.3 7 - 100.4 6 - - $m0d1$mu_reg_poisson - [1] 0 - - $m0d1$tau_reg_poisson - [1] 1e-04 - - $m0d1$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0d1$shape_diag_RinvD - [1] "0.01" - - $m0d1$rate_diag_RinvD - [1] "0.001" - - - $m0d2 - $m0d2$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0d2$M_lvlone - p1 - 1 5 - 1.1 3 - 1.2 8 - 1.3 6 - 2 5 - 2.1 3 - 2.2 2 - 3 7 - 3.1 2 - 3.2 8 - 4 2 - 4.1 4 - 4.2 2 - 4.3 6 - 5 6 - 5.1 2 - 5.2 3 - 5.3 2 - 6 4 - 7 2 - 7.1 6 - 7.2 4 - 8 2 - 8.1 2 - 8.2 1 - 8.3 2 - 8.4 2 - 8.5 4 - 9 3 - 9.1 3 - 9.2 2 - 10 4 - 10.1 5 - 11 2 - 11.1 4 - 11.2 6 - 11.3 2 - 11.4 1 - 12 5 - 13 2 - 13.1 6 - 14 3 - 14.1 2 - 14.2 4 - 14.3 2 - 15 4 - 15.1 7 - 15.2 4 - 15.3 3 - 16 3 - 16.1 2 - 16.2 5 - 16.3 3 - 16.4 2 - 16.5 6 - 17 3 - 17.1 1 - 17.2 4 - 17.3 5 - 17.4 5 - 18 8 - 19 5 - 19.1 6 - 19.2 4 - 19.3 3 - 20 5 - 20.1 8 - 20.2 3 - 20.3 3 - 20.4 3 - 20.5 3 - 21 3 - 21.1 3 - 21.2 4 - 22 6 - 22.1 3 - 23 3 - 23.1 2 - 24 1 - 25 2 - 25.1 0 - 25.2 6 - 25.3 6 - 25.4 2 - 25.5 2 - 26 6 - 26.1 0 - 26.2 1 - 26.3 4 - 27 2 - 27.1 4 - 28 5 - 28.1 0 - 28.2 7 - 28.3 3 - 29 4 - 29.1 1 - 29.2 4 - 29.3 3 - 30 5 - 30.1 5 - 30.2 6 - 31 1 - 32 2 - 32.1 5 - 32.2 5 - 32.3 6 - 33 4 - 33.1 7 - 34 2 - 34.1 5 - 34.2 6 - 34.3 2 - 35 3 - 35.1 2 - 35.2 3 - 36 3 - 36.1 1 - 36.2 6 - 36.3 4 - 36.4 1 - 37 4 - 37.1 6 - 37.2 8 - 38 3 - 39 2 - 39.1 3 - 39.2 6 - 39.3 4 - 39.4 3 - 39.5 6 - 40 1 - 40.1 3 - 40.2 0 - 40.3 4 - 41 1 - 41.1 4 - 41.2 7 - 41.3 5 - 41.4 2 - 42 1 - 42.1 3 - 43 5 - 43.1 2 - 43.2 3 - 44 3 - 44.1 3 - 44.2 3 - 44.3 4 - 45 4 - 45.1 2 - 46 8 - 46.1 5 - 46.2 5 - 47 3 - 47.1 5 - 47.2 5 - 47.3 2 - 47.4 5 - 48 2 - 48.1 5 - 49 4 - 50 1 - 51 9 - 52 3 - 52.1 3 - 52.2 4 - 52.3 11 - 52.4 3 - 52.5 3 - 53 5 - 53.1 3 - 53.2 2 - 54 1 - 54.1 4 - 54.2 2 - 54.3 2 - 54.4 6 - 55 1 - 55.1 2 - 55.2 2 - 55.3 3 - 55.4 5 - 56 5 - 56.1 5 - 56.2 2 - 56.3 3 - 56.4 6 - 56.5 1 - 57 3 - 57.1 6 - 57.2 3 - 57.3 2 - 58 6 - 58.1 5 - 58.2 2 - 58.3 4 - 58.4 4 - 58.5 4 - 59 6 - 59.1 4 - 60 7 - 61 6 - 61.1 3 - 61.2 2 - 61.3 5 - 61.4 4 - 62 1 - 62.1 1 - 62.2 2 - 62.3 4 - 63 6 - 63.1 2 - 64 2 - 65 3 - 65.1 4 - 65.2 2 - 65.3 2 - 66 6 - 66.1 0 - 66.2 5 - 67 8 - 68 5 - 68.1 5 - 68.2 4 - 68.3 3 - 68.4 1 - 69 5 - 70 6 - 70.1 2 - 71 4 - 71.1 2 - 71.2 5 - 71.3 10 - 71.4 2 - 72 2 - 72.1 4 - 72.2 8 - 72.3 6 - 72.4 4 - 72.5 1 - 73 1 - 74 1 - 75 6 - 76 3 - 76.1 4 - 76.2 5 - 77 1 - 78 2 - 79 2 - 79.1 6 - 79.2 5 - 80 5 - 80.1 1 - 80.2 4 - 81 4 - 81.1 5 - 81.2 2 - 81.3 5 - 82 1 - 82.1 2 - 82.2 5 - 83 5 - 83.1 1 - 83.2 1 - 83.3 4 - 84 1 - 84.1 5 - 85 6 - 85.1 5 - 85.2 3 - 85.3 2 - 85.4 2 - 85.5 6 - 86 3 - 86.1 3 - 86.2 6 - 86.3 5 - 86.4 5 - 86.5 4 - 87 3 - 87.1 6 - 87.2 2 - 88 1 - 88.1 6 - 88.2 1 - 88.3 6 - 89 7 - 90 3 - 90.1 8 - 90.2 4 - 90.3 2 - 91 4 - 91.1 2 - 91.2 5 - 92 3 - 93 3 - 93.1 3 - 93.2 4 - 93.3 2 - 93.4 6 - 94 2 - 94.1 4 - 94.2 2 - 94.3 6 - 94.4 5 - 94.5 5 - 95 8 - 95.1 4 - 95.2 1 - 96 2 - 96.1 3 - 96.2 2 - 96.3 6 - 96.4 6 - 96.5 3 - 97 2 - 97.1 5 - 98 7 - 98.1 2 - 98.2 6 - 99 3 - 99.1 4 - 99.2 5 - 100 2 - 100.1 3 - 100.2 3 - 100.3 7 - 100.4 6 - - $m0d2$mu_reg_poisson - [1] 0 - - $m0d2$tau_reg_poisson - [1] 1e-04 - - $m0d2$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0d2$shape_diag_RinvD - [1] "0.01" - - $m0d2$rate_diag_RinvD - [1] "0.001" - - - $m0e1 - $m0e1$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0e1$M_lvlone - L1 - 1 0.09647609 - 1.1 0.47743206 - 1.2 0.49307743 - 1.3 0.18468863 - 2 0.54595313 - 2.1 0.21966792 - 2.2 0.73654737 - 3 0.20862809 - 3.1 0.24312223 - 3.2 0.03051627 - 4 0.39499609 - 4.1 0.72632316 - 4.2 0.34199228 - 4.3 0.38062927 - 5 0.62202135 - 5.1 0.20305630 - 5.2 0.41717969 - 5.3 0.23980703 - 6 0.37653463 - 7 0.36356663 - 7.1 0.06266071 - 7.2 0.37849716 - 8 0.37802506 - 8.1 0.61143062 - 8.2 0.75648801 - 8.3 2.54406375 - 8.4 1.18637590 - 8.5 0.05930316 - 9 0.95013074 - 9.1 0.11917116 - 9.2 0.86629295 - 10 0.23914695 - 10.1 0.13708051 - 11 0.11067204 - 11.1 0.23176079 - 11.2 0.60038623 - 11.3 0.42684714 - 11.4 0.16458522 - 12 0.12861686 - 13 1.33377494 - 13.1 0.37267514 - 14 0.48728084 - 14.1 0.31792264 - 14.2 0.89257832 - 14.3 0.48509920 - 15 0.37711346 - 15.1 0.24850749 - 15.2 0.48117461 - 15.3 0.42758680 - 16 0.43666855 - 16.1 0.18190724 - 16.2 0.18617239 - 16.3 1.87047608 - 16.4 0.41864602 - 16.5 0.43588009 - 17 0.17925916 - 17.1 0.32367639 - 17.2 0.24912593 - 17.3 0.56230768 - 17.4 0.26182608 - 18 0.42338083 - 19 0.23371438 - 19.1 0.45720781 - 19.2 1.07923724 - 19.3 0.74433885 - 20 0.23860936 - 20.1 1.49001161 - 20.2 0.82847676 - 20.3 0.71062057 - 20.4 0.58928158 - 20.5 0.49204025 - 21 0.39710041 - 21.1 0.63253881 - 21.2 0.58877978 - 22 0.30440876 - 22.1 0.42787265 - 23 0.15078177 - 23.1 0.97104584 - 24 0.55355206 - 25 0.76006220 - 25.1 0.42500306 - 25.2 0.68011522 - 25.3 0.38187835 - 25.4 0.67265847 - 25.5 0.09078197 - 26 0.17032539 - 26.1 0.36699769 - 26.2 0.19300220 - 26.3 1.26993276 - 27 0.63999648 - 27.1 1.14153094 - 28 0.39991376 - 28.1 0.20658853 - 28.2 0.42519397 - 28.3 1.68848543 - 29 0.20853337 - 29.1 0.32240000 - 29.2 0.59527557 - 29.3 0.34253455 - 30 0.70885491 - 30.1 0.31107139 - 30.2 0.46423208 - 31 0.54603320 - 32 0.48896515 - 32.1 0.26838930 - 32.2 0.33314256 - 32.3 0.15482204 - 33 0.63379200 - 33.1 0.53403306 - 34 0.30684588 - 34.1 0.15596697 - 34.2 0.73177916 - 34.3 0.78232073 - 35 0.12725486 - 35.1 0.32104659 - 35.2 0.92993903 - 36 0.82634942 - 36.1 0.15790991 - 36.2 0.28319688 - 36.3 0.30894311 - 36.4 0.38835761 - 37 0.28006122 - 37.1 0.51936935 - 37.2 0.03553058 - 38 0.10984700 - 39 1.01908377 - 39.1 0.58760885 - 39.2 0.63292533 - 39.3 0.42095489 - 39.4 0.25220230 - 39.5 0.51242643 - 40 0.70636121 - 40.1 1.22834105 - 40.2 0.81839083 - 40.3 0.23540757 - 41 0.08592119 - 41.1 0.22834515 - 41.2 1.61636130 - 41.3 0.15342660 - 41.4 0.47650400 - 42 0.64398703 - 42.1 1.15130398 - 43 0.79292461 - 43.1 0.38506794 - 43.2 0.11139587 - 44 0.89129328 - 44.1 0.08958946 - 44.2 0.85701827 - 44.3 0.96417530 - 45 0.51097634 - 45.1 0.98340980 - 46 0.44798505 - 46.1 0.82655580 - 46.2 0.37637628 - 47 0.41876182 - 47.1 0.48389648 - 47.2 0.02396924 - 47.3 1.80138667 - 47.4 0.61109603 - 48 0.19473894 - 48.1 0.04006959 - 49 0.29560575 - 50 0.15625313 - 51 0.47908892 - 52 1.40786781 - 52.1 0.35019229 - 52.2 0.39332493 - 52.3 0.51225821 - 52.4 0.11419627 - 52.5 0.55575005 - 53 0.13011523 - 53.1 0.90571584 - 53.2 0.50906734 - 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71.1 0.34854738 - 71.2 0.97913866 - 71.3 0.19630242 - 71.4 0.31230175 - 72 1.04871582 - 72.1 0.09370234 - 72.2 0.72454755 - 72.3 0.80705501 - 72.4 0.40641012 - 72.5 0.81634161 - 73 0.74327324 - 74 0.49202243 - 75 0.42954173 - 76 1.22280380 - 76.1 0.09905853 - 76.2 0.34132786 - 77 1.20980413 - 78 0.26184214 - 79 0.94287180 - 79.1 0.08463026 - 79.2 0.66769705 - 80 0.68766428 - 80.1 0.95426300 - 80.2 1.84421668 - 81 0.60279596 - 81.1 0.73369496 - 81.2 0.83514184 - 81.3 0.91767999 - 82 0.46992524 - 82.1 0.50002097 - 82.2 0.43711796 - 83 0.46587065 - 83.1 0.43364034 - 83.2 0.23196757 - 83.3 0.73616193 - 84 0.47791427 - 84.1 0.05551055 - 85 0.27482891 - 85.1 1.77694842 - 85.2 0.71141066 - 85.3 0.78806704 - 85.4 0.80223323 - 85.5 0.22172219 - 86 0.15018053 - 86.1 0.31597396 - 86.2 0.95686193 - 86.3 0.11022188 - 86.4 0.68477369 - 86.5 0.33125367 - 87 0.29289308 - 87.1 0.66197512 - 87.2 0.30055939 - 88 0.22930153 - 88.1 1.02206005 - 88.2 0.52724756 - 88.3 0.16276648 - 89 0.09190440 - 90 0.15333982 - 90.1 0.42756943 - 90.2 0.60354432 - 90.3 0.41070560 - 91 1.01739949 - 91.1 0.41121541 - 91.2 0.08932488 - 92 1.08669724 - 93 0.30303806 - 93.1 0.16800845 - 93.2 1.29098296 - 93.3 0.39962093 - 93.4 0.88339337 - 94 0.23233022 - 94.1 0.08638527 - 94.2 0.43737650 - 94.3 0.19800807 - 94.4 0.42942963 - 94.5 0.14150685 - 95 1.07323107 - 95.1 0.26037856 - 95.2 0.48623052 - 96 0.79796998 - 96.1 0.30822508 - 96.2 0.91060931 - 96.3 0.26069030 - 96.4 0.22889234 - 96.5 0.97046560 - 97 0.16946638 - 97.1 0.20265816 - 98 1.22465795 - 98.1 0.15250019 - 98.2 0.44675949 - 99 0.44238919 - 99.1 0.63211897 - 99.2 0.40140806 - 100 0.10484468 - 100.1 0.56141377 - 100.2 0.23655004 - 100.3 0.74552230 - 100.4 0.34230391 - - $m0e1$mu_reg_norm - [1] 0 - - $m0e1$tau_reg_norm - [1] 1e-04 - - $m0e1$shape_tau_norm - [1] 0.01 - - $m0e1$rate_tau_norm - [1] 0.01 - - $m0e1$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0e1$shape_diag_RinvD - [1] "0.01" - - $m0e1$rate_diag_RinvD - [1] "0.001" - - - $m0f1 - $m0f1$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m0f1$M_lvlone - Be1 - 1 0.4480520 - 1.1 0.4872580 - 1.2 0.8042241 - 1.3 0.8554321 - 2 0.9060032 - 2.1 0.9275039 - 2.2 0.9684475 - 3 0.5305313 - 3.1 0.9121229 - 3.2 0.9822343 - 4 0.3989620 - 4.1 0.5799009 - 4.2 0.8662223 - 4.3 0.9158089 - 5 0.5896069 - 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41.3 0.9329089 - 41.4 0.9426326 - 42 0.4363467 - 42.1 0.9730745 - 43 0.4523650 - 43.1 0.5797085 - 43.2 0.8653434 - 44 0.5063579 - 44.1 0.8708165 - 44.2 0.9306269 - 44.3 0.9669009 - 45 0.3684179 - 45.1 0.7793063 - 46 0.6489748 - 46.1 0.8931511 - 46.2 0.9754655 - 47 0.4659563 - 47.1 0.8418508 - 47.2 0.9055038 - 47.3 0.9202183 - 47.4 0.9798157 - 48 0.8934160 - 48.1 0.8980019 - 49 0.8792169 - 50 0.6106779 - 51 0.6695505 - 52 0.8016848 - 52.1 0.9145302 - 52.2 0.9166014 - 52.3 0.9448693 - 52.4 0.9831856 - 52.5 0.9859644 - 53 0.4430250 - 53.1 0.9440152 - 53.2 0.9792363 - 54 0.6568450 - 54.1 0.7552906 - 54.2 0.8527773 - 54.3 0.8839761 - 54.4 0.9630372 - 55 0.4682570 - 55.1 0.5018449 - 55.2 0.8890551 - 55.3 0.9163416 - 55.4 0.9229283 - 56 0.6156368 - 56.1 0.8327518 - 56.2 0.8600168 - 56.3 0.9001284 - 56.4 0.9223855 - 56.5 0.9349592 - 57 0.3810809 - 57.1 0.3837051 - 57.2 0.6031393 - 57.3 0.8011333 - 58 0.6212946 - 58.1 0.7124804 - 58.2 0.7217629 - 58.3 0.8705746 - 58.4 0.8930050 - 58.5 0.9450905 - 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82.2 0.8693680 - 83 0.3360995 - 83.1 0.8976786 - 83.2 0.9156363 - 83.3 0.9825687 - 84 0.8794223 - 84.1 0.9307356 - 85 0.3930294 - 85.1 0.7324405 - 85.2 0.8756930 - 85.3 0.9189753 - 85.4 0.9613144 - 85.5 0.9776185 - 86 0.5224769 - 86.1 0.5632108 - 86.2 0.6209203 - 86.3 0.8068072 - 86.4 0.8449636 - 86.5 0.9553382 - 87 0.8762447 - 87.1 0.9368280 - 87.2 0.9775674 - 88 0.3258678 - 88.1 0.4960216 - 88.2 0.8541774 - 88.3 0.9290415 - 89 0.4802962 - 90 0.3626402 - 90.1 0.8658220 - 90.2 0.8734278 - 90.3 0.9161187 - 91 0.4759845 - 91.1 0.8685282 - 91.2 0.9827553 - 92 0.3397660 - 93 0.3869728 - 93.1 0.5736674 - 93.2 0.8522942 - 93.3 0.8955441 - 93.4 0.9764547 - 94 0.5306638 - 94.1 0.5815770 - 94.2 0.7718092 - 94.3 0.9125421 - 94.4 0.9138265 - 94.5 0.9747802 - 95 0.7844217 - 95.1 0.9640897 - 95.2 0.9787801 - 96 0.3324701 - 96.1 0.3553187 - 96.2 0.4854947 - 96.3 0.8098962 - 96.4 0.8170439 - 96.5 0.9709596 - 97 0.6156077 - 97.1 0.9857374 - 98 0.3662077 - 98.1 0.4202527 - 98.2 0.9407308 - 99 0.4075622 - 99.1 0.9811408 - 99.2 0.9861494 - 100 0.5819523 - 100.1 0.6840806 - 100.2 0.8040634 - 100.3 0.9583620 - 100.4 0.9805147 - - $m0f1$mu_reg_beta - [1] 0 - - $m0f1$tau_reg_beta - [1] 1e-04 - - $m0f1$shape_tau_beta - [1] 0.01 - - $m0f1$rate_tau_beta - [1] 0.01 - - $m0f1$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m0f1$shape_diag_RinvD - [1] "0.01" - - $m0f1$rate_diag_RinvD - [1] "0.001" - - - $m1a - $m1a$M_id - (Intercept) C1 - 1 1 0.7175865 - 2 1 0.7507170 - 3 1 0.7255954 - 4 1 0.7469352 - 5 1 0.7139120 - 6 1 0.7332505 - 7 1 0.7345929 - 8 1 0.7652589 - 9 1 0.7200622 - 10 1 0.7423879 - 11 1 0.7437448 - 12 1 0.7446470 - 13 1 0.7530186 - 14 1 0.7093137 - 15 1 0.7331192 - 16 1 0.7011390 - 17 1 0.7432395 - 18 1 0.7545191 - 19 1 0.7528487 - 20 1 0.7612865 - 21 1 0.7251719 - 22 1 0.7300630 - 23 1 0.7087249 - 24 1 0.7391938 - 25 1 0.7820641 - 26 1 0.7118298 - 27 1 0.7230857 - 28 1 0.7489353 - 29 1 0.7510888 - 30 1 0.7300717 - 31 1 0.7550721 - 32 1 0.7321898 - 33 1 0.7306414 - 34 1 0.7427216 - 35 1 0.7193042 - 36 1 0.7312888 - 37 1 0.7100436 - 38 1 0.7670184 - 39 1 0.7400449 - 40 1 0.7397304 - 41 1 0.7490966 - 42 1 0.7419274 - 43 1 0.7527810 - 44 1 0.7408315 - 45 1 0.7347550 - 46 1 0.7332398 - 47 1 0.7376481 - 48 1 0.7346179 - 49 1 0.7329402 - 50 1 0.7260436 - 51 1 0.7242910 - 52 1 0.7298067 - 53 1 0.7254741 - 54 1 0.7542067 - 55 1 0.7389952 - 56 1 0.7520638 - 57 1 0.7219958 - 58 1 0.7259632 - 59 1 0.7458606 - 60 1 0.7672421 - 61 1 0.7257179 - 62 1 0.7189892 - 63 1 0.7333356 - 64 1 0.7320243 - 65 1 0.7477711 - 66 1 0.7343974 - 67 1 0.7491624 - 68 1 0.7482736 - 69 1 0.7338267 - 70 1 0.7607742 - 71 1 0.7777600 - 72 1 0.7408143 - 73 1 0.7248271 - 74 1 0.7364916 - 75 1 0.7464926 - 76 1 0.7355430 - 77 1 0.7208449 - 78 1 0.7373573 - 79 1 0.7598079 - 80 1 0.7360415 - 81 1 0.7293932 - 82 1 0.7279309 - 83 1 0.7344643 - 84 1 0.7384350 - 85 1 0.7323716 - 86 1 0.7576597 - 87 1 0.7496139 - 88 1 0.7275239 - 89 1 0.7250648 - 90 1 0.7335262 - 91 1 0.7343980 - 92 1 0.7380425 - 93 1 0.7389460 - 94 1 0.7259951 - 95 1 0.7282840 - 96 1 0.7281676 - 97 1 0.7245642 - 98 1 0.7526938 - 99 1 0.7272309 - 100 1 0.7383460 - - $m1a$M_lvlone - y - 1 -13.0493856 - 1.1 -9.3335901 - 1.2 -22.3469852 - 1.3 -15.0417337 - 2 -12.0655434 - 2.1 -15.8674476 - 2.2 -7.8800006 - 3 -11.4820604 - 3.1 -10.5983220 - 3.2 -22.4519157 - 4 -1.2697775 - 4.1 -11.1215184 - 4.2 -3.6134138 - 4.3 -14.5982385 - 5 -6.8457515 - 5.1 -7.0551214 - 5.2 -12.3418980 - 5.3 -9.2366906 - 6 -5.1648211 - 7 -10.0599502 - 7.1 -18.3267285 - 7.2 -12.5138426 - 8 -1.6305331 - 8.1 -9.6520453 - 8.2 -1.5278462 - 8.3 -7.4172211 - 8.4 -7.1238609 - 8.5 -8.8706950 - 9 -0.1634429 - 9.1 -2.6034300 - 9.2 -6.7272369 - 10 -6.4172202 - 10.1 -11.4834569 - 11 -8.7911356 - 11.1 -19.6645080 - 11.2 -20.2030932 - 11.3 -21.3082176 - 11.4 -14.5802901 - 12 -15.2006287 - 13 0.8058816 - 13.1 -13.6379208 - 14 -15.3422873 - 14.1 -10.0965208 - 14.2 -16.6452027 - 14.3 -15.8389733 - 15 -8.9424594 - 15.1 -22.0101983 - 15.2 -7.3975599 - 15.3 -10.3567334 - 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86 -10.8756412 - 86.1 -12.0055331 - 86.2 -13.3724699 - 86.3 -13.3252145 - 86.4 -14.9191290 - 86.5 -17.7515546 - 87 -10.7027963 - 87.1 -22.4941954 - 87.2 -14.9616716 - 88 -2.2264493 - 88.1 -8.9626474 - 88.2 -2.5095281 - 88.3 -16.3345673 - 89 -11.0459647 - 90 -4.5610239 - 90.1 -11.7036651 - 90.2 -5.3838521 - 90.3 -4.1636999 - 91 -7.1462503 - 91.1 -12.8374475 - 91.2 -18.2576707 - 92 -6.4119222 - 93 5.2122168 - 93.1 3.1211725 - 93.2 -3.6841177 - 93.3 2.6223542 - 93.4 -11.1877696 - 94 -6.9602492 - 94.1 -7.4318416 - 94.2 -4.3498045 - 94.3 -11.6340088 - 94.4 -12.9357964 - 94.5 -14.7648530 - 95 -12.8849309 - 95.1 -9.7451502 - 95.2 -0.8535063 - 96 -4.9139832 - 96.1 -3.9582653 - 96.2 -9.6555492 - 96.3 -11.8690793 - 96.4 -11.0224373 - 96.5 -10.9530403 - 97 -9.8540471 - 97.1 -19.2262840 - 98 -11.9651231 - 98.1 -2.6515128 - 98.2 -12.2606382 - 99 -11.4720500 - 99.1 -14.0596866 - 99.2 -17.3939469 - 100 1.1005874 - 100.1 -3.8226248 - 100.2 -0.9123182 - 100.3 -15.8389474 - 100.4 -12.8093826 - - $m1a$spM_id - center scale - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m1a$mu_reg_norm - [1] 0 - - $m1a$tau_reg_norm - [1] 1e-04 - - $m1a$shape_tau_norm - [1] 0.01 - - $m1a$rate_tau_norm - [1] 0.01 - - $m1a$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m1a$shape_diag_RinvD - [1] "0.01" - - $m1a$rate_diag_RinvD - [1] "0.001" - - - $m1b - $m1b$M_id - (Intercept) C1 - 1 1 0.7175865 - 2 1 0.7507170 - 3 1 0.7255954 - 4 1 0.7469352 - 5 1 0.7139120 - 6 1 0.7332505 - 7 1 0.7345929 - 8 1 0.7652589 - 9 1 0.7200622 - 10 1 0.7423879 - 11 1 0.7437448 - 12 1 0.7446470 - 13 1 0.7530186 - 14 1 0.7093137 - 15 1 0.7331192 - 16 1 0.7011390 - 17 1 0.7432395 - 18 1 0.7545191 - 19 1 0.7528487 - 20 1 0.7612865 - 21 1 0.7251719 - 22 1 0.7300630 - 23 1 0.7087249 - 24 1 0.7391938 - 25 1 0.7820641 - 26 1 0.7118298 - 27 1 0.7230857 - 28 1 0.7489353 - 29 1 0.7510888 - 30 1 0.7300717 - 31 1 0.7550721 - 32 1 0.7321898 - 33 1 0.7306414 - 34 1 0.7427216 - 35 1 0.7193042 - 36 1 0.7312888 - 37 1 0.7100436 - 38 1 0.7670184 - 39 1 0.7400449 - 40 1 0.7397304 - 41 1 0.7490966 - 42 1 0.7419274 - 43 1 0.7527810 - 44 1 0.7408315 - 45 1 0.7347550 - 46 1 0.7332398 - 47 1 0.7376481 - 48 1 0.7346179 - 49 1 0.7329402 - 50 1 0.7260436 - 51 1 0.7242910 - 52 1 0.7298067 - 53 1 0.7254741 - 54 1 0.7542067 - 55 1 0.7389952 - 56 1 0.7520638 - 57 1 0.7219958 - 58 1 0.7259632 - 59 1 0.7458606 - 60 1 0.7672421 - 61 1 0.7257179 - 62 1 0.7189892 - 63 1 0.7333356 - 64 1 0.7320243 - 65 1 0.7477711 - 66 1 0.7343974 - 67 1 0.7491624 - 68 1 0.7482736 - 69 1 0.7338267 - 70 1 0.7607742 - 71 1 0.7777600 - 72 1 0.7408143 - 73 1 0.7248271 - 74 1 0.7364916 - 75 1 0.7464926 - 76 1 0.7355430 - 77 1 0.7208449 - 78 1 0.7373573 - 79 1 0.7598079 - 80 1 0.7360415 - 81 1 0.7293932 - 82 1 0.7279309 - 83 1 0.7344643 - 84 1 0.7384350 - 85 1 0.7323716 - 86 1 0.7576597 - 87 1 0.7496139 - 88 1 0.7275239 - 89 1 0.7250648 - 90 1 0.7335262 - 91 1 0.7343980 - 92 1 0.7380425 - 93 1 0.7389460 - 94 1 0.7259951 - 95 1 0.7282840 - 96 1 0.7281676 - 97 1 0.7245642 - 98 1 0.7526938 - 99 1 0.7272309 - 100 1 0.7383460 - - $m1b$M_lvlone - b1 - 1 0 - 1.1 1 - 1.2 1 - 1.3 0 - 2 1 - 2.1 1 - 2.2 1 - 3 1 - 3.1 0 - 3.2 0 - 4 1 - 4.1 1 - 4.2 0 - 4.3 1 - 5 0 - 5.1 1 - 5.2 1 - 5.3 1 - 6 0 - 7 1 - 7.1 0 - 7.2 1 - 8 0 - 8.1 1 - 8.2 1 - 8.3 0 - 8.4 0 - 8.5 1 - 9 1 - 9.1 1 - 9.2 0 - 10 1 - 10.1 1 - 11 1 - 11.1 1 - 11.2 1 - 11.3 1 - 11.4 1 - 12 1 - 13 0 - 13.1 1 - 14 0 - 14.1 1 - 14.2 0 - 14.3 0 - 15 0 - 15.1 0 - 15.2 0 - 15.3 1 - 16 1 - 16.1 0 - 16.2 1 - 16.3 1 - 16.4 1 - 16.5 0 - 17 0 - 17.1 0 - 17.2 1 - 17.3 0 - 17.4 1 - 18 1 - 19 1 - 19.1 1 - 19.2 1 - 19.3 1 - 20 0 - 20.1 1 - 20.2 0 - 20.3 0 - 20.4 0 - 20.5 0 - 21 1 - 21.1 1 - 21.2 0 - 22 0 - 22.1 1 - 23 1 - 23.1 1 - 24 0 - 25 0 - 25.1 1 - 25.2 1 - 25.3 0 - 25.4 0 - 25.5 0 - 26 1 - 26.1 1 - 26.2 1 - 26.3 0 - 27 1 - 27.1 1 - 28 1 - 28.1 0 - 28.2 1 - 28.3 1 - 29 1 - 29.1 0 - 29.2 0 - 29.3 1 - 30 1 - 30.1 1 - 30.2 1 - 31 0 - 32 1 - 32.1 1 - 32.2 1 - 32.3 1 - 33 0 - 33.1 0 - 34 1 - 34.1 0 - 34.2 1 - 34.3 1 - 35 1 - 35.1 0 - 35.2 1 - 36 0 - 36.1 0 - 36.2 1 - 36.3 0 - 36.4 1 - 37 1 - 37.1 0 - 37.2 0 - 38 1 - 39 1 - 39.1 0 - 39.2 0 - 39.3 0 - 39.4 1 - 39.5 1 - 40 0 - 40.1 0 - 40.2 0 - 40.3 1 - 41 1 - 41.1 1 - 41.2 0 - 41.3 1 - 41.4 1 - 42 1 - 42.1 1 - 43 0 - 43.1 0 - 43.2 1 - 44 1 - 44.1 0 - 44.2 0 - 44.3 1 - 45 1 - 45.1 0 - 46 1 - 46.1 0 - 46.2 1 - 47 0 - 47.1 0 - 47.2 1 - 47.3 0 - 47.4 0 - 48 0 - 48.1 1 - 49 0 - 50 1 - 51 1 - 52 1 - 52.1 1 - 52.2 0 - 52.3 0 - 52.4 1 - 52.5 1 - 53 1 - 53.1 1 - 53.2 1 - 54 0 - 54.1 1 - 54.2 0 - 54.3 1 - 54.4 0 - 55 1 - 55.1 1 - 55.2 1 - 55.3 0 - 55.4 1 - 56 0 - 56.1 1 - 56.2 1 - 56.3 0 - 56.4 0 - 56.5 1 - 57 1 - 57.1 1 - 57.2 0 - 57.3 0 - 58 1 - 58.1 1 - 58.2 1 - 58.3 1 - 58.4 1 - 58.5 1 - 59 0 - 59.1 1 - 60 0 - 61 1 - 61.1 1 - 61.2 1 - 61.3 0 - 61.4 1 - 62 1 - 62.1 0 - 62.2 0 - 62.3 1 - 63 0 - 63.1 1 - 64 1 - 65 1 - 65.1 1 - 65.2 0 - 65.3 0 - 66 1 - 66.1 0 - 66.2 0 - 67 0 - 68 0 - 68.1 0 - 68.2 0 - 68.3 0 - 68.4 1 - 69 1 - 70 1 - 70.1 1 - 71 1 - 71.1 1 - 71.2 0 - 71.3 0 - 71.4 0 - 72 1 - 72.1 1 - 72.2 1 - 72.3 0 - 72.4 0 - 72.5 1 - 73 1 - 74 1 - 75 0 - 76 1 - 76.1 1 - 76.2 1 - 77 1 - 78 1 - 79 0 - 79.1 1 - 79.2 0 - 80 1 - 80.1 0 - 80.2 1 - 81 1 - 81.1 1 - 81.2 1 - 81.3 1 - 82 1 - 82.1 1 - 82.2 0 - 83 1 - 83.1 0 - 83.2 0 - 83.3 1 - 84 1 - 84.1 0 - 85 0 - 85.1 0 - 85.2 1 - 85.3 1 - 85.4 1 - 85.5 1 - 86 0 - 86.1 1 - 86.2 1 - 86.3 0 - 86.4 1 - 86.5 0 - 87 0 - 87.1 1 - 87.2 0 - 88 0 - 88.1 0 - 88.2 0 - 88.3 0 - 89 1 - 90 0 - 90.1 1 - 90.2 1 - 90.3 0 - 91 0 - 91.1 0 - 91.2 1 - 92 1 - 93 0 - 93.1 1 - 93.2 0 - 93.3 1 - 93.4 0 - 94 1 - 94.1 0 - 94.2 1 - 94.3 0 - 94.4 0 - 94.5 0 - 95 1 - 95.1 1 - 95.2 0 - 96 1 - 96.1 0 - 96.2 0 - 96.3 0 - 96.4 0 - 96.5 1 - 97 0 - 97.1 0 - 98 0 - 98.1 0 - 98.2 0 - 99 1 - 99.1 1 - 99.2 1 - 100 0 - 100.1 0 - 100.2 1 - 100.3 1 - 100.4 1 - - $m1b$spM_id - center scale - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m1b$mu_reg_binom - [1] 0 - - $m1b$tau_reg_binom - [1] 1e-04 - - $m1b$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m1b$shape_diag_RinvD - [1] "0.01" - - $m1b$rate_diag_RinvD - [1] "0.001" - - - $m1c - $m1c$M_id - (Intercept) C1 - 1 1 0.7175865 - 2 1 0.7507170 - 3 1 0.7255954 - 4 1 0.7469352 - 5 1 0.7139120 - 6 1 0.7332505 - 7 1 0.7345929 - 8 1 0.7652589 - 9 1 0.7200622 - 10 1 0.7423879 - 11 1 0.7437448 - 12 1 0.7446470 - 13 1 0.7530186 - 14 1 0.7093137 - 15 1 0.7331192 - 16 1 0.7011390 - 17 1 0.7432395 - 18 1 0.7545191 - 19 1 0.7528487 - 20 1 0.7612865 - 21 1 0.7251719 - 22 1 0.7300630 - 23 1 0.7087249 - 24 1 0.7391938 - 25 1 0.7820641 - 26 1 0.7118298 - 27 1 0.7230857 - 28 1 0.7489353 - 29 1 0.7510888 - 30 1 0.7300717 - 31 1 0.7550721 - 32 1 0.7321898 - 33 1 0.7306414 - 34 1 0.7427216 - 35 1 0.7193042 - 36 1 0.7312888 - 37 1 0.7100436 - 38 1 0.7670184 - 39 1 0.7400449 - 40 1 0.7397304 - 41 1 0.7490966 - 42 1 0.7419274 - 43 1 0.7527810 - 44 1 0.7408315 - 45 1 0.7347550 - 46 1 0.7332398 - 47 1 0.7376481 - 48 1 0.7346179 - 49 1 0.7329402 - 50 1 0.7260436 - 51 1 0.7242910 - 52 1 0.7298067 - 53 1 0.7254741 - 54 1 0.7542067 - 55 1 0.7389952 - 56 1 0.7520638 - 57 1 0.7219958 - 58 1 0.7259632 - 59 1 0.7458606 - 60 1 0.7672421 - 61 1 0.7257179 - 62 1 0.7189892 - 63 1 0.7333356 - 64 1 0.7320243 - 65 1 0.7477711 - 66 1 0.7343974 - 67 1 0.7491624 - 68 1 0.7482736 - 69 1 0.7338267 - 70 1 0.7607742 - 71 1 0.7777600 - 72 1 0.7408143 - 73 1 0.7248271 - 74 1 0.7364916 - 75 1 0.7464926 - 76 1 0.7355430 - 77 1 0.7208449 - 78 1 0.7373573 - 79 1 0.7598079 - 80 1 0.7360415 - 81 1 0.7293932 - 82 1 0.7279309 - 83 1 0.7344643 - 84 1 0.7384350 - 85 1 0.7323716 - 86 1 0.7576597 - 87 1 0.7496139 - 88 1 0.7275239 - 89 1 0.7250648 - 90 1 0.7335262 - 91 1 0.7343980 - 92 1 0.7380425 - 93 1 0.7389460 - 94 1 0.7259951 - 95 1 0.7282840 - 96 1 0.7281676 - 97 1 0.7245642 - 98 1 0.7526938 - 99 1 0.7272309 - 100 1 0.7383460 - - $m1c$M_lvlone - L1 - 1 0.09647609 - 1.1 0.47743206 - 1.2 0.49307743 - 1.3 0.18468863 - 2 0.54595313 - 2.1 0.21966792 - 2.2 0.73654737 - 3 0.20862809 - 3.1 0.24312223 - 3.2 0.03051627 - 4 0.39499609 - 4.1 0.72632316 - 4.2 0.34199228 - 4.3 0.38062927 - 5 0.62202135 - 5.1 0.20305630 - 5.2 0.41717969 - 5.3 0.23980703 - 6 0.37653463 - 7 0.36356663 - 7.1 0.06266071 - 7.2 0.37849716 - 8 0.37802506 - 8.1 0.61143062 - 8.2 0.75648801 - 8.3 2.54406375 - 8.4 1.18637590 - 8.5 0.05930316 - 9 0.95013074 - 9.1 0.11917116 - 9.2 0.86629295 - 10 0.23914695 - 10.1 0.13708051 - 11 0.11067204 - 11.1 0.23176079 - 11.2 0.60038623 - 11.3 0.42684714 - 11.4 0.16458522 - 12 0.12861686 - 13 1.33377494 - 13.1 0.37267514 - 14 0.48728084 - 14.1 0.31792264 - 14.2 0.89257832 - 14.3 0.48509920 - 15 0.37711346 - 15.1 0.24850749 - 15.2 0.48117461 - 15.3 0.42758680 - 16 0.43666855 - 16.1 0.18190724 - 16.2 0.18617239 - 16.3 1.87047608 - 16.4 0.41864602 - 16.5 0.43588009 - 17 0.17925916 - 17.1 0.32367639 - 17.2 0.24912593 - 17.3 0.56230768 - 17.4 0.26182608 - 18 0.42338083 - 19 0.23371438 - 19.1 0.45720781 - 19.2 1.07923724 - 19.3 0.74433885 - 20 0.23860936 - 20.1 1.49001161 - 20.2 0.82847676 - 20.3 0.71062057 - 20.4 0.58928158 - 20.5 0.49204025 - 21 0.39710041 - 21.1 0.63253881 - 21.2 0.58877978 - 22 0.30440876 - 22.1 0.42787265 - 23 0.15078177 - 23.1 0.97104584 - 24 0.55355206 - 25 0.76006220 - 25.1 0.42500306 - 25.2 0.68011522 - 25.3 0.38187835 - 25.4 0.67265847 - 25.5 0.09078197 - 26 0.17032539 - 26.1 0.36699769 - 26.2 0.19300220 - 26.3 1.26993276 - 27 0.63999648 - 27.1 1.14153094 - 28 0.39991376 - 28.1 0.20658853 - 28.2 0.42519397 - 28.3 1.68848543 - 29 0.20853337 - 29.1 0.32240000 - 29.2 0.59527557 - 29.3 0.34253455 - 30 0.70885491 - 30.1 0.31107139 - 30.2 0.46423208 - 31 0.54603320 - 32 0.48896515 - 32.1 0.26838930 - 32.2 0.33314256 - 32.3 0.15482204 - 33 0.63379200 - 33.1 0.53403306 - 34 0.30684588 - 34.1 0.15596697 - 34.2 0.73177916 - 34.3 0.78232073 - 35 0.12725486 - 35.1 0.32104659 - 35.2 0.92993903 - 36 0.82634942 - 36.1 0.15790991 - 36.2 0.28319688 - 36.3 0.30894311 - 36.4 0.38835761 - 37 0.28006122 - 37.1 0.51936935 - 37.2 0.03553058 - 38 0.10984700 - 39 1.01908377 - 39.1 0.58760885 - 39.2 0.63292533 - 39.3 0.42095489 - 39.4 0.25220230 - 39.5 0.51242643 - 40 0.70636121 - 40.1 1.22834105 - 40.2 0.81839083 - 40.3 0.23540757 - 41 0.08592119 - 41.1 0.22834515 - 41.2 1.61636130 - 41.3 0.15342660 - 41.4 0.47650400 - 42 0.64398703 - 42.1 1.15130398 - 43 0.79292461 - 43.1 0.38506794 - 43.2 0.11139587 - 44 0.89129328 - 44.1 0.08958946 - 44.2 0.85701827 - 44.3 0.96417530 - 45 0.51097634 - 45.1 0.98340980 - 46 0.44798505 - 46.1 0.82655580 - 46.2 0.37637628 - 47 0.41876182 - 47.1 0.48389648 - 47.2 0.02396924 - 47.3 1.80138667 - 47.4 0.61109603 - 48 0.19473894 - 48.1 0.04006959 - 49 0.29560575 - 50 0.15625313 - 51 0.47908892 - 52 1.40786781 - 52.1 0.35019229 - 52.2 0.39332493 - 52.3 0.51225821 - 52.4 0.11419627 - 52.5 0.55575005 - 53 0.13011523 - 53.1 0.90571584 - 53.2 0.50906734 - 54 0.46031273 - 54.1 0.46156182 - 54.2 0.52071389 - 54.3 0.76983675 - 54.4 0.52623423 - 55 0.60555180 - 55.1 0.10776713 - 55.2 1.03837178 - 55.3 0.45001542 - 55.4 0.65395611 - 56 0.07535464 - 56.1 0.73328954 - 56.2 0.27578594 - 56.3 0.68719648 - 56.4 1.57220834 - 56.5 0.28753078 - 57 0.17289659 - 57.1 0.72170220 - 57.2 1.26500225 - 57.3 0.20213479 - 58 0.13611631 - 58.1 0.37311297 - 58.2 0.72470365 - 58.3 1.43014769 - 58.4 0.78817203 - 58.5 0.78387559 - 59 0.46747067 - 59.1 0.04947979 - 60 0.16059397 - 61 0.29220662 - 61.1 0.41535569 - 61.2 0.73742285 - 61.3 0.43320659 - 61.4 1.19954814 - 62 0.20260386 - 62.1 0.06652907 - 62.2 0.25063288 - 62.3 0.36290927 - 63 0.52314649 - 63.1 0.25699016 - 64 1.02878746 - 65 0.45575444 - 65.1 0.46306113 - 65.2 0.42269832 - 65.3 0.73172542 - 66 0.74765742 - 66.1 0.25888221 - 66.2 0.38244280 - 67 0.23644835 - 68 0.83195685 - 68.1 0.68395486 - 68.2 0.53889898 - 68.3 0.33762340 - 68.4 0.79922369 - 69 0.20260053 - 70 1.04535151 - 70.1 0.03979648 - 71 0.56397408 - 71.1 0.34854738 - 71.2 0.97913866 - 71.3 0.19630242 - 71.4 0.31230175 - 72 1.04871582 - 72.1 0.09370234 - 72.2 0.72454755 - 72.3 0.80705501 - 72.4 0.40641012 - 72.5 0.81634161 - 73 0.74327324 - 74 0.49202243 - 75 0.42954173 - 76 1.22280380 - 76.1 0.09905853 - 76.2 0.34132786 - 77 1.20980413 - 78 0.26184214 - 79 0.94287180 - 79.1 0.08463026 - 79.2 0.66769705 - 80 0.68766428 - 80.1 0.95426300 - 80.2 1.84421668 - 81 0.60279596 - 81.1 0.73369496 - 81.2 0.83514184 - 81.3 0.91767999 - 82 0.46992524 - 82.1 0.50002097 - 82.2 0.43711796 - 83 0.46587065 - 83.1 0.43364034 - 83.2 0.23196757 - 83.3 0.73616193 - 84 0.47791427 - 84.1 0.05551055 - 85 0.27482891 - 85.1 1.77694842 - 85.2 0.71141066 - 85.3 0.78806704 - 85.4 0.80223323 - 85.5 0.22172219 - 86 0.15018053 - 86.1 0.31597396 - 86.2 0.95686193 - 86.3 0.11022188 - 86.4 0.68477369 - 86.5 0.33125367 - 87 0.29289308 - 87.1 0.66197512 - 87.2 0.30055939 - 88 0.22930153 - 88.1 1.02206005 - 88.2 0.52724756 - 88.3 0.16276648 - 89 0.09190440 - 90 0.15333982 - 90.1 0.42756943 - 90.2 0.60354432 - 90.3 0.41070560 - 91 1.01739949 - 91.1 0.41121541 - 91.2 0.08932488 - 92 1.08669724 - 93 0.30303806 - 93.1 0.16800845 - 93.2 1.29098296 - 93.3 0.39962093 - 93.4 0.88339337 - 94 0.23233022 - 94.1 0.08638527 - 94.2 0.43737650 - 94.3 0.19800807 - 94.4 0.42942963 - 94.5 0.14150685 - 95 1.07323107 - 95.1 0.26037856 - 95.2 0.48623052 - 96 0.79796998 - 96.1 0.30822508 - 96.2 0.91060931 - 96.3 0.26069030 - 96.4 0.22889234 - 96.5 0.97046560 - 97 0.16946638 - 97.1 0.20265816 - 98 1.22465795 - 98.1 0.15250019 - 98.2 0.44675949 - 99 0.44238919 - 99.1 0.63211897 - 99.2 0.40140806 - 100 0.10484468 - 100.1 0.56141377 - 100.2 0.23655004 - 100.3 0.74552230 - 100.4 0.34230391 - - $m1c$spM_id - center scale - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m1c$mu_reg_gamma - [1] 0 - - $m1c$tau_reg_gamma - [1] 1e-04 - - $m1c$shape_tau_gamma - [1] 0.01 - - $m1c$rate_tau_gamma - [1] 0.01 - - $m1c$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m1c$shape_diag_RinvD - [1] "0.01" - - $m1c$rate_diag_RinvD - [1] "0.001" - - - $m1d - $m1d$M_id - (Intercept) C1 - 1 1 0.7175865 - 2 1 0.7507170 - 3 1 0.7255954 - 4 1 0.7469352 - 5 1 0.7139120 - 6 1 0.7332505 - 7 1 0.7345929 - 8 1 0.7652589 - 9 1 0.7200622 - 10 1 0.7423879 - 11 1 0.7437448 - 12 1 0.7446470 - 13 1 0.7530186 - 14 1 0.7093137 - 15 1 0.7331192 - 16 1 0.7011390 - 17 1 0.7432395 - 18 1 0.7545191 - 19 1 0.7528487 - 20 1 0.7612865 - 21 1 0.7251719 - 22 1 0.7300630 - 23 1 0.7087249 - 24 1 0.7391938 - 25 1 0.7820641 - 26 1 0.7118298 - 27 1 0.7230857 - 28 1 0.7489353 - 29 1 0.7510888 - 30 1 0.7300717 - 31 1 0.7550721 - 32 1 0.7321898 - 33 1 0.7306414 - 34 1 0.7427216 - 35 1 0.7193042 - 36 1 0.7312888 - 37 1 0.7100436 - 38 1 0.7670184 - 39 1 0.7400449 - 40 1 0.7397304 - 41 1 0.7490966 - 42 1 0.7419274 - 43 1 0.7527810 - 44 1 0.7408315 - 45 1 0.7347550 - 46 1 0.7332398 - 47 1 0.7376481 - 48 1 0.7346179 - 49 1 0.7329402 - 50 1 0.7260436 - 51 1 0.7242910 - 52 1 0.7298067 - 53 1 0.7254741 - 54 1 0.7542067 - 55 1 0.7389952 - 56 1 0.7520638 - 57 1 0.7219958 - 58 1 0.7259632 - 59 1 0.7458606 - 60 1 0.7672421 - 61 1 0.7257179 - 62 1 0.7189892 - 63 1 0.7333356 - 64 1 0.7320243 - 65 1 0.7477711 - 66 1 0.7343974 - 67 1 0.7491624 - 68 1 0.7482736 - 69 1 0.7338267 - 70 1 0.7607742 - 71 1 0.7777600 - 72 1 0.7408143 - 73 1 0.7248271 - 74 1 0.7364916 - 75 1 0.7464926 - 76 1 0.7355430 - 77 1 0.7208449 - 78 1 0.7373573 - 79 1 0.7598079 - 80 1 0.7360415 - 81 1 0.7293932 - 82 1 0.7279309 - 83 1 0.7344643 - 84 1 0.7384350 - 85 1 0.7323716 - 86 1 0.7576597 - 87 1 0.7496139 - 88 1 0.7275239 - 89 1 0.7250648 - 90 1 0.7335262 - 91 1 0.7343980 - 92 1 0.7380425 - 93 1 0.7389460 - 94 1 0.7259951 - 95 1 0.7282840 - 96 1 0.7281676 - 97 1 0.7245642 - 98 1 0.7526938 - 99 1 0.7272309 - 100 1 0.7383460 - - $m1d$M_lvlone - p1 - 1 5 - 1.1 3 - 1.2 8 - 1.3 6 - 2 5 - 2.1 3 - 2.2 2 - 3 7 - 3.1 2 - 3.2 8 - 4 2 - 4.1 4 - 4.2 2 - 4.3 6 - 5 6 - 5.1 2 - 5.2 3 - 5.3 2 - 6 4 - 7 2 - 7.1 6 - 7.2 4 - 8 2 - 8.1 2 - 8.2 1 - 8.3 2 - 8.4 2 - 8.5 4 - 9 3 - 9.1 3 - 9.2 2 - 10 4 - 10.1 5 - 11 2 - 11.1 4 - 11.2 6 - 11.3 2 - 11.4 1 - 12 5 - 13 2 - 13.1 6 - 14 3 - 14.1 2 - 14.2 4 - 14.3 2 - 15 4 - 15.1 7 - 15.2 4 - 15.3 3 - 16 3 - 16.1 2 - 16.2 5 - 16.3 3 - 16.4 2 - 16.5 6 - 17 3 - 17.1 1 - 17.2 4 - 17.3 5 - 17.4 5 - 18 8 - 19 5 - 19.1 6 - 19.2 4 - 19.3 3 - 20 5 - 20.1 8 - 20.2 3 - 20.3 3 - 20.4 3 - 20.5 3 - 21 3 - 21.1 3 - 21.2 4 - 22 6 - 22.1 3 - 23 3 - 23.1 2 - 24 1 - 25 2 - 25.1 0 - 25.2 6 - 25.3 6 - 25.4 2 - 25.5 2 - 26 6 - 26.1 0 - 26.2 1 - 26.3 4 - 27 2 - 27.1 4 - 28 5 - 28.1 0 - 28.2 7 - 28.3 3 - 29 4 - 29.1 1 - 29.2 4 - 29.3 3 - 30 5 - 30.1 5 - 30.2 6 - 31 1 - 32 2 - 32.1 5 - 32.2 5 - 32.3 6 - 33 4 - 33.1 7 - 34 2 - 34.1 5 - 34.2 6 - 34.3 2 - 35 3 - 35.1 2 - 35.2 3 - 36 3 - 36.1 1 - 36.2 6 - 36.3 4 - 36.4 1 - 37 4 - 37.1 6 - 37.2 8 - 38 3 - 39 2 - 39.1 3 - 39.2 6 - 39.3 4 - 39.4 3 - 39.5 6 - 40 1 - 40.1 3 - 40.2 0 - 40.3 4 - 41 1 - 41.1 4 - 41.2 7 - 41.3 5 - 41.4 2 - 42 1 - 42.1 3 - 43 5 - 43.1 2 - 43.2 3 - 44 3 - 44.1 3 - 44.2 3 - 44.3 4 - 45 4 - 45.1 2 - 46 8 - 46.1 5 - 46.2 5 - 47 3 - 47.1 5 - 47.2 5 - 47.3 2 - 47.4 5 - 48 2 - 48.1 5 - 49 4 - 50 1 - 51 9 - 52 3 - 52.1 3 - 52.2 4 - 52.3 11 - 52.4 3 - 52.5 3 - 53 5 - 53.1 3 - 53.2 2 - 54 1 - 54.1 4 - 54.2 2 - 54.3 2 - 54.4 6 - 55 1 - 55.1 2 - 55.2 2 - 55.3 3 - 55.4 5 - 56 5 - 56.1 5 - 56.2 2 - 56.3 3 - 56.4 6 - 56.5 1 - 57 3 - 57.1 6 - 57.2 3 - 57.3 2 - 58 6 - 58.1 5 - 58.2 2 - 58.3 4 - 58.4 4 - 58.5 4 - 59 6 - 59.1 4 - 60 7 - 61 6 - 61.1 3 - 61.2 2 - 61.3 5 - 61.4 4 - 62 1 - 62.1 1 - 62.2 2 - 62.3 4 - 63 6 - 63.1 2 - 64 2 - 65 3 - 65.1 4 - 65.2 2 - 65.3 2 - 66 6 - 66.1 0 - 66.2 5 - 67 8 - 68 5 - 68.1 5 - 68.2 4 - 68.3 3 - 68.4 1 - 69 5 - 70 6 - 70.1 2 - 71 4 - 71.1 2 - 71.2 5 - 71.3 10 - 71.4 2 - 72 2 - 72.1 4 - 72.2 8 - 72.3 6 - 72.4 4 - 72.5 1 - 73 1 - 74 1 - 75 6 - 76 3 - 76.1 4 - 76.2 5 - 77 1 - 78 2 - 79 2 - 79.1 6 - 79.2 5 - 80 5 - 80.1 1 - 80.2 4 - 81 4 - 81.1 5 - 81.2 2 - 81.3 5 - 82 1 - 82.1 2 - 82.2 5 - 83 5 - 83.1 1 - 83.2 1 - 83.3 4 - 84 1 - 84.1 5 - 85 6 - 85.1 5 - 85.2 3 - 85.3 2 - 85.4 2 - 85.5 6 - 86 3 - 86.1 3 - 86.2 6 - 86.3 5 - 86.4 5 - 86.5 4 - 87 3 - 87.1 6 - 87.2 2 - 88 1 - 88.1 6 - 88.2 1 - 88.3 6 - 89 7 - 90 3 - 90.1 8 - 90.2 4 - 90.3 2 - 91 4 - 91.1 2 - 91.2 5 - 92 3 - 93 3 - 93.1 3 - 93.2 4 - 93.3 2 - 93.4 6 - 94 2 - 94.1 4 - 94.2 2 - 94.3 6 - 94.4 5 - 94.5 5 - 95 8 - 95.1 4 - 95.2 1 - 96 2 - 96.1 3 - 96.2 2 - 96.3 6 - 96.4 6 - 96.5 3 - 97 2 - 97.1 5 - 98 7 - 98.1 2 - 98.2 6 - 99 3 - 99.1 4 - 99.2 5 - 100 2 - 100.1 3 - 100.2 3 - 100.3 7 - 100.4 6 - - $m1d$spM_id - center scale - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m1d$mu_reg_poisson - [1] 0 - - $m1d$tau_reg_poisson - [1] 1e-04 - - $m1d$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m1d$shape_diag_RinvD - [1] "0.01" - - $m1d$rate_diag_RinvD - [1] "0.001" - - - $m1e - $m1e$M_id - (Intercept) C1 - 1 1 0.7175865 - 2 1 0.7507170 - 3 1 0.7255954 - 4 1 0.7469352 - 5 1 0.7139120 - 6 1 0.7332505 - 7 1 0.7345929 - 8 1 0.7652589 - 9 1 0.7200622 - 10 1 0.7423879 - 11 1 0.7437448 - 12 1 0.7446470 - 13 1 0.7530186 - 14 1 0.7093137 - 15 1 0.7331192 - 16 1 0.7011390 - 17 1 0.7432395 - 18 1 0.7545191 - 19 1 0.7528487 - 20 1 0.7612865 - 21 1 0.7251719 - 22 1 0.7300630 - 23 1 0.7087249 - 24 1 0.7391938 - 25 1 0.7820641 - 26 1 0.7118298 - 27 1 0.7230857 - 28 1 0.7489353 - 29 1 0.7510888 - 30 1 0.7300717 - 31 1 0.7550721 - 32 1 0.7321898 - 33 1 0.7306414 - 34 1 0.7427216 - 35 1 0.7193042 - 36 1 0.7312888 - 37 1 0.7100436 - 38 1 0.7670184 - 39 1 0.7400449 - 40 1 0.7397304 - 41 1 0.7490966 - 42 1 0.7419274 - 43 1 0.7527810 - 44 1 0.7408315 - 45 1 0.7347550 - 46 1 0.7332398 - 47 1 0.7376481 - 48 1 0.7346179 - 49 1 0.7329402 - 50 1 0.7260436 - 51 1 0.7242910 - 52 1 0.7298067 - 53 1 0.7254741 - 54 1 0.7542067 - 55 1 0.7389952 - 56 1 0.7520638 - 57 1 0.7219958 - 58 1 0.7259632 - 59 1 0.7458606 - 60 1 0.7672421 - 61 1 0.7257179 - 62 1 0.7189892 - 63 1 0.7333356 - 64 1 0.7320243 - 65 1 0.7477711 - 66 1 0.7343974 - 67 1 0.7491624 - 68 1 0.7482736 - 69 1 0.7338267 - 70 1 0.7607742 - 71 1 0.7777600 - 72 1 0.7408143 - 73 1 0.7248271 - 74 1 0.7364916 - 75 1 0.7464926 - 76 1 0.7355430 - 77 1 0.7208449 - 78 1 0.7373573 - 79 1 0.7598079 - 80 1 0.7360415 - 81 1 0.7293932 - 82 1 0.7279309 - 83 1 0.7344643 - 84 1 0.7384350 - 85 1 0.7323716 - 86 1 0.7576597 - 87 1 0.7496139 - 88 1 0.7275239 - 89 1 0.7250648 - 90 1 0.7335262 - 91 1 0.7343980 - 92 1 0.7380425 - 93 1 0.7389460 - 94 1 0.7259951 - 95 1 0.7282840 - 96 1 0.7281676 - 97 1 0.7245642 - 98 1 0.7526938 - 99 1 0.7272309 - 100 1 0.7383460 - - $m1e$M_lvlone - L1 - 1 0.09647609 - 1.1 0.47743206 - 1.2 0.49307743 - 1.3 0.18468863 - 2 0.54595313 - 2.1 0.21966792 - 2.2 0.73654737 - 3 0.20862809 - 3.1 0.24312223 - 3.2 0.03051627 - 4 0.39499609 - 4.1 0.72632316 - 4.2 0.34199228 - 4.3 0.38062927 - 5 0.62202135 - 5.1 0.20305630 - 5.2 0.41717969 - 5.3 0.23980703 - 6 0.37653463 - 7 0.36356663 - 7.1 0.06266071 - 7.2 0.37849716 - 8 0.37802506 - 8.1 0.61143062 - 8.2 0.75648801 - 8.3 2.54406375 - 8.4 1.18637590 - 8.5 0.05930316 - 9 0.95013074 - 9.1 0.11917116 - 9.2 0.86629295 - 10 0.23914695 - 10.1 0.13708051 - 11 0.11067204 - 11.1 0.23176079 - 11.2 0.60038623 - 11.3 0.42684714 - 11.4 0.16458522 - 12 0.12861686 - 13 1.33377494 - 13.1 0.37267514 - 14 0.48728084 - 14.1 0.31792264 - 14.2 0.89257832 - 14.3 0.48509920 - 15 0.37711346 - 15.1 0.24850749 - 15.2 0.48117461 - 15.3 0.42758680 - 16 0.43666855 - 16.1 0.18190724 - 16.2 0.18617239 - 16.3 1.87047608 - 16.4 0.41864602 - 16.5 0.43588009 - 17 0.17925916 - 17.1 0.32367639 - 17.2 0.24912593 - 17.3 0.56230768 - 17.4 0.26182608 - 18 0.42338083 - 19 0.23371438 - 19.1 0.45720781 - 19.2 1.07923724 - 19.3 0.74433885 - 20 0.23860936 - 20.1 1.49001161 - 20.2 0.82847676 - 20.3 0.71062057 - 20.4 0.58928158 - 20.5 0.49204025 - 21 0.39710041 - 21.1 0.63253881 - 21.2 0.58877978 - 22 0.30440876 - 22.1 0.42787265 - 23 0.15078177 - 23.1 0.97104584 - 24 0.55355206 - 25 0.76006220 - 25.1 0.42500306 - 25.2 0.68011522 - 25.3 0.38187835 - 25.4 0.67265847 - 25.5 0.09078197 - 26 0.17032539 - 26.1 0.36699769 - 26.2 0.19300220 - 26.3 1.26993276 - 27 0.63999648 - 27.1 1.14153094 - 28 0.39991376 - 28.1 0.20658853 - 28.2 0.42519397 - 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46.1 0.82655580 - 46.2 0.37637628 - 47 0.41876182 - 47.1 0.48389648 - 47.2 0.02396924 - 47.3 1.80138667 - 47.4 0.61109603 - 48 0.19473894 - 48.1 0.04006959 - 49 0.29560575 - 50 0.15625313 - 51 0.47908892 - 52 1.40786781 - 52.1 0.35019229 - 52.2 0.39332493 - 52.3 0.51225821 - 52.4 0.11419627 - 52.5 0.55575005 - 53 0.13011523 - 53.1 0.90571584 - 53.2 0.50906734 - 54 0.46031273 - 54.1 0.46156182 - 54.2 0.52071389 - 54.3 0.76983675 - 54.4 0.52623423 - 55 0.60555180 - 55.1 0.10776713 - 55.2 1.03837178 - 55.3 0.45001542 - 55.4 0.65395611 - 56 0.07535464 - 56.1 0.73328954 - 56.2 0.27578594 - 56.3 0.68719648 - 56.4 1.57220834 - 56.5 0.28753078 - 57 0.17289659 - 57.1 0.72170220 - 57.2 1.26500225 - 57.3 0.20213479 - 58 0.13611631 - 58.1 0.37311297 - 58.2 0.72470365 - 58.3 1.43014769 - 58.4 0.78817203 - 58.5 0.78387559 - 59 0.46747067 - 59.1 0.04947979 - 60 0.16059397 - 61 0.29220662 - 61.1 0.41535569 - 61.2 0.73742285 - 61.3 0.43320659 - 61.4 1.19954814 - 62 0.20260386 - 62.1 0.06652907 - 62.2 0.25063288 - 62.3 0.36290927 - 63 0.52314649 - 63.1 0.25699016 - 64 1.02878746 - 65 0.45575444 - 65.1 0.46306113 - 65.2 0.42269832 - 65.3 0.73172542 - 66 0.74765742 - 66.1 0.25888221 - 66.2 0.38244280 - 67 0.23644835 - 68 0.83195685 - 68.1 0.68395486 - 68.2 0.53889898 - 68.3 0.33762340 - 68.4 0.79922369 - 69 0.20260053 - 70 1.04535151 - 70.1 0.03979648 - 71 0.56397408 - 71.1 0.34854738 - 71.2 0.97913866 - 71.3 0.19630242 - 71.4 0.31230175 - 72 1.04871582 - 72.1 0.09370234 - 72.2 0.72454755 - 72.3 0.80705501 - 72.4 0.40641012 - 72.5 0.81634161 - 73 0.74327324 - 74 0.49202243 - 75 0.42954173 - 76 1.22280380 - 76.1 0.09905853 - 76.2 0.34132786 - 77 1.20980413 - 78 0.26184214 - 79 0.94287180 - 79.1 0.08463026 - 79.2 0.66769705 - 80 0.68766428 - 80.1 0.95426300 - 80.2 1.84421668 - 81 0.60279596 - 81.1 0.73369496 - 81.2 0.83514184 - 81.3 0.91767999 - 82 0.46992524 - 82.1 0.50002097 - 82.2 0.43711796 - 83 0.46587065 - 83.1 0.43364034 - 83.2 0.23196757 - 83.3 0.73616193 - 84 0.47791427 - 84.1 0.05551055 - 85 0.27482891 - 85.1 1.77694842 - 85.2 0.71141066 - 85.3 0.78806704 - 85.4 0.80223323 - 85.5 0.22172219 - 86 0.15018053 - 86.1 0.31597396 - 86.2 0.95686193 - 86.3 0.11022188 - 86.4 0.68477369 - 86.5 0.33125367 - 87 0.29289308 - 87.1 0.66197512 - 87.2 0.30055939 - 88 0.22930153 - 88.1 1.02206005 - 88.2 0.52724756 - 88.3 0.16276648 - 89 0.09190440 - 90 0.15333982 - 90.1 0.42756943 - 90.2 0.60354432 - 90.3 0.41070560 - 91 1.01739949 - 91.1 0.41121541 - 91.2 0.08932488 - 92 1.08669724 - 93 0.30303806 - 93.1 0.16800845 - 93.2 1.29098296 - 93.3 0.39962093 - 93.4 0.88339337 - 94 0.23233022 - 94.1 0.08638527 - 94.2 0.43737650 - 94.3 0.19800807 - 94.4 0.42942963 - 94.5 0.14150685 - 95 1.07323107 - 95.1 0.26037856 - 95.2 0.48623052 - 96 0.79796998 - 96.1 0.30822508 - 96.2 0.91060931 - 96.3 0.26069030 - 96.4 0.22889234 - 96.5 0.97046560 - 97 0.16946638 - 97.1 0.20265816 - 98 1.22465795 - 98.1 0.15250019 - 98.2 0.44675949 - 99 0.44238919 - 99.1 0.63211897 - 99.2 0.40140806 - 100 0.10484468 - 100.1 0.56141377 - 100.2 0.23655004 - 100.3 0.74552230 - 100.4 0.34230391 - - $m1e$spM_id - center scale - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m1e$mu_reg_norm - [1] 0 - - $m1e$tau_reg_norm - [1] 1e-04 - - $m1e$shape_tau_norm - [1] 0.01 - - $m1e$rate_tau_norm - [1] 0.01 - - $m1e$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m1e$shape_diag_RinvD - [1] "0.01" - - $m1e$rate_diag_RinvD - [1] "0.001" - - - $m1f - $m1f$M_id - (Intercept) C1 - 1 1 0.7175865 - 2 1 0.7507170 - 3 1 0.7255954 - 4 1 0.7469352 - 5 1 0.7139120 - 6 1 0.7332505 - 7 1 0.7345929 - 8 1 0.7652589 - 9 1 0.7200622 - 10 1 0.7423879 - 11 1 0.7437448 - 12 1 0.7446470 - 13 1 0.7530186 - 14 1 0.7093137 - 15 1 0.7331192 - 16 1 0.7011390 - 17 1 0.7432395 - 18 1 0.7545191 - 19 1 0.7528487 - 20 1 0.7612865 - 21 1 0.7251719 - 22 1 0.7300630 - 23 1 0.7087249 - 24 1 0.7391938 - 25 1 0.7820641 - 26 1 0.7118298 - 27 1 0.7230857 - 28 1 0.7489353 - 29 1 0.7510888 - 30 1 0.7300717 - 31 1 0.7550721 - 32 1 0.7321898 - 33 1 0.7306414 - 34 1 0.7427216 - 35 1 0.7193042 - 36 1 0.7312888 - 37 1 0.7100436 - 38 1 0.7670184 - 39 1 0.7400449 - 40 1 0.7397304 - 41 1 0.7490966 - 42 1 0.7419274 - 43 1 0.7527810 - 44 1 0.7408315 - 45 1 0.7347550 - 46 1 0.7332398 - 47 1 0.7376481 - 48 1 0.7346179 - 49 1 0.7329402 - 50 1 0.7260436 - 51 1 0.7242910 - 52 1 0.7298067 - 53 1 0.7254741 - 54 1 0.7542067 - 55 1 0.7389952 - 56 1 0.7520638 - 57 1 0.7219958 - 58 1 0.7259632 - 59 1 0.7458606 - 60 1 0.7672421 - 61 1 0.7257179 - 62 1 0.7189892 - 63 1 0.7333356 - 64 1 0.7320243 - 65 1 0.7477711 - 66 1 0.7343974 - 67 1 0.7491624 - 68 1 0.7482736 - 69 1 0.7338267 - 70 1 0.7607742 - 71 1 0.7777600 - 72 1 0.7408143 - 73 1 0.7248271 - 74 1 0.7364916 - 75 1 0.7464926 - 76 1 0.7355430 - 77 1 0.7208449 - 78 1 0.7373573 - 79 1 0.7598079 - 80 1 0.7360415 - 81 1 0.7293932 - 82 1 0.7279309 - 83 1 0.7344643 - 84 1 0.7384350 - 85 1 0.7323716 - 86 1 0.7576597 - 87 1 0.7496139 - 88 1 0.7275239 - 89 1 0.7250648 - 90 1 0.7335262 - 91 1 0.7343980 - 92 1 0.7380425 - 93 1 0.7389460 - 94 1 0.7259951 - 95 1 0.7282840 - 96 1 0.7281676 - 97 1 0.7245642 - 98 1 0.7526938 - 99 1 0.7272309 - 100 1 0.7383460 - - $m1f$M_lvlone - Be1 - 1 0.4480520 - 1.1 0.4872580 - 1.2 0.8042241 - 1.3 0.8554321 - 2 0.9060032 - 2.1 0.9275039 - 2.2 0.9684475 - 3 0.5305313 - 3.1 0.9121229 - 3.2 0.9822343 - 4 0.3989620 - 4.1 0.5799009 - 4.2 0.8662223 - 4.3 0.9158089 - 5 0.5896069 - 5.1 0.7459908 - 5.2 0.8891508 - 5.3 0.8907166 - 6 0.7404475 - 7 0.9290914 - 7.1 0.9510258 - 7.2 0.9826571 - 8 0.5888906 - 8.1 0.7383562 - 8.2 0.7412208 - 8.3 0.8882677 - 8.4 0.9307178 - 8.5 0.9751765 - 9 0.5598906 - 9.1 0.9000440 - 9.2 0.9835368 - 10 0.8256582 - 10.1 0.9686602 - 11 0.6081450 - 11.1 0.6203091 - 11.2 0.7109057 - 11.3 0.9335259 - 11.4 0.9831774 - 12 0.5534331 - 13 0.3337862 - 13.1 0.9431649 - 14 0.9653479 - 14.1 0.9772848 - 14.2 0.9806705 - 14.3 0.9816445 - 15 0.4519208 - 15.1 0.6121121 - 15.2 0.6848939 - 15.3 0.9850242 - 16 0.6319642 - 16.1 0.8660451 - 16.2 0.8755852 - 16.3 0.9456980 - 16.4 0.9552169 - 16.5 0.9638766 - 17 0.7004195 - 17.1 0.8447710 - 17.2 0.9074097 - 17.3 0.9301938 - 17.4 0.9579581 - 18 0.8432895 - 19 0.5558578 - 19.1 0.5971935 - 19.2 0.8186257 - 19.3 0.9694859 - 20 0.7222660 - 20.1 0.7300751 - 20.2 0.8161188 - 20.3 0.8175187 - 20.4 0.9387767 - 20.5 0.9680716 - 21 0.7248177 - 21.1 0.9030819 - 21.2 0.9553646 - 22 0.8506311 - 22.1 0.9192797 - 23 0.6969316 - 23.1 0.8359296 - 24 0.8898412 - 25 0.4393270 - 25.1 0.6952775 - 25.2 0.7013550 - 25.3 0.9229146 - 25.4 0.9642968 - 25.5 0.9668809 - 26 0.3844839 - 26.1 0.8498397 - 26.2 0.9472023 - 26.3 0.9698339 - 27 0.9513160 - 27.1 0.9713089 - 28 0.4565391 - 28.1 0.8854882 - 28.2 0.9695846 - 28.3 0.9763767 - 29 0.6079730 - 29.1 0.7332778 - 29.2 0.7807345 - 29.3 0.9344282 - 30 0.8225127 - 30.1 0.9460257 - 30.2 0.9470397 - 31 0.9745123 - 32 0.7195703 - 32.1 0.8984963 - 32.2 0.9033895 - 32.3 0.9700494 - 33 0.3271062 - 33.1 0.9386866 - 34 0.6807359 - 34.1 0.9561254 - 34.2 0.9594764 - 34.3 0.9614131 - 35 0.6479695 - 35.1 0.6917668 - 35.2 0.9777582 - 36 0.4952571 - 36.1 0.7438280 - 36.2 0.7493185 - 36.3 0.9721512 - 36.4 0.9799281 - 37 0.7844567 - 37.1 0.9505294 - 37.2 0.9629006 - 38 0.5537002 - 39 0.4880363 - 39.1 0.5405940 - 39.2 0.6377289 - 39.3 0.6902395 - 39.4 0.9200815 - 39.5 0.9676849 - 40 0.5970791 - 40.1 0.8759223 - 40.2 0.9088713 - 40.3 0.9808585 - 41 0.7657773 - 41.1 0.9203076 - 41.2 0.9265998 - 41.3 0.9329089 - 41.4 0.9426326 - 42 0.4363467 - 42.1 0.9730745 - 43 0.4523650 - 43.1 0.5797085 - 43.2 0.8653434 - 44 0.5063579 - 44.1 0.8708165 - 44.2 0.9306269 - 44.3 0.9669009 - 45 0.3684179 - 45.1 0.7793063 - 46 0.6489748 - 46.1 0.8931511 - 46.2 0.9754655 - 47 0.4659563 - 47.1 0.8418508 - 47.2 0.9055038 - 47.3 0.9202183 - 47.4 0.9798157 - 48 0.8934160 - 48.1 0.8980019 - 49 0.8792169 - 50 0.6106779 - 51 0.6695505 - 52 0.8016848 - 52.1 0.9145302 - 52.2 0.9166014 - 52.3 0.9448693 - 52.4 0.9831856 - 52.5 0.9859644 - 53 0.4430250 - 53.1 0.9440152 - 53.2 0.9792363 - 54 0.6568450 - 54.1 0.7552906 - 54.2 0.8527773 - 54.3 0.8839761 - 54.4 0.9630372 - 55 0.4682570 - 55.1 0.5018449 - 55.2 0.8890551 - 55.3 0.9163416 - 55.4 0.9229283 - 56 0.6156368 - 56.1 0.8327518 - 56.2 0.8600168 - 56.3 0.9001284 - 56.4 0.9223855 - 56.5 0.9349592 - 57 0.3810809 - 57.1 0.3837051 - 57.2 0.6031393 - 57.3 0.8011333 - 58 0.6212946 - 58.1 0.7124804 - 58.2 0.7217629 - 58.3 0.8705746 - 58.4 0.8930050 - 58.5 0.9450905 - 59 0.7607033 - 59.1 0.9856252 - 60 0.8926604 - 61 0.4989113 - 61.1 0.8310345 - 61.2 0.8559453 - 61.3 0.9203703 - 61.4 0.9466752 - 62 0.4538041 - 62.1 0.4949445 - 62.2 0.9393143 - 62.3 0.9834371 - 63 0.8885881 - 63.1 0.9620223 - 64 0.9672991 - 65 0.4899624 - 65.1 0.7820160 - 65.2 0.9141166 - 65.3 0.9204984 - 66 0.9404727 - 66.1 0.9540581 - 66.2 0.9613658 - 67 0.9684363 - 68 0.3499904 - 68.1 0.7374372 - 68.2 0.7860111 - 68.3 0.8995662 - 68.4 0.9641669 - 69 0.9680556 - 70 0.3631962 - 70.1 0.4309940 - 71 0.4991001 - 71.1 0.6705385 - 71.2 0.9643633 - 71.3 0.9806792 - 71.4 0.9810444 - 72 0.5476810 - 72.1 0.6080648 - 72.2 0.7596830 - 72.3 0.9396045 - 72.4 0.9501505 - 72.5 0.9659276 - 73 0.9797107 - 74 0.6739684 - 75 0.9245569 - 76 0.7449652 - 76.1 0.9716113 - 76.2 0.9857034 - 77 0.5312239 - 78 0.5214249 - 79 0.3314961 - 79.1 0.8430143 - 79.2 0.9266576 - 80 0.5405270 - 80.1 0.6473533 - 80.2 0.8876091 - 81 0.3275558 - 81.1 0.5529946 - 81.2 0.9109145 - 81.3 0.9319014 - 82 0.6572741 - 82.1 0.7373364 - 82.2 0.8693680 - 83 0.3360995 - 83.1 0.8976786 - 83.2 0.9156363 - 83.3 0.9825687 - 84 0.8794223 - 84.1 0.9307356 - 85 0.3930294 - 85.1 0.7324405 - 85.2 0.8756930 - 85.3 0.9189753 - 85.4 0.9613144 - 85.5 0.9776185 - 86 0.5224769 - 86.1 0.5632108 - 86.2 0.6209203 - 86.3 0.8068072 - 86.4 0.8449636 - 86.5 0.9553382 - 87 0.8762447 - 87.1 0.9368280 - 87.2 0.9775674 - 88 0.3258678 - 88.1 0.4960216 - 88.2 0.8541774 - 88.3 0.9290415 - 89 0.4802962 - 90 0.3626402 - 90.1 0.8658220 - 90.2 0.8734278 - 90.3 0.9161187 - 91 0.4759845 - 91.1 0.8685282 - 91.2 0.9827553 - 92 0.3397660 - 93 0.3869728 - 93.1 0.5736674 - 93.2 0.8522942 - 93.3 0.8955441 - 93.4 0.9764547 - 94 0.5306638 - 94.1 0.5815770 - 94.2 0.7718092 - 94.3 0.9125421 - 94.4 0.9138265 - 94.5 0.9747802 - 95 0.7844217 - 95.1 0.9640897 - 95.2 0.9787801 - 96 0.3324701 - 96.1 0.3553187 - 96.2 0.4854947 - 96.3 0.8098962 - 96.4 0.8170439 - 96.5 0.9709596 - 97 0.6156077 - 97.1 0.9857374 - 98 0.3662077 - 98.1 0.4202527 - 98.2 0.9407308 - 99 0.4075622 - 99.1 0.9811408 - 99.2 0.9861494 - 100 0.5819523 - 100.1 0.6840806 - 100.2 0.8040634 - 100.3 0.9583620 - 100.4 0.9805147 - - $m1f$spM_id - center scale - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m1f$mu_reg_beta - [1] 0 - - $m1f$tau_reg_beta - [1] 1e-04 - - $m1f$shape_tau_beta - [1] 0.01 - - $m1f$rate_tau_beta - [1] 0.01 - - $m1f$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m1f$shape_diag_RinvD - [1] "0.01" - - $m1f$rate_diag_RinvD - [1] "0.001" - - - $m2a - $m2a$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 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[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - 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[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m2b$shape_diag_RinvD - [1] "0.01" - - $m2b$rate_diag_RinvD - [1] "0.001" - - - $m2c - $m2c$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 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[1] 1e-04 - - $m2c$shape_tau_norm - [1] 0.01 - - $m2c$rate_tau_norm - [1] 0.01 - - $m2c$mu_reg_gamma - [1] 0 - - $m2c$tau_reg_gamma - [1] 1e-04 - - $m2c$shape_tau_gamma - [1] 0.01 - - $m2c$rate_tau_gamma - [1] 0.01 - - $m2c$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m2c$shape_diag_RinvD - [1] "0.01" - - $m2c$rate_diag_RinvD - [1] "0.001" - - - $m2d - $m2d$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 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96.1 NA -0.32504815 - 96.2 5 -0.20395970 - 96.3 1 -0.06221501 - 96.4 0 -0.14801097 - 96.5 3 -0.28658893 - 97 4 -0.34484656 - 97.1 2 -0.35658805 - 98 3 -0.36913003 - 98.1 NA NA - 98.2 NA -0.17154225 - 99 5 -0.24753132 - 99.1 NA -0.27947829 - 99.2 NA -0.09033035 - 100 NA -0.17326698 - 100.1 4 NA - 100.2 NA -0.12072016 - 100.3 4 -0.27657520 - 100.4 NA -0.14631556 - - $m2d$spM_lvlone - center scale - p2 2.7125749 1.6247402 - c2 -0.2237158 0.1059527 - - $m2d$mu_reg_norm - [1] 0 - - $m2d$tau_reg_norm - [1] 1e-04 - - $m2d$shape_tau_norm - [1] 0.01 - - $m2d$rate_tau_norm - [1] 0.01 - - $m2d$mu_reg_poisson - [1] 0 - - $m2d$tau_reg_poisson - [1] 1e-04 - - $m2d$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m2d$shape_diag_RinvD - [1] "0.01" - - $m2d$rate_diag_RinvD - [1] "0.001" - - - $m2e - $m2e$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m2e$M_lvlone - L1mis c2 - 1 1.38634787 NA - 1.1 0.79402906 -0.08061445 - 1.2 0.53603334 -0.26523782 - 1.3 0.24129804 -0.30260393 - 2 NA -0.33443795 - 2.1 0.31668065 -0.11819800 - 2.2 0.37114414 -0.31532280 - 3 0.54680608 -0.12920657 - 3.1 0.28280274 NA - 3.2 0.76277262 NA - 4 0.56100366 -0.31177403 - 4.1 0.38514140 -0.23894886 - 4.2 0.04026174 -0.15533613 - 4.3 0.16025873 -0.14644545 - 5 0.21080161 -0.28360457 - 5.1 0.36665700 -0.20135143 - 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[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m2e$shape_diag_RinvD - [1] "0.01" - - $m2e$rate_diag_RinvD - [1] "0.001" - - - $m2f - $m2f$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m2f$M_lvlone - Be2 c2 - 1 4.596628e-06 NA - 1.1 2.296427e-04 -0.08061445 - 1.2 3.455922e-10 -0.26523782 - 1.3 9.618613e-07 -0.30260393 - 2 NA -0.33443795 - 2.1 1.065639e-07 -0.11819800 - 2.2 1.320730e-03 -0.31532280 - 3 9.707820e-06 -0.12920657 - 3.1 3.645271e-05 NA - 3.2 NA NA - 4 5.555794e-01 -0.31177403 - 4.1 6.853316e-06 -0.23894886 - 4.2 6.324951e-02 -0.15533613 - 4.3 4.330745e-07 -0.14644545 - 5 NA -0.28360457 - 5.1 6.556812e-04 -0.20135143 - 5.2 6.963312e-06 -0.28293375 - 5.3 1.159006e-04 NA - 6 1.509745e-02 -0.08617066 - 7 NA -0.22243495 - 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[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m3c$shape_diag_RinvD - [1] "0.01" - - $m3c$rate_diag_RinvD - [1] "0.001" - - - $m3d - $m3d$M_id - C2 (Intercept) - 1 -1.381594459 1 - 2 0.344426024 1 - 3 NA 1 - 4 -0.228910007 1 - 5 NA 1 - 6 -2.143955482 1 - 7 -1.156567023 1 - 8 -0.598827660 1 - 9 NA 1 - 10 -1.006719032 1 - 11 0.239801450 1 - 12 -1.064969789 1 - 13 -0.538082688 1 - 14 NA 1 - 15 -1.781049276 1 - 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[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m3d$shape_diag_RinvD - [1] "0.01" - - $m3d$rate_diag_RinvD - [1] "0.001" - - - $m3e - $m3e$M_id - C2 (Intercept) - 1 -1.381594459 1 - 2 0.344426024 1 - 3 NA 1 - 4 -0.228910007 1 - 5 NA 1 - 6 -2.143955482 1 - 7 -1.156567023 1 - 8 -0.598827660 1 - 9 NA 1 - 10 -1.006719032 1 - 11 0.239801450 1 - 12 -1.064969789 1 - 13 -0.538082688 1 - 14 NA 1 - 15 -1.781049276 1 - 16 NA 1 - 17 NA 1 - 18 -0.014579883 1 - 19 -2.121550136 1 - 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88.2 0.48037889 - 88.3 0.97755681 - 89 0.70242369 - 90 0.40042977 - 90.1 0.63975731 - 90.2 0.33412775 - 90.3 0.38399003 - 91 0.58250391 - 91.1 0.13223217 - 91.2 0.46613305 - 92 0.18997862 - 93 1.05243347 - 93.1 0.01479757 - 93.2 0.50955172 - 93.3 0.78122514 - 93.4 0.63940704 - 94 0.45596305 - 94.1 0.41610667 - 94.2 0.52744298 - 94.3 0.70890756 - 94.4 0.84412478 - 94.5 0.21166602 - 95 0.57713135 - 95.1 0.44400207 - 95.2 0.42397776 - 96 0.72391015 - 96.1 0.32593738 - 96.2 0.23249511 - 96.3 1.01679990 - 96.4 0.92267953 - 96.5 0.83843412 - 97 0.47151154 - 97.1 0.15596614 - 98 0.05179545 - 98.1 0.47332096 - 98.2 0.19706341 - 99 0.22574556 - 99.1 1.00732330 - 99.2 0.09749127 - 100 0.22857989 - 100.1 0.39548654 - 100.2 NA - 100.3 0.32695372 - 100.4 0.10043925 - - $m3e$spM_id - center scale - C2 -0.6240921 0.6857108 - (Intercept) NA NA - - $m3e$mu_reg_norm - [1] 0 - - $m3e$tau_reg_norm - [1] 1e-04 - - $m3e$shape_tau_norm - [1] 0.01 - - $m3e$rate_tau_norm - [1] 0.01 - - $m3e$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m3e$shape_diag_RinvD - [1] "0.01" - - $m3e$rate_diag_RinvD - [1] "0.001" - - - $m3f - $m3f$M_id - C2 (Intercept) - 1 -1.381594459 1 - 2 0.344426024 1 - 3 NA 1 - 4 -0.228910007 1 - 5 NA 1 - 6 -2.143955482 1 - 7 -1.156567023 1 - 8 -0.598827660 1 - 9 NA 1 - 10 -1.006719032 1 - 11 0.239801450 1 - 12 -1.064969789 1 - 13 -0.538082688 1 - 14 NA 1 - 15 -1.781049276 1 - 16 NA 1 - 17 NA 1 - 18 -0.014579883 1 - 19 -2.121550136 1 - 20 NA 1 - 21 -0.363239698 1 - 22 -0.121568514 1 - 23 -0.951271111 1 - 24 NA 1 - 25 -0.974288621 1 - 26 -1.130632418 1 - 27 0.114339868 1 - 28 0.238334648 1 - 29 0.840744958 1 - 30 NA 1 - 31 NA 1 - 32 -1.466312154 1 - 33 -0.637352277 1 - 34 NA 1 - 35 NA 1 - 36 NA 1 - 37 NA 1 - 38 NA 1 - 39 0.006728205 1 - 40 NA 1 - 41 -1.663281353 1 - 42 0.161184794 1 - 43 0.457939180 1 - 44 -0.307070331 1 - 45 NA 1 - 46 -1.071668276 1 - 47 -0.814751321 1 - 48 -0.547630662 1 - 49 NA 1 - 50 -1.350213782 1 - 51 0.719054706 1 - 52 NA 1 - 53 -1.207130750 1 - 54 NA 1 - 55 -0.408600991 1 - 56 -0.271380529 1 - 57 -1.361925974 1 - 58 NA 1 - 59 NA 1 - 60 -0.323712205 1 - 61 NA 1 - 62 NA 1 - 63 -1.386906880 1 - 64 NA 1 - 65 NA 1 - 66 -0.565191691 1 - 67 -0.382899912 1 - 68 NA 1 - 69 -0.405642769 1 - 70 NA 1 - 71 -0.843748427 1 - 72 0.116003683 1 - 73 -0.778634325 1 - 74 NA 1 - 75 NA 1 - 76 NA 1 - 77 -0.632974758 1 - 78 NA 1 - 79 -0.778064615 1 - 80 NA 1 - 81 NA 1 - 82 -0.246123253 1 - 83 -1.239659782 1 - 84 -0.467772280 1 - 85 NA 1 - 86 -2.160485036 1 - 87 -0.657675572 1 - 88 NA 1 - 89 -0.696710744 1 - 90 NA 1 - 91 -0.179395847 1 - 92 -0.441545568 1 - 93 -0.685799334 1 - 94 NA 1 - 95 0.191929445 1 - 96 NA 1 - 97 -0.069760671 1 - 98 NA 1 - 99 NA 1 - 100 NA 1 - - $m3f$M_lvlone - Be2 - 1 4.596628e-06 - 1.1 2.296427e-04 - 1.2 3.455922e-10 - 1.3 9.618613e-07 - 2 NA - 2.1 1.065639e-07 - 2.2 1.320730e-03 - 3 9.707820e-06 - 3.1 3.645271e-05 - 3.2 NA - 4 5.555794e-01 - 4.1 6.853316e-06 - 4.2 6.324951e-02 - 4.3 4.330745e-07 - 5 NA - 5.1 6.556812e-04 - 5.2 6.963312e-06 - 5.3 1.159006e-04 - 6 1.509745e-02 - 7 NA - 7.1 1.679086e-08 - 7.2 3.972447e-06 - 8 9.888512e-02 - 8.1 8.790334e-05 - 8.2 NA - 8.3 5.411705e-04 - 8.4 8.446731e-04 - 8.5 2.059814e-04 - 9 4.160033e-01 - 9.1 NA - 9.2 1.087331e-03 - 10 9.321715e-04 - 10.1 8.167897e-06 - 11 2.528529e-04 - 11.1 NA - 11.2 5.587553e-10 - 11.3 5.240776e-10 - 11.4 2.830994e-07 - 12 1.962202e-07 - 13 NA - 13.1 1.330415e-06 - 14 5.900181e-07 - 14.1 3.694946e-05 - 14.2 6.871447e-08 - 14.3 NA - 15 1.848068e-04 - 15.1 1.714157e-10 - 15.2 1.088807e-03 - 15.3 2.677330e-05 - 16 NA - 16.1 1.411453e-04 - 16.2 1.897147e-03 - 16.3 5.950632e-02 - 16.4 3.944608e-02 - 16.5 NA - 17 4.808238e-05 - 17.1 6.175264e-04 - 17.2 2.319036e-07 - 17.3 1.393008e-09 - 17.4 NA - 18 2.685853e-09 - 19 2.949370e-07 - 19.1 1.183423e-08 - 19.2 7.844699e-08 - 19.3 NA - 20 4.920475e-06 - 20.1 6.885500e-08 - 20.2 9.577206e-04 - 20.3 1.325632e-03 - 20.4 NA - 20.5 1.011637e-06 - 21 3.032947e-04 - 21.1 4.370975e-06 - 21.2 8.793700e-06 - 22 NA - 22.1 7.397166e-06 - 23 4.931346e-02 - 23.1 3.799306e-02 - 24 1.018950e-01 - 25 NA - 25.1 2.264756e-02 - 25.2 6.622343e-07 - 25.3 2.802504e-09 - 25.4 1.873599e-04 - 25.5 NA - 26 4.587570e-09 - 26.1 2.394334e-06 - 26.2 4.510972e-08 - 26.3 3.657318e-11 - 27 NA - 27.1 8.874134e-06 - 28 3.673907e-06 - 28.1 4.541426e-04 - 28.2 2.697966e-12 - 28.3 NA - 29 3.282475e-03 - 29.1 2.270717e-01 - 29.2 9.981536e-03 - 29.3 2.343590e-02 - 30 NA - 30.1 1.591483e-07 - 30.2 1.896944e-11 - 31 5.546285e-08 - 32 9.411981e-09 - 32.1 1.270914e-08 - 32.2 3.910478e-09 - 32.3 9.124048e-10 - 33 9.056156e-01 - 33.1 3.047254e-06 - 34 1.040462e-04 - 34.1 5.714390e-12 - 34.2 7.883166e-09 - 34.3 3.055823e-07 - 35 1.287796e-07 - 35.1 1.762232e-06 - 35.2 5.355159e-08 - 36 7.250797e-06 - 36.1 2.370652e-06 - 36.2 1.537090e-05 - 36.3 6.993214e-07 - 36.4 4.950009e-05 - 37 2.755165e-07 - 37.1 3.400517e-07 - 37.2 2.489007e-09 - 38 1.302651e-01 - 39 4.343746e-04 - 39.1 6.653143e-05 - 39.2 1.940204e-09 - 39.3 8.300468e-07 - 39.4 7.464169e-08 - 39.5 5.765597e-10 - 40 9.140572e-01 - 40.1 1.883555e-03 - 40.2 2.303001e-01 - 40.3 2.799910e-05 - 41 3.700067e-02 - 41.1 5.798225e-06 - 41.2 1.086252e-08 - 41.3 3.088732e-07 - 41.4 4.549537e-05 - 42 5.220968e-03 - 42.1 7.264286e-08 - 43 1.498125e-07 - 43.1 1.316763e-04 - 43.2 8.151771e-07 - 44 1.032476e-03 - 44.1 3.120174e-09 - 44.2 2.571257e-10 - 44.3 2.227416e-09 - 45 3.948036e-01 - 45.1 1.066310e-03 - 46 2.219556e-08 - 46.1 1.434525e-08 - 46.2 1.523026e-07 - 47 5.404537e-03 - 47.1 3.739267e-07 - 47.2 7.171916e-06 - 47.3 3.850162e-05 - 47.4 1.767264e-08 - 48 1.988010e-04 - 48.1 6.074589e-09 - 49 1.321544e-06 - 50 4.240393e-05 - 51 1.986093e-09 - 52 1.632022e-02 - 52.1 2.653038e-02 - 52.2 2.262881e-03 - 52.3 6.572647e-10 - 52.4 1.393737e-04 - 52.5 5.069462e-03 - 53 5.848890e-05 - 53.1 1.878509e-04 - 53.2 1.293417e-04 - 54 1.818441e-03 - 54.1 2.251839e-07 - 54.2 5.638172e-06 - 54.3 5.320676e-03 - 54.4 1.491367e-07 - 55 3.183775e-03 - 55.1 1.183380e-03 - 55.2 1.817077e-06 - 55.3 1.424370e-06 - 55.4 3.119967e-07 - 56 1.169667e-06 - 56.1 1.182293e-06 - 56.2 2.087533e-04 - 56.3 5.728251e-06 - 56.4 4.087596e-08 - 56.5 8.040370e-07 - 57 1.438387e-02 - 57.1 3.202179e-05 - 57.2 1.486318e-03 - 57.3 1.718412e-04 - 58 3.114123e-05 - 58.1 1.403881e-04 - 58.2 2.111006e-01 - 58.3 9.586985e-06 - 58.4 4.073162e-03 - 58.5 9.285307e-04 - 59 2.711478e-06 - 59.1 1.173472e-04 - 60 7.579680e-09 - 61 4.545759e-03 - 61.1 5.936674e-02 - 61.2 3.897281e-01 - 61.3 6.237379e-02 - 61.4 5.103038e-01 - 62 3.707353e-02 - 62.1 1.901660e-03 - 62.2 7.844369e-08 - 62.3 1.496168e-08 - 63 5.101070e-11 - 63.1 1.106013e-05 - 64 1.685171e-09 - 65 1.684142e-01 - 65.1 1.413479e-05 - 65.2 2.841196e-03 - 65.3 3.118871e-04 - 66 1.078473e-06 - 66.1 1.136650e-01 - 66.2 7.007044e-08 - 67 4.025749e-11 - 68 2.469503e-06 - 68.1 1.067638e-08 - 68.2 1.508555e-06 - 68.3 7.862972e-06 - 68.4 1.970326e-05 - 69 5.089430e-07 - 70 5.575849e-07 - 70.1 6.115107e-04 - 71 1.867742e-05 - 71.1 4.616167e-04 - 71.2 5.314611e-08 - 71.3 1.790244e-10 - 71.4 1.924070e-03 - 72 6.526547e-05 - 72.1 5.540491e-11 - 72.2 2.391191e-12 - 72.3 2.878783e-12 - 72.4 1.014404e-09 - 72.5 1.281231e-05 - 73 6.661564e-02 - 74 3.683842e-04 - 75 2.274469e-06 - 76 9.155636e-04 - 76.1 1.485365e-04 - 76.2 3.118702e-06 - 77 4.946432e-01 - 78 8.533933e-05 - 79 1.980588e-01 - 79.1 8.624235e-06 - 79.2 2.176176e-05 - 80 2.929029e-06 - 80.1 1.126162e-04 - 80.2 9.847382e-08 - 81 4.026095e-01 - 81.1 2.093927e-02 - 81.2 9.224440e-01 - 81.3 8.175654e-03 - 82 1.228129e-01 - 82.1 6.656575e-05 - 82.2 2.001426e-08 - 83 5.690020e-06 - 83.1 5.980615e-06 - 83.2 1.880816e-05 - 83.3 4.048910e-09 - 84 6.552173e-02 - 84.1 8.829278e-06 - 85 4.118253e-06 - 85.1 2.311994e-06 - 85.2 5.182892e-05 - 85.3 1.689467e-03 - 85.4 1.168017e-03 - 85.5 7.945131e-07 - 86 2.905567e-05 - 86.1 5.331467e-06 - 86.2 1.761451e-06 - 86.3 2.272397e-06 - 86.4 4.467006e-06 - 86.5 1.693940e-08 - 87 6.396865e-05 - 87.1 1.264093e-10 - 87.2 4.933807e-07 - 88 9.223531e-02 - 88.1 4.654325e-05 - 88.2 1.260399e-01 - 88.3 8.029866e-08 - 89 7.489307e-05 - 90 1.100491e-02 - 90.1 2.715349e-05 - 90.2 5.916576e-03 - 90.3 2.920657e-02 - 91 2.411997e-03 - 91.1 8.870147e-06 - 91.2 1.652965e-08 - 92 2.613551e-03 - 93 9.958480e-01 - 93.1 9.915375e-01 - 93.2 4.861680e-02 - 93.3 9.769008e-01 - 93.4 5.977439e-05 - 94 7.091952e-04 - 94.1 6.005522e-04 - 94.2 8.134430e-03 - 94.3 1.747604e-05 - 94.4 9.404259e-07 - 94.5 6.832077e-07 - 95 3.216011e-06 - 95.1 6.324477e-05 - 95.2 1.762187e-01 - 96 1.578796e-02 - 96.1 2.610661e-02 - 96.2 3.941700e-05 - 96.3 1.683671e-05 - 96.4 1.095127e-04 - 96.5 1.479105e-05 - 97 2.082560e-04 - 97.1 7.903013e-10 - 98 1.795949e-06 - 98.1 2.776600e-02 - 98.2 4.050457e-06 - 99 2.316802e-05 - 99.1 2.206426e-06 - 99.2 2.488411e-08 - 100 7.572193e-01 - 100.1 9.794641e-02 - 100.2 4.934595e-01 - 100.3 1.502083e-07 - 100.4 2.515993e-06 - - $m3f$spM_id - center scale - C2 -0.6240921 0.6857108 - (Intercept) NA NA - - $m3f$mu_reg_norm - [1] 0 - - $m3f$tau_reg_norm - [1] 1e-04 - - $m3f$shape_tau_norm - [1] 0.01 - - $m3f$rate_tau_norm - [1] 0.01 - - $m3f$mu_reg_beta - [1] 0 - - $m3f$tau_reg_beta - [1] 1e-04 - - $m3f$shape_tau_beta - [1] 0.01 - - $m3f$rate_tau_beta - [1] 0.01 - - $m3f$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m3f$shape_diag_RinvD - [1] "0.01" - - $m3f$rate_diag_RinvD - [1] "0.001" - - - $m4a - $m4a$M_id - B2 (Intercept) B21 - 1 1 1 NA - 2 NA 1 NA - 3 NA 1 NA - 4 1 1 NA - 5 1 1 NA - 6 1 1 NA - 7 0 1 NA - 8 1 1 NA - 9 1 1 NA - 10 0 1 NA - 11 1 1 NA - 12 1 1 NA - 13 1 1 NA - 14 1 1 NA - 15 NA 1 NA - 16 1 1 NA - 17 1 1 NA - 18 1 1 NA - 19 1 1 NA - 20 0 1 NA - 21 1 1 NA - 22 1 1 NA - 23 1 1 NA - 24 NA 1 NA - 25 0 1 NA - 26 1 1 NA - 27 1 1 NA - 28 0 1 NA - 29 1 1 NA - 30 0 1 NA - 31 0 1 NA - 32 1 1 NA - 33 1 1 NA - 34 0 1 NA - 35 1 1 NA - 36 0 1 NA - 37 1 1 NA - 38 1 1 NA - 39 1 1 NA - 40 1 1 NA - 41 1 1 NA - 42 1 1 NA - 43 1 1 NA - 44 NA 1 NA - 45 1 1 NA - 46 1 1 NA - 47 1 1 NA - 48 1 1 NA - 49 1 1 NA - 50 1 1 NA - 51 0 1 NA - 52 1 1 NA - 53 1 1 NA - 54 0 1 NA - 55 1 1 NA - 56 0 1 NA - 57 1 1 NA - 58 NA 1 NA - 59 1 1 NA - 60 1 1 NA - 61 0 1 NA - 62 0 1 NA - 63 1 1 NA - 64 1 1 NA - 65 1 1 NA - 66 1 1 NA - 67 1 1 NA - 68 1 1 NA - 69 NA 1 NA - 70 1 1 NA - 71 1 1 NA - 72 1 1 NA - 73 1 1 NA - 74 1 1 NA - 75 1 1 NA - 76 1 1 NA - 77 1 1 NA - 78 1 1 NA - 79 1 1 NA - 80 1 1 NA - 81 1 1 NA - 82 1 1 NA - 83 1 1 NA - 84 1 1 NA - 85 1 1 NA - 86 1 1 NA - 87 1 1 NA - 88 1 1 NA - 89 1 1 NA - 90 1 1 NA - 91 NA 1 NA - 92 1 1 NA - 93 1 1 NA - 94 1 1 NA - 95 1 1 NA - 96 NA 1 NA - 97 NA 1 NA - 98 1 1 NA - 99 1 1 NA - 100 1 1 NA - - $m4a$M_lvlone - c1 p2 c2 L1mis Be2 - 1 0.7592026489 2 NA 1.38634787 4.596628e-06 - 1.1 0.9548337990 2 -0.08061445 0.79402906 2.296427e-04 - 1.2 0.5612235156 NA -0.26523782 0.53603334 3.455922e-10 - 1.3 1.1873391025 NA -0.30260393 0.24129804 9.618613e-07 - 2 0.9192204198 NA -0.33443795 NA NA - 2.1 -0.1870730476 6 -0.11819800 0.31668065 1.065639e-07 - 2.2 1.2517512331 3 -0.31532280 0.37114414 1.320730e-03 - 3 -0.0605087604 NA -0.12920657 0.54680608 9.707820e-06 - 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38 0.8078948077 0 -0.11218486 NA 1.302651e-01 - 39 0.9876451040 NA -0.38072211 0.14395367 4.343746e-04 - 39.1 -0.3431222274 1 -0.32094428 0.36454923 6.653143e-05 - 39.2 -1.7909380751 NA NA 1.03700002 1.940204e-09 - 39.3 -0.1798746191 NA -0.40173480 0.41320585 8.300468e-07 - 39.4 -0.1850961689 NA -0.20041848 0.20901554 7.464169e-08 - 39.5 0.4544226146 NA -0.26027990 0.51603848 5.765597e-10 - 40 0.5350190436 2 -0.19751956 0.33912363 9.140572e-01 - 40.1 0.4189342752 4 -0.08399467 0.21892118 1.883555e-03 - 40.2 0.4211994981 NA -0.20864416 0.74070896 2.303001e-01 - 40.3 0.0916687506 NA NA 0.82927399 2.799910e-05 - 41 -0.1035047421 NA -0.26096953 0.25193679 3.700067e-02 - 41.1 -0.4684202411 4 -0.23953874 0.28760510 5.798225e-06 - 41.2 0.5972615368 2 -0.03079344 0.45553197 1.086252e-08 - 41.3 0.9885613862 3 NA 0.79237611 3.088732e-07 - 41.4 -0.3908036794 NA NA 0.12582175 4.549537e-05 - 42 -0.0338893961 3 -0.16084527 0.50079604 5.220968e-03 - 42.1 -0.4498363172 5 -0.13812521 0.61140760 7.264286e-08 - 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90 0.0626760573 3 -0.25029126 0.40042977 1.100491e-02 - 90.1 1.1896872985 NA -0.26974303 0.63975731 2.715349e-05 - 90.2 0.2597888783 NA -0.28804531 0.33412775 5.916576e-03 - 90.3 0.6599799887 NA -0.19180615 0.38399003 2.920657e-02 - 91 1.1213651365 NA -0.26591197 0.58250391 2.411997e-03 - 91.1 1.2046371625 NA -0.09153470 0.13223217 8.870147e-06 - 91.2 0.3395603754 NA -0.48414390 0.46613305 1.652965e-08 - 92 0.4674939332 NA NA 0.18997862 2.613551e-03 - 93 0.2677965647 2 -0.11939966 1.05243347 9.958480e-01 - 93.1 1.6424445368 4 NA 0.01479757 9.915375e-01 - 93.2 0.7101700066 4 -0.21089379 0.50955172 4.861680e-02 - 93.3 1.1222322893 NA NA 0.78122514 9.769008e-01 - 93.4 1.4628960401 3 -0.23618836 0.63940704 5.977439e-05 - 94 -0.2904211940 4 NA 0.45596305 7.091952e-04 - 94.1 0.0147813580 2 -0.10217284 0.41610667 6.005522e-04 - 94.2 -0.4536774482 NA -0.36713471 0.52744298 8.134430e-03 - 94.3 0.6793464917 1 -0.13806763 0.70890756 1.747604e-05 - 94.4 -0.9411356550 NA -0.42353804 0.84412478 9.404259e-07 - 94.5 0.5683867264 2 -0.15513707 0.21166602 6.832077e-07 - 95 0.2375652188 3 -0.24149687 0.57713135 3.216011e-06 - 95.1 0.0767152977 5 -0.21315958 0.44400207 6.324477e-05 - 95.2 -0.6886731251 2 -0.15777208 0.42397776 1.762187e-01 - 96 0.7813892121 NA -0.16780948 0.72391015 1.578796e-02 - 96.1 0.3391519695 NA -0.32504815 0.32593738 2.610661e-02 - 96.2 -0.4857246503 5 -0.20395970 0.23249511 3.941700e-05 - 96.3 0.8771471244 1 -0.06221501 1.01679990 1.683671e-05 - 96.4 1.9030768981 0 -0.14801097 0.92267953 1.095127e-04 - 96.5 -0.1684332749 3 -0.28658893 0.83843412 1.479105e-05 - 97 1.3775130083 4 -0.34484656 0.47151154 2.082560e-04 - 97.1 -1.7323228619 2 -0.35658805 0.15596614 7.903013e-10 - 98 -1.2648518889 3 -0.36913003 0.05179545 1.795949e-06 - 98.1 -0.9042716241 NA NA 0.47332096 2.776600e-02 - 98.2 -0.1560385207 NA -0.17154225 0.19706341 4.050457e-06 - 99 0.7993356425 5 -0.24753132 0.22574556 2.316802e-05 - 99.1 1.0355522332 NA -0.27947829 1.00732330 2.206426e-06 - 99.2 -0.1150895843 NA -0.09033035 0.09749127 2.488411e-08 - 100 0.0369067906 NA -0.17326698 0.22857989 7.572193e-01 - 100.1 1.6023713093 4 NA 0.39548654 9.794641e-02 - 100.2 0.8861545820 NA -0.12072016 NA 4.934595e-01 - 100.3 0.1277046316 4 -0.27657520 0.32695372 1.502083e-07 - 100.4 -0.0834577654 NA -0.14631556 0.10043925 2.515993e-06 - - $m4a$spM_lvlone - center scale - c1 0.25599956 0.6718095 - p2 2.71257485 1.6247402 - c2 -0.22371584 0.1059527 - L1mis 0.48184811 0.3462447 - Be2 0.04274145 0.1563798 - - $m4a$mu_reg_norm - [1] 0 - - $m4a$tau_reg_norm - [1] 1e-04 - - $m4a$shape_tau_norm - [1] 0.01 - - $m4a$rate_tau_norm - [1] 0.01 - - $m4a$mu_reg_gamma - [1] 0 - - $m4a$tau_reg_gamma - [1] 1e-04 - - $m4a$shape_tau_gamma - [1] 0.01 - - $m4a$rate_tau_gamma - [1] 0.01 - - $m4a$mu_reg_beta - [1] 0 - - $m4a$tau_reg_beta - [1] 1e-04 - - $m4a$shape_tau_beta - [1] 0.01 - - $m4a$rate_tau_beta - [1] 0.01 - - $m4a$mu_reg_binom - [1] 0 - - $m4a$tau_reg_binom - [1] 1e-04 - - $m4a$mu_reg_poisson - [1] 0 - - $m4a$tau_reg_poisson - [1] 1e-04 - - $m4a$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m4a$shape_diag_RinvD - [1] "0.01" - - $m4a$rate_diag_RinvD - [1] "0.001" - - - $m4b - $m4b$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m4b$M_lvlone - c1 p2 b2 c2 L1mis b21 - 1 0.7592026489 2 NA NA 1.38634787 NA - 1.1 0.9548337990 2 0 -0.08061445 0.79402906 NA - 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[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m4b$shape_diag_RinvD - [1] "0.01" - - $m4b$rate_diag_RinvD - 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[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m4c$shape_diag_RinvD - [1] "0.01" - - $m4c$rate_diag_RinvD - 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88.3 -16.3345673 -0.12308715 NA 3.2995816297 NA NA NA NA - 89 -11.0459647 -0.18527715 3 0.6462086167 NA NA NA NA - 90 -4.5610239 -0.25029126 2 0.1696030737 NA NA NA NA - 90.1 -11.7036651 -0.26974303 2 2.5980385230 NA NA NA NA - 90.2 -5.3838521 -0.28804531 2 2.6651392167 NA NA NA NA - 90.3 -4.1636999 -0.19180615 4 3.1242690247 NA NA NA NA - 91 -7.1462503 -0.26591197 2 0.6382618390 NA NA NA NA - 91.1 -12.8374475 -0.09153470 NA 2.6224059286 NA NA NA NA - 91.2 -18.2576707 -0.48414390 3 4.7772527603 NA NA NA NA - 92 -6.4119222 NA 2 0.0737052364 NA NA NA NA - 93 5.2122168 -0.11939966 3 0.2788909199 NA NA NA NA - 93.1 3.1211725 NA 2 1.0357759963 NA NA NA NA - 93.2 -3.6841177 -0.21089379 3 2.4916551099 NA NA NA NA - 93.3 2.6223542 NA 2 2.8876129608 NA NA NA NA - 93.4 -11.1877696 -0.23618836 4 4.4639474002 NA NA NA NA - 94 -6.9602492 NA NA 0.8488043118 NA NA NA NA - 94.1 -7.4318416 -0.10217284 2 1.0552454425 NA NA NA NA - 94.2 -4.3498045 -0.36713471 NA 1.9445500884 NA NA NA NA - 94.3 -11.6340088 -0.13806763 3 3.0710722448 NA NA NA NA - 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99.2 -17.3939469 -0.09033035 4 4.9927084171 NA NA NA NA - 100 1.1005874 -0.17326698 1 1.0691387602 NA NA NA NA - 100.1 -3.8226248 NA NA 1.5109344281 NA NA NA NA - 100.2 -0.9123182 -0.12072016 1 2.1502332564 NA NA NA NA - 100.3 -15.8389474 -0.27657520 4 3.8745574222 NA NA NA NA - 100.4 -12.8093826 -0.14631556 1 4.6567608765 NA NA NA NA - I(time^2) o22:abs(C1 - c2) o23:abs(C1 - c2) o24:abs(C1 - c2) - 1 2.591239e-01 NA NA NA - 1.1 4.443657e-01 NA NA NA - 1.2 4.539005e+00 NA NA NA - 1.3 6.227241e+00 NA NA NA - 2 9.099267e+00 NA NA NA - 2.1 1.088789e+01 NA NA NA - 2.2 1.742860e+01 NA NA NA - 3 7.188883e-01 NA NA NA - 3.1 9.396866e+00 NA NA NA - 3.2 2.245012e+01 NA NA NA - 4 1.136655e-01 NA NA NA - 4.1 1.143407e+00 NA NA NA - 4.2 6.837688e+00 NA NA NA - 4.3 9.819783e+00 NA NA NA - 5 1.158319e+00 NA NA NA - 5.1 3.208593e+00 NA NA NA - 5.2 7.817661e+00 NA NA NA - 5.3 7.907311e+00 NA NA NA - 6 3.173907e+00 NA NA NA - 7 1.093895e+01 NA NA NA - 7.1 1.369622e+01 NA NA NA - 7.2 2.276883e+01 NA NA NA - 8 1.264815e+00 NA NA NA - 8.1 3.249731e+00 NA NA NA - 8.2 3.303606e+00 NA NA NA - 8.3 8.056666e+00 NA NA NA - 8.4 1.130995e+01 NA NA NA - 8.5 1.967885e+01 NA NA NA - 9 9.230989e-01 NA NA NA - 9.1 8.513413e+00 NA NA NA - 9.2 2.313696e+01 NA NA NA - 10 5.278740e+00 NA NA NA - 10.1 1.741737e+01 NA NA NA - 11 1.400119e+00 NA NA NA - 11.1 1.524250e+00 NA NA NA - 11.2 2.701196e+00 NA NA NA - 11.3 1.146433e+01 NA NA NA - 11.4 2.315350e+01 NA NA NA - 12 9.200622e-01 NA NA NA - 13 3.832672e-03 NA NA NA - 13.1 1.268860e+01 NA NA NA - 14 1.629287e+01 NA NA NA - 14.1 1.999034e+01 NA NA NA - 14.2 2.149175e+01 NA NA NA - 14.3 2.198311e+01 NA NA NA - 15 2.918229e-01 NA NA NA - 15.1 1.414477e+00 NA NA NA - 15.2 2.278512e+00 NA NA NA - 15.3 2.419998e+01 NA NA NA - 16 1.542046e+00 NA NA NA - 16.1 6.592429e+00 NA NA NA - 16.2 7.035280e+00 NA NA NA - 16.3 1.266294e+01 NA NA NA - 16.4 1.414697e+01 NA NA NA - 16.5 1.588151e+01 NA NA NA - 17 2.536170e+00 NA NA NA - 17.1 5.940935e+00 NA NA NA - 17.2 9.154551e+00 NA NA NA - 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28.3 1.998584e+01 NA NA NA - 29 1.415834e+00 NA NA NA - 29.1 3.106075e+00 NA NA NA - 29.2 4.084605e+00 NA NA NA - 29.3 1.161363e+01 NA NA NA - 30 5.123598e+00 NA NA NA - 30.1 1.291564e+01 NA NA NA - 30.2 1.306006e+01 NA NA NA - 31 1.934957e+01 NA NA NA - 32 2.804020e+00 NA NA NA - 32.1 8.484502e+00 NA NA NA - 32.2 8.806981e+00 NA NA NA - 32.3 1.772399e+01 NA NA NA - 33 8.720901e-05 NA NA NA - 33.1 1.196554e+01 NA NA NA - 34 2.249632e+00 NA NA NA - 34.1 1.462509e+01 NA NA NA - 34.2 1.526641e+01 NA NA NA - 34.3 1.566745e+01 NA NA NA - 35 1.767384e+00 NA NA NA - 35.1 2.333857e+00 NA NA NA - 35.2 2.027334e+01 NA NA NA - 36 5.073953e-01 NA NA NA - 36.1 3.230105e+00 NA NA NA - 36.2 3.335261e+00 NA NA NA - 36.3 1.835276e+01 NA NA NA - 36.4 2.133929e+01 NA NA NA - 37 4.007496e+00 NA NA NA - 37.1 1.343724e+01 NA NA NA - 37.2 1.573228e+01 NA NA NA - 38 9.656032e-01 NA NA NA - 39 4.791143e-01 NA NA NA - 39.1 8.150103e-01 NA NA NA - 39.2 1.704501e+00 NA NA NA - 39.3 2.375557e+00 NA NA NA - 39.4 1.013467e+01 NA NA NA - 39.5 1.713477e+01 NA NA NA - 40 1.283779e+00 NA NA NA - 40.1 7.258172e+00 NA NA NA - 40.2 9.239542e+00 NA NA NA - 40.3 2.186776e+01 NA NA NA - 41 3.739257e+00 NA NA NA - 41.1 1.021205e+01 NA NA NA - 41.2 1.078920e+01 NA NA NA - 41.3 1.143355e+01 NA NA NA - 41.4 1.259041e+01 NA NA NA - 42 2.361234e-01 NA NA NA - 42.1 1.874295e+01 NA NA NA - 43 3.154636e-01 NA NA NA - 43.1 1.154245e+00 NA NA NA - 43.2 6.828709e+00 NA NA NA - 44 5.871613e-01 NA NA NA - 44.1 7.017356e+00 NA NA NA - 44.2 1.113684e+01 NA NA NA - 44.3 1.693668e+01 NA NA NA - 45 3.831610e-02 NA NA NA - 45.1 3.985546e+00 NA NA NA - 46 1.816499e+00 NA NA NA - 46.1 8.160046e+00 NA NA NA - 46.2 1.950170e+01 NA NA NA - 47 3.615162e-01 NA NA NA - 47.1 5.806713e+00 NA NA NA - 47.2 8.985482e+00 NA NA NA - 47.3 1.013127e+01 NA NA NA - 47.4 2.134538e+01 NA NA NA - 48 8.183814e+00 NA NA NA - 48.1 8.467142e+00 NA NA NA - 49 7.387392e+00 NA NA NA - 50 1.383461e+00 NA NA NA - 51 2.046169e+00 NA NA NA - 52 4.522702e+00 NA NA NA - 52.1 9.610339e+00 NA NA NA - 52.2 9.777177e+00 NA NA NA - 52.3 1.275308e+01 NA NA NA - 52.4 2.302432e+01 NA NA NA - 52.5 2.481859e+01 NA NA NA - 53 2.465916e-01 NA NA NA - 53.1 1.260630e+01 NA NA NA - 53.2 2.096763e+01 NA NA NA - 54 1.969735e+00 NA NA NA - 54.1 3.539056e+00 NA NA NA - 54.2 6.303910e+00 NA NA NA - 54.3 7.755338e+00 NA NA NA - 54.4 1.611531e+01 NA NA NA - 55 3.743632e-01 NA NA NA - 55.1 5.570753e-01 NA NA NA - 55.2 7.953081e+00 NA NA NA - 55.3 9.813453e+00 NA NA NA - 55.4 1.038006e+01 NA NA NA - 56 1.496055e+00 NA NA NA - 56.1 5.556959e+00 NA NA NA - 56.2 6.592024e+00 NA NA NA - 56.3 8.706727e+00 NA NA NA - 56.4 1.041529e+01 NA NA NA - 56.5 1.167966e+01 NA NA NA - 57 5.618471e-02 NA NA NA - 57.1 6.157569e-02 NA NA NA - 57.2 1.300874e+00 NA NA NA - 57.3 4.474869e+00 NA NA NA - 58 1.490865e+00 NA NA NA - 58.1 2.668076e+00 NA NA NA - 58.2 2.819667e+00 NA NA NA - 58.3 6.927488e+00 NA NA NA - 58.4 8.109812e+00 NA NA NA - 58.5 1.275602e+01 NA NA NA - 59 3.619125e+00 NA NA NA - 59.1 2.473731e+01 NA NA NA - 60 8.325824e+00 NA NA NA - 61 5.203647e-01 NA NA NA - 61.1 5.376345e+00 NA NA NA - 61.2 6.288716e+00 NA NA NA - 61.3 1.006861e+01 NA NA NA - 61.4 1.297637e+01 NA NA NA - 62 2.848114e-01 NA NA NA - 62.1 4.882748e-01 NA NA NA - 62.2 1.196074e+01 NA NA NA - 62.3 2.306763e+01 NA NA NA - 63 7.894611e+00 NA NA NA - 63.1 1.572420e+01 NA NA NA - 64 1.696724e+01 NA NA NA - 65 5.007194e-01 NA NA NA - 65.1 4.101535e+00 NA NA NA - 65.2 9.689140e+00 NA NA NA - 65.3 1.022023e+01 NA NA NA - 66 1.221045e+01 NA NA NA - 66.1 1.419589e+01 NA NA NA - 66.2 1.559155e+01 NA NA NA - 67 1.741258e+01 NA NA NA - 68 1.669057e-02 NA NA NA - 68.1 3.171834e+00 NA NA NA - 68.2 4.199715e+00 NA NA NA - 68.3 8.647640e+00 NA NA NA - 68.4 1.632699e+01 NA NA NA - 69 1.718202e+01 NA NA NA - 70 3.970284e-02 NA NA NA - 70.1 2.332672e-01 NA NA NA - 71 5.993245e-01 NA NA NA - 71.1 2.215280e+00 NA NA NA - 71.2 1.661257e+01 NA NA NA - 71.3 2.213537e+01 NA NA NA - 71.4 2.231881e+01 NA NA NA - 72 8.688470e-01 NA NA NA - 72.1 1.392398e+00 NA NA NA - 72.2 3.578744e+00 NA NA NA - 72.3 1.214773e+01 NA NA NA - 72.4 1.360449e+01 NA NA NA - 72.5 1.669062e+01 NA NA NA - 73 2.117830e+01 NA NA NA - 74 2.139435e+00 NA NA NA - 75 1.057829e+01 NA NA NA - 76 3.266987e+00 NA NA NA - 76.1 1.822015e+01 NA NA NA - 76.2 2.468970e+01 NA NA NA - 77 7.155525e-01 NA NA NA - 78 6.775091e-01 NA NA NA - 79 3.408452e-03 NA NA NA - 79.1 5.956710e+00 NA NA NA - 79.2 1.086528e+01 NA NA NA - 80 8.073131e-01 NA NA NA - 80.1 1.804904e+00 NA NA NA - 80.2 7.854511e+00 NA NA NA - 81 1.026675e-04 NA NA NA - 81.1 8.876861e-01 NA NA NA - 81.2 9.328415e+00 NA NA NA - 81.3 1.119346e+01 NA NA NA - 82 1.901920e+00 NA NA NA - 82.1 3.097956e+00 NA NA NA - 82.2 6.881772e+00 NA NA NA - 83 2.887926e-03 NA NA NA - 83.1 8.445749e+00 NA NA NA - 83.2 9.727823e+00 NA NA NA - 83.3 2.271823e+01 NA NA NA - 84 7.427838e+00 NA NA NA - 84.1 1.113209e+01 NA NA NA - 85 8.867032e-02 NA NA NA - 85.1 3.025553e+00 NA NA NA - 85.2 7.207254e+00 NA NA NA - 85.3 9.991139e+00 NA NA NA - 85.4 1.556465e+01 NA NA NA - 85.5 2.033338e+01 NA NA NA - 86 7.184729e-01 NA NA NA - 86.1 1.023867e+00 NA NA NA - 86.2 1.565291e+00 NA NA NA - 86.3 4.783212e+00 NA NA NA - 86.4 6.018649e+00 NA NA NA - 86.5 1.459703e+01 NA NA NA - 87 7.327595e+00 NA NA NA - 87.1 1.187665e+01 NA NA NA - 87.2 2.046808e+01 NA NA NA - 88 3.472088e-07 NA NA NA - 88.1 5.063888e-01 NA NA NA - 88.2 6.226384e+00 NA NA NA - 88.3 1.088724e+01 NA NA NA - 89 4.175856e-01 NA NA NA - 90 2.876520e-02 NA NA NA - 90.1 6.749804e+00 NA NA NA - 90.2 7.102967e+00 NA NA NA - 90.3 9.761057e+00 NA NA NA - 91 4.073782e-01 NA NA NA - 91.1 6.877013e+00 NA NA NA - 91.2 2.282214e+01 NA NA NA - 92 5.432462e-03 NA NA NA - 93 7.778015e-02 NA NA NA - 93.1 1.072832e+00 NA NA NA - 93.2 6.208345e+00 NA NA NA - 93.3 8.338309e+00 NA NA NA - 93.4 1.992683e+01 NA NA NA - 94 7.204688e-01 NA NA NA - 94.1 1.113543e+00 NA NA NA - 94.2 3.781275e+00 NA NA NA - 94.3 9.431485e+00 NA NA NA - 94.4 9.531256e+00 NA NA NA - 94.5 1.918945e+01 NA NA NA - 95 4.080021e+00 NA NA NA - 95.1 1.614790e+01 NA NA NA - 95.2 2.079044e+01 NA NA NA - 96 9.692853e-04 NA NA NA - 96.1 1.753685e-02 NA NA NA - 96.2 4.490747e-01 NA NA NA - 96.3 4.741523e+00 NA NA NA - 96.4 4.948909e+00 NA NA NA - 96.5 1.795865e+01 NA NA NA - 97 1.429245e+00 NA NA NA - 97.1 2.460468e+01 NA NA NA - 98 4.168671e-02 NA NA NA - 98.1 1.857247e-01 NA NA NA - 98.2 1.237113e+01 NA NA NA - 99 1.247351e-01 NA NA NA - 99.1 2.189252e+01 NA NA NA - 99.2 2.492714e+01 NA NA NA - 100 1.143058e+00 NA NA NA - 100.1 2.282923e+00 NA NA NA - 100.2 4.623503e+00 NA NA NA - 100.3 1.501220e+01 NA NA NA - 100.4 2.168542e+01 NA NA NA - - $m5a$spM_id - center scale - M2 NA NA - (Intercept) NA NA - M22 NA NA - M23 NA NA - M24 NA NA - log(C1) -0.3049822 0.01990873 - C1 0.7372814 0.01472882 - - $m5a$spM_lvlone - center scale - y -11.1733710 6.2496619 - c2 -0.2237158 0.1059527 - o2 NA NA - time 2.5339403 1.3818094 - o22 NA NA - o23 NA NA - o24 NA NA - abs(C1 - c2) 0.9613865 0.1064886 - I(time^2) 8.3244468 7.0900029 - o22:abs(C1 - c2) 0.2166402 0.4111132 - o23:abs(C1 - c2) 0.2721613 0.4294402 - o24:abs(C1 - c2) 0.2492394 0.4265852 - - $m5a$mu_reg_norm - [1] 0 - - $m5a$tau_reg_norm - [1] 1e-04 - - $m5a$shape_tau_norm - [1] 0.01 - - $m5a$rate_tau_norm - [1] 0.01 - - $m5a$mu_reg_multinomial - [1] 0 - - $m5a$tau_reg_multinomial - [1] 1e-04 - - $m5a$mu_reg_ordinal - [1] 0 - - $m5a$tau_reg_ordinal - [1] 1e-04 - - $m5a$mu_delta_ordinal - [1] 0 - - $m5a$tau_delta_ordinal - [1] 1e-04 - - $m5a$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m5a$shape_diag_RinvD - [1] "0.01" - - $m5a$rate_diag_RinvD - [1] "0.001" - - $m5a$RinvD_y_id - [,1] [,2] - [1,] NA 0 - [2,] 0 NA - - $m5a$KinvD_y_id - id - 3 - - - $m5b - $m5b$M_id - C2 (Intercept) - 1 -1.381594459 1 - 2 0.344426024 1 - 3 NA 1 - 4 -0.228910007 1 - 5 NA 1 - 6 -2.143955482 1 - 7 -1.156567023 1 - 8 -0.598827660 1 - 9 NA 1 - 10 -1.006719032 1 - 11 0.239801450 1 - 12 -1.064969789 1 - 13 -0.538082688 1 - 14 NA 1 - 15 -1.781049276 1 - 16 NA 1 - 17 NA 1 - 18 -0.014579883 1 - 19 -2.121550136 1 - 20 NA 1 - 21 -0.363239698 1 - 22 -0.121568514 1 - 23 -0.951271111 1 - 24 NA 1 - 25 -0.974288621 1 - 26 -1.130632418 1 - 27 0.114339868 1 - 28 0.238334648 1 - 29 0.840744958 1 - 30 NA 1 - 31 NA 1 - 32 -1.466312154 1 - 33 -0.637352277 1 - 34 NA 1 - 35 NA 1 - 36 NA 1 - 37 NA 1 - 38 NA 1 - 39 0.006728205 1 - 40 NA 1 - 41 -1.663281353 1 - 42 0.161184794 1 - 43 0.457939180 1 - 44 -0.307070331 1 - 45 NA 1 - 46 -1.071668276 1 - 47 -0.814751321 1 - 48 -0.547630662 1 - 49 NA 1 - 50 -1.350213782 1 - 51 0.719054706 1 - 52 NA 1 - 53 -1.207130750 1 - 54 NA 1 - 55 -0.408600991 1 - 56 -0.271380529 1 - 57 -1.361925974 1 - 58 NA 1 - 59 NA 1 - 60 -0.323712205 1 - 61 NA 1 - 62 NA 1 - 63 -1.386906880 1 - 64 NA 1 - 65 NA 1 - 66 -0.565191691 1 - 67 -0.382899912 1 - 68 NA 1 - 69 -0.405642769 1 - 70 NA 1 - 71 -0.843748427 1 - 72 0.116003683 1 - 73 -0.778634325 1 - 74 NA 1 - 75 NA 1 - 76 NA 1 - 77 -0.632974758 1 - 78 NA 1 - 79 -0.778064615 1 - 80 NA 1 - 81 NA 1 - 82 -0.246123253 1 - 83 -1.239659782 1 - 84 -0.467772280 1 - 85 NA 1 - 86 -2.160485036 1 - 87 -0.657675572 1 - 88 NA 1 - 89 -0.696710744 1 - 90 NA 1 - 91 -0.179395847 1 - 92 -0.441545568 1 - 93 -0.685799334 1 - 94 NA 1 - 95 0.191929445 1 - 96 NA 1 - 97 -0.069760671 1 - 98 NA 1 - 99 NA 1 - 100 NA 1 - - $m5b$M_lvlone - b1 L1mis Be2 c1 time abs(c1 - C2) - 1 0 1.38634787 4.596628e-06 0.7592026489 0.5090421822 NA - 1.1 1 0.79402906 2.296427e-04 0.9548337990 0.6666076288 NA - 1.2 1 0.53603334 3.455922e-10 0.5612235156 2.1304941282 NA - 1.3 0 0.24129804 9.618613e-07 1.1873391025 2.4954441458 NA - 2 1 NA NA 0.9192204198 3.0164990982 NA - 2.1 1 0.31668065 1.065639e-07 -0.1870730476 3.2996806887 NA - 2.2 1 0.37114414 1.320730e-03 1.2517512331 4.1747569619 NA - 3 1 0.54680608 9.707820e-06 -0.0605087604 0.8478727890 NA - 3.1 0 0.28280274 3.645271e-05 0.3788637747 3.0654308549 NA - 3.2 0 0.76277262 NA 0.9872578281 4.7381553578 NA - 4 1 0.56100366 5.555794e-01 1.4930175328 0.3371432109 NA - 4.1 1 0.38514140 6.853316e-06 -0.7692526880 1.0693019140 NA - 4.2 0 0.04026174 6.324951e-02 0.9180841450 2.6148973033 NA - 4.3 1 0.16025873 4.330745e-07 -0.0541170782 3.1336532847 NA - 5 0 0.21080161 NA -0.1376784521 1.0762525082 NA - 5.1 1 0.36665700 6.556812e-04 -0.2740585866 1.7912546196 NA - 5.2 1 0.66368829 6.963312e-06 0.4670496929 2.7960080339 NA - 5.3 1 0.40788895 1.159006e-04 0.1740288049 2.8119940578 NA - 6 0 0.11889539 1.509745e-02 0.9868044683 1.7815462884 NA - 7 1 1.04286843 NA -0.1280320918 3.3074087673 NA - 7.1 0 0.52098933 1.679086e-08 0.4242971219 3.7008403614 NA - 7.2 1 0.09858876 3.972447e-06 0.0777182491 4.7716691741 NA - 8 0 0.17281472 9.888512e-02 -0.5791408712 1.1246398522 NA - 8.1 1 0.25970093 8.790334e-05 0.3128604232 1.8027009873 NA - 8.2 1 0.30550233 NA 0.6258446356 1.8175825174 NA - 8.3 0 0.88029778 5.411705e-04 -0.1040137707 2.8384267003 NA - 8.4 0 0.20200392 8.446731e-04 0.0481450285 3.3630275307 NA - 8.5 1 NA 2.059814e-04 0.3831763675 4.4360849704 NA - 9 1 1.12218535 4.160033e-01 -0.1757592269 0.9607803822 NA - 9.1 1 0.57911079 NA -0.1791541200 2.9177753383 NA - 9.2 0 0.81350994 1.087331e-03 -0.0957042935 4.8100892501 NA - 10 1 0.32744766 9.321715e-04 -0.5598409704 2.2975509102 NA - 10.1 1 0.62912282 8.167897e-06 -0.2318340451 4.1734118364 NA - 11 1 0.92140073 2.528529e-04 0.5086859475 1.1832662905 NA - 11.1 1 0.16012129 NA 0.4951758188 1.2346051680 NA - 11.2 1 0.16166775 5.587553e-10 -1.1022162541 1.6435316263 NA - 11.3 1 0.14979756 5.240776e-10 -0.0611636705 3.3859017969 NA - 11.4 1 0.46855190 2.830994e-07 -0.4971774316 4.8118087661 NA - 12 1 0.76818678 1.962202e-07 -0.2433996286 0.9591987054 NA - 13 0 0.34264972 NA 0.8799673116 0.0619085738 NA - 13.1 1 0.14526619 1.330415e-06 0.1079022586 3.5621061502 NA - 14 0 0.80630788 5.900181e-07 0.9991752617 4.0364430007 NA - 14.1 1 0.35697552 3.694946e-05 -0.1094019046 4.4710561272 NA - 14.2 0 0.21330192 6.871447e-08 0.1518967560 4.6359198843 NA - 14.3 0 NA NA 0.3521012473 4.6886152599 NA - 15 0 0.30769119 1.848068e-04 0.3464447888 0.5402063532 NA - 15.1 0 0.28349746 1.714157e-10 -0.4767313971 1.1893180816 NA - 15.2 0 0.64618365 1.088807e-03 0.5759767791 1.5094739688 NA - 15.3 1 0.51680884 2.677330e-05 -0.1713452662 4.9193474615 NA - 16 1 0.71265471 NA 0.4564754473 1.2417913869 NA - 16.1 0 0.38925880 1.411453e-04 1.0652558311 2.5675726333 NA - 16.2 1 0.23648869 1.897147e-03 0.6971872493 2.6524101500 NA - 16.3 1 0.45048730 5.950632e-02 0.5259331838 3.5585018690 NA - 16.4 1 0.23181791 3.944608e-02 0.2046601798 3.7612454291 NA - 16.5 0 0.13985349 NA 1.0718540464 3.9851612889 NA - 17 0 0.25995399 4.808238e-05 0.6048676222 1.5925356350 NA - 17.1 0 0.03594878 6.175264e-04 0.2323298304 2.4374032998 NA - 17.2 1 0.77583623 2.319036e-07 1.2617499032 3.0256489082 NA - 17.3 0 0.60015197 1.393008e-09 -0.3913230895 3.3329089405 NA - 17.4 1 0.13998405 NA 0.9577299112 3.8693758985 NA - 18 1 0.96475839 2.685853e-09 -0.0050324072 2.4374292302 NA - 19 1 0.10596495 2.949370e-07 -0.4187468937 0.9772165376 NA - 19.1 1 0.13338947 1.183423e-08 -0.4478828944 1.1466335913 NA - 19.2 1 0.41662218 7.844699e-08 -1.1966721302 2.2599126538 NA - 19.3 1 0.53670855 NA -0.5877091668 4.2114245973 NA - 20 0 0.41688567 4.920475e-06 0.6838223064 1.7170160066 NA - 20.1 1 NA 6.885500e-08 0.3278571109 1.7562902288 NA - 20.2 0 0.81634101 9.577206e-04 -0.8489831990 2.2515566566 NA - 20.3 0 0.39232496 1.325632e-03 1.3169975191 2.2609123867 NA - 20.4 0 0.57925554 NA 0.0444804531 3.4913365287 NA - 20.5 0 0.74200986 1.011637e-06 -0.4535207652 4.1730977828 NA - 21 1 0.24759801 3.032947e-04 -0.4030302960 1.6936582839 NA - 21.1 1 0.34052205 4.370975e-06 -0.4069674045 2.9571191233 NA - 21.2 0 0.03905058 8.793700e-06 1.0650265940 3.7887385779 NA - 22 0 0.48605351 NA -0.0673274516 2.4696226232 NA - 22.1 1 0.43761071 7.397166e-06 0.9601388170 3.1626627257 NA - 23 1 0.47599712 4.931346e-02 0.5556634840 1.5414533857 NA - 23.1 1 0.47680301 3.799306e-02 1.4407865964 2.3369736120 NA - 24 0 0.51696505 1.018950e-01 0.3856376411 2.8283136466 NA - 25 0 0.59392591 NA 0.3564400705 0.5381704110 NA - 25.1 1 0.74010330 2.264756e-02 0.0982553434 1.6069735331 NA - 25.2 1 NA 6.622343e-07 0.1928682598 1.6358226922 NA - 25.3 0 0.73081722 2.802504e-09 -0.0192488594 3.2646870392 NA - 25.4 0 0.29274286 1.873599e-04 0.4466012931 4.0782226040 NA - 25.5 0 0.74425342 NA 1.1425193342 4.1560292873 NA - 26 1 0.20974346 4.587570e-09 0.5341531449 0.2412706357 NA - 26.1 1 NA 2.394334e-06 1.2268695927 2.4451737676 NA - 26.2 1 0.22908815 4.510972e-08 0.3678294939 3.5988757887 NA - 26.3 0 0.41880799 3.657318e-11 0.5948516018 4.1822362854 NA - 27 1 0.10097167 NA -0.3342844147 3.6955824879 NA - 27.1 1 NA 8.874134e-06 -0.4835141229 4.2451434687 NA - 28 1 NA 3.673907e-06 -0.7145915499 0.5746519344 NA - 28.1 0 0.56052750 4.541426e-04 0.5063671955 2.7943964268 NA - 28.2 1 0.15301800 2.697966e-12 -0.2067413142 4.2108539480 NA - 28.3 1 0.27802542 NA 0.1196789973 4.4705521734 NA - 29 1 0.43556671 3.282475e-03 0.1392699487 1.1898884235 NA - 29.1 0 0.27593085 2.270717e-01 0.7960234776 1.7624059319 NA - 29.2 0 0.55256871 9.981536e-03 1.0398214352 2.0210406382 NA - 29.3 1 0.47272109 2.343590e-02 0.0813246429 3.4078777023 NA - 30 1 0.32743933 NA -0.3296323050 2.2635366488 NA - 30.1 1 0.02231535 1.591483e-07 1.3635850954 3.5938334477 NA - 30.2 1 0.12833697 1.896944e-11 0.7354171050 3.6138710892 NA - 31 0 0.11126366 5.546285e-08 0.3708398217 4.3988140998 NA - 32 1 1.11731084 9.411981e-09 -0.0474059668 1.6745209007 NA - 32.1 1 0.85943330 1.270914e-08 1.2507771489 2.9128167813 NA - 32.2 1 1.53730925 3.910478e-09 0.1142915519 2.9676558380 NA - 32.3 1 0.43831965 9.124048e-10 0.6773270619 4.2099863547 NA - 33 0 0.46726055 9.056156e-01 0.1774293842 0.0093385763 NA - 33.1 0 0.76818259 3.047254e-06 0.6159606291 3.4591242753 NA - 34 1 NA 1.040462e-04 0.8590979166 1.4998774312 NA - 34.1 0 1.14350292 5.714390e-12 0.0546216775 3.8242761395 NA - 34.2 1 0.19103604 7.883166e-09 -0.0897224473 3.9072251692 NA - 34.3 1 NA 3.055823e-07 0.4163395571 3.9582124643 NA - 35 1 0.66303137 1.287796e-07 -1.4693520528 1.3294299203 NA - 35.1 0 NA 1.762232e-06 -0.3031734330 1.5276966314 NA - 35.2 1 NA 5.355159e-08 -0.6045512101 4.5025920868 NA - 36 0 0.93843318 7.250797e-06 0.9823048960 0.7123168337 NA - 36.1 0 NA 2.370652e-06 1.4466051416 1.7972493160 NA - 36.2 1 0.29886676 1.537090e-05 1.1606752905 1.8262697803 NA - 36.3 0 0.22616598 6.993214e-07 0.8373091576 4.2840119381 NA - 36.4 1 0.53849566 4.950009e-05 0.2640591685 4.6194464504 NA - 37 1 1.68107300 2.755165e-07 0.1177313455 2.0018732361 NA - 37.1 0 1.13777638 3.400517e-07 -0.1415483779 3.6656836793 NA - 37.2 0 0.26931933 2.489007e-09 0.0054610124 3.9663937816 NA - 38 1 NA 1.302651e-01 0.8078948077 0.9826511063 NA - 39 1 0.14395367 4.343746e-04 0.9876451040 0.6921808305 NA - 39.1 0 0.36454923 6.653143e-05 -0.3431222274 0.9027792048 NA - 39.2 0 1.03700002 1.940204e-09 -1.7909380751 1.3055654289 NA - 39.3 0 0.41320585 8.300468e-07 -0.1798746191 1.5412842878 NA - 39.4 1 0.20901554 7.464169e-08 -0.1850961689 3.1834997435 NA - 39.5 1 0.51603848 5.765597e-10 0.4544226146 4.1394166439 NA - 40 0 0.33912363 9.140572e-01 0.5350190436 1.1330395646 NA - 40.1 0 0.21892118 1.883555e-03 0.4189342752 2.6940994046 NA - 40.2 0 0.74070896 2.303001e-01 0.4211994981 3.0396614212 NA - 40.3 1 0.82927399 2.799910e-05 0.0916687506 4.6762977762 NA - 41 1 0.25193679 3.700067e-02 -0.1035047421 1.9337158254 NA - 41.1 1 0.28760510 5.798225e-06 -0.4684202411 3.1956304458 NA - 41.2 0 0.45553197 1.086252e-08 0.5972615368 3.2846923557 NA - 41.3 1 0.79237611 3.088732e-07 0.9885613862 3.3813529415 NA - 41.4 1 0.12582175 4.549537e-05 -0.3908036794 3.5482964432 NA - 42 1 0.50079604 5.220968e-03 -0.0338893961 0.4859252973 NA - 42.1 1 0.61140760 7.264286e-08 -0.4498363172 4.3293134298 NA - 43 0 0.29752019 1.498125e-07 0.8965546110 0.5616614548 NA - 43.1 0 0.51793497 1.316763e-04 0.6199122090 1.0743579536 NA - 43.2 1 0.15152473 8.151771e-07 0.1804894429 2.6131797966 NA - 44 1 0.38806434 1.032476e-03 1.3221409285 0.7662644819 NA - 44.1 0 1.11140786 3.120174e-09 0.3416426284 2.6490291790 NA - 44.2 0 0.39132534 2.571257e-10 0.5706610068 3.3371910988 NA - 44.3 1 0.40934909 2.227416e-09 1.2679497430 4.1154200875 NA - 45 1 0.68587067 3.948036e-01 0.1414983160 0.1957449992 NA - 45.1 0 0.34530800 1.066310e-03 0.7220892521 1.9963831536 NA - 46 1 0.71312288 2.219556e-08 1.5391054233 1.3477755385 NA - 46.1 0 0.62537420 1.434525e-08 0.3889107049 2.8565793915 NA - 46.2 1 0.79574391 1.523026e-07 0.1248719493 4.4160729996 NA - 47 0 0.48660773 5.404537e-03 0.2014101100 0.6012621359 NA - 47.1 0 0.51241790 3.739267e-07 0.2982973539 2.4097121472 NA - 47.2 1 0.58869379 7.171916e-06 1.1518107179 2.9975794035 NA - 47.3 0 0.22171504 3.850162e-05 0.5196802157 3.1829649757 NA - 47.4 0 0.11366347 1.767264e-08 0.3702301552 4.6201055450 NA - 48 0 0.19677010 1.988010e-04 -0.2128602862 2.8607365978 NA - 48.1 1 0.17706320 6.074589e-09 -0.5337239976 2.9098354396 NA - 49 0 0.30752382 1.321544e-06 -0.5236770035 2.7179756400 NA - 50 1 0.93663423 4.240393e-05 0.3897705981 1.1762060679 NA - 51 1 0.34107606 1.986093e-09 -0.7213343736 1.4304436720 NA - 52 1 0.19007135 1.632022e-02 0.3758235358 2.1266646020 NA - 52.1 1 0.75662940 2.653038e-02 0.7138067080 3.1000545993 NA - 52.2 0 1.66104719 2.262881e-03 0.8872895233 3.1268477370 NA - 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76.1 NA 1.822015e+01 - 76.2 NA 2.468970e+01 - 77 NA 7.155525e-01 - 78 NA 6.775091e-01 - 79 NA 3.408452e-03 - 79.1 NA 5.956710e+00 - 79.2 NA 1.086528e+01 - 80 NA 8.073131e-01 - 80.1 NA 1.804904e+00 - 80.2 NA 7.854511e+00 - 81 NA 1.026675e-04 - 81.1 NA 8.876861e-01 - 81.2 NA 9.328415e+00 - 81.3 NA 1.119346e+01 - 82 NA 1.901920e+00 - 82.1 NA 3.097956e+00 - 82.2 NA 6.881772e+00 - 83 NA 2.887926e-03 - 83.1 NA 8.445749e+00 - 83.2 NA 9.727823e+00 - 83.3 NA 2.271823e+01 - 84 NA 7.427838e+00 - 84.1 NA 1.113209e+01 - 85 NA 8.867032e-02 - 85.1 NA 3.025553e+00 - 85.2 NA 7.207254e+00 - 85.3 NA 9.991139e+00 - 85.4 NA 1.556465e+01 - 85.5 NA 2.033338e+01 - 86 NA 7.184729e-01 - 86.1 NA 1.023867e+00 - 86.2 NA 1.565291e+00 - 86.3 NA 4.783212e+00 - 86.4 NA 6.018649e+00 - 86.5 NA 1.459703e+01 - 87 NA 7.327595e+00 - 87.1 NA 1.187665e+01 - 87.2 NA 2.046808e+01 - 88 NA 3.472088e-07 - 88.1 NA 5.063888e-01 - 88.2 NA 6.226384e+00 - 88.3 NA 1.088724e+01 - 89 NA 4.175856e-01 - 90 NA 2.876520e-02 - 90.1 NA 6.749804e+00 - 90.2 NA 7.102967e+00 - 90.3 NA 9.761057e+00 - 91 NA 4.073782e-01 - 91.1 NA 6.877013e+00 - 91.2 NA 2.282214e+01 - 92 NA 5.432462e-03 - 93 NA 7.778015e-02 - 93.1 NA 1.072832e+00 - 93.2 NA 6.208345e+00 - 93.3 NA 8.338309e+00 - 93.4 NA 1.992683e+01 - 94 NA 7.204688e-01 - 94.1 NA 1.113543e+00 - 94.2 NA 3.781275e+00 - 94.3 NA 9.431485e+00 - 94.4 NA 9.531256e+00 - 94.5 NA 1.918945e+01 - 95 NA 4.080021e+00 - 95.1 NA 1.614790e+01 - 95.2 NA 2.079044e+01 - 96 NA 9.692853e-04 - 96.1 NA 1.753685e-02 - 96.2 NA 4.490747e-01 - 96.3 NA 4.741523e+00 - 96.4 NA 4.948909e+00 - 96.5 NA 1.795865e+01 - 97 NA 1.429245e+00 - 97.1 NA 2.460468e+01 - 98 NA 4.168671e-02 - 98.1 NA 1.857247e-01 - 98.2 NA 1.237113e+01 - 99 NA 1.247351e-01 - 99.1 NA 2.189252e+01 - 99.2 NA 2.492714e+01 - 100 NA 1.143058e+00 - 100.1 NA 2.282923e+00 - 100.2 NA 4.623503e+00 - 100.3 NA 1.501220e+01 - 100.4 NA 2.168542e+01 - - $m5b$spM_id - center scale - C2 -0.6240921 0.6857108 - (Intercept) NA NA - - $m5b$spM_lvlone - center scale - b1 NA NA - L1mis 0.48184811 0.3462447 - Be2 0.04274145 0.1563798 - c1 0.25599956 0.6718095 - time 2.53394028 1.3818094 - abs(c1 - C2) 1.12675664 0.7813693 - log(Be2) -11.02063958 6.0744935 - I(time^2) 8.32444679 7.0900029 - - $m5b$mu_reg_norm - [1] 0 - - $m5b$tau_reg_norm - [1] 1e-04 - - $m5b$shape_tau_norm - [1] 0.01 - - $m5b$rate_tau_norm - [1] 0.01 - - $m5b$mu_reg_gamma - [1] 0 - - $m5b$tau_reg_gamma - [1] 1e-04 - - $m5b$shape_tau_gamma - [1] 0.01 - - $m5b$rate_tau_gamma - [1] 0.01 - - $m5b$mu_reg_beta - [1] 0 - - $m5b$tau_reg_beta - [1] 1e-04 - - $m5b$shape_tau_beta - [1] 0.01 - - $m5b$rate_tau_beta - [1] 0.01 - - $m5b$mu_reg_binom - [1] 0 - - $m5b$tau_reg_binom - [1] 1e-04 - - $m5b$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m5b$shape_diag_RinvD - [1] "0.01" - - $m5b$rate_diag_RinvD - [1] "0.001" - - $m5b$RinvD_b1_id - [,1] [,2] [,3] - [1,] NA 0 0 - [2,] 0 NA 0 - [3,] 0 0 NA - - $m5b$KinvD_b1_id - id - 4 - - - $m6a - $m6a$M_id - C2 (Intercept) C1 - 1 -1.381594459 1 0.7175865 - 2 0.344426024 1 0.7507170 - 3 NA 1 0.7255954 - 4 -0.228910007 1 0.7469352 - 5 NA 1 0.7139120 - 6 -2.143955482 1 0.7332505 - 7 -1.156567023 1 0.7345929 - 8 -0.598827660 1 0.7652589 - 9 NA 1 0.7200622 - 10 -1.006719032 1 0.7423879 - 11 0.239801450 1 0.7437448 - 12 -1.064969789 1 0.7446470 - 13 -0.538082688 1 0.7530186 - 14 NA 1 0.7093137 - 15 -1.781049276 1 0.7331192 - 16 NA 1 0.7011390 - 17 NA 1 0.7432395 - 18 -0.014579883 1 0.7545191 - 19 -2.121550136 1 0.7528487 - 20 NA 1 0.7612865 - 21 -0.363239698 1 0.7251719 - 22 -0.121568514 1 0.7300630 - 23 -0.951271111 1 0.7087249 - 24 NA 1 0.7391938 - 25 -0.974288621 1 0.7820641 - 26 -1.130632418 1 0.7118298 - 27 0.114339868 1 0.7230857 - 28 0.238334648 1 0.7489353 - 29 0.840744958 1 0.7510888 - 30 NA 1 0.7300717 - 31 NA 1 0.7550721 - 32 -1.466312154 1 0.7321898 - 33 -0.637352277 1 0.7306414 - 34 NA 1 0.7427216 - 35 NA 1 0.7193042 - 36 NA 1 0.7312888 - 37 NA 1 0.7100436 - 38 NA 1 0.7670184 - 39 0.006728205 1 0.7400449 - 40 NA 1 0.7397304 - 41 -1.663281353 1 0.7490966 - 42 0.161184794 1 0.7419274 - 43 0.457939180 1 0.7527810 - 44 -0.307070331 1 0.7408315 - 45 NA 1 0.7347550 - 46 -1.071668276 1 0.7332398 - 47 -0.814751321 1 0.7376481 - 48 -0.547630662 1 0.7346179 - 49 NA 1 0.7329402 - 50 -1.350213782 1 0.7260436 - 51 0.719054706 1 0.7242910 - 52 NA 1 0.7298067 - 53 -1.207130750 1 0.7254741 - 54 NA 1 0.7542067 - 55 -0.408600991 1 0.7389952 - 56 -0.271380529 1 0.7520638 - 57 -1.361925974 1 0.7219958 - 58 NA 1 0.7259632 - 59 NA 1 0.7458606 - 60 -0.323712205 1 0.7672421 - 61 NA 1 0.7257179 - 62 NA 1 0.7189892 - 63 -1.386906880 1 0.7333356 - 64 NA 1 0.7320243 - 65 NA 1 0.7477711 - 66 -0.565191691 1 0.7343974 - 67 -0.382899912 1 0.7491624 - 68 NA 1 0.7482736 - 69 -0.405642769 1 0.7338267 - 70 NA 1 0.7607742 - 71 -0.843748427 1 0.7777600 - 72 0.116003683 1 0.7408143 - 73 -0.778634325 1 0.7248271 - 74 NA 1 0.7364916 - 75 NA 1 0.7464926 - 76 NA 1 0.7355430 - 77 -0.632974758 1 0.7208449 - 78 NA 1 0.7373573 - 79 -0.778064615 1 0.7598079 - 80 NA 1 0.7360415 - 81 NA 1 0.7293932 - 82 -0.246123253 1 0.7279309 - 83 -1.239659782 1 0.7344643 - 84 -0.467772280 1 0.7384350 - 85 NA 1 0.7323716 - 86 -2.160485036 1 0.7576597 - 87 -0.657675572 1 0.7496139 - 88 NA 1 0.7275239 - 89 -0.696710744 1 0.7250648 - 90 NA 1 0.7335262 - 91 -0.179395847 1 0.7343980 - 92 -0.441545568 1 0.7380425 - 93 -0.685799334 1 0.7389460 - 94 NA 1 0.7259951 - 95 0.191929445 1 0.7282840 - 96 NA 1 0.7281676 - 97 -0.069760671 1 0.7245642 - 98 NA 1 0.7526938 - 99 NA 1 0.7272309 - 100 NA 1 0.7383460 - - $m6a$M_lvlone - y b2 b21 time - 1 -13.0493856 NA NA 0.5090421822 - 1.1 -9.3335901 0 NA 0.6666076288 - 1.2 -22.3469852 NA NA 2.1304941282 - 1.3 -15.0417337 0 NA 2.4954441458 - 2 -12.0655434 0 NA 3.0164990982 - 2.1 -15.8674476 NA NA 3.2996806887 - 2.2 -7.8800006 NA NA 4.1747569619 - 3 -11.4820604 0 NA 0.8478727890 - 3.1 -10.5983220 NA NA 3.0654308549 - 3.2 -22.4519157 1 NA 4.7381553578 - 4 -1.2697775 1 NA 0.3371432109 - 4.1 -11.1215184 0 NA 1.0693019140 - 4.2 -3.6134138 0 NA 2.6148973033 - 4.3 -14.5982385 0 NA 3.1336532847 - 5 -6.8457515 NA NA 1.0762525082 - 5.1 -7.0551214 0 NA 1.7912546196 - 5.2 -12.3418980 NA NA 2.7960080339 - 5.3 -9.2366906 NA NA 2.8119940578 - 6 -5.1648211 NA NA 1.7815462884 - 7 -10.0599502 NA NA 3.3074087673 - 7.1 -18.3267285 NA NA 3.7008403614 - 7.2 -12.5138426 0 NA 4.7716691741 - 8 -1.6305331 0 NA 1.1246398522 - 8.1 -9.6520453 0 NA 1.8027009873 - 8.2 -1.5278462 NA NA 1.8175825174 - 8.3 -7.4172211 1 NA 2.8384267003 - 8.4 -7.1238609 0 NA 3.3630275307 - 8.5 -8.8706950 1 NA 4.4360849704 - 9 -0.1634429 0 NA 0.9607803822 - 9.1 -2.6034300 NA NA 2.9177753383 - 9.2 -6.7272369 NA NA 4.8100892501 - 10 -6.4172202 NA NA 2.2975509102 - 10.1 -11.4834569 0 NA 4.1734118364 - 11 -8.7911356 0 NA 1.1832662905 - 11.1 -19.6645080 0 NA 1.2346051680 - 11.2 -20.2030932 0 NA 1.6435316263 - 11.3 -21.3082176 0 NA 3.3859017969 - 11.4 -14.5802901 0 NA 4.8118087661 - 12 -15.2006287 0 NA 0.9591987054 - 13 0.8058816 NA NA 0.0619085738 - 13.1 -13.6379208 0 NA 3.5621061502 - 14 -15.3422873 NA NA 4.0364430007 - 14.1 -10.0965208 NA NA 4.4710561272 - 14.2 -16.6452027 NA NA 4.6359198843 - 14.3 -15.8389733 NA NA 4.6886152599 - 15 -8.9424594 0 NA 0.5402063532 - 15.1 -22.0101983 0 NA 1.1893180816 - 15.2 -7.3975599 0 NA 1.5094739688 - 15.3 -10.3567334 0 NA 4.9193474615 - 16 -1.9691302 1 NA 1.2417913869 - 16.1 -9.9308357 NA NA 2.5675726333 - 16.2 -6.9626923 NA NA 2.6524101500 - 16.3 -3.2862557 0 NA 3.5585018690 - 16.4 -3.3972355 0 NA 3.7612454291 - 16.5 -11.5767835 NA NA 3.9851612889 - 17 -10.5474144 0 NA 1.5925356350 - 17.1 -7.6215009 0 NA 2.4374032998 - 17.2 -16.5386939 0 NA 3.0256489082 - 17.3 -20.0004774 NA NA 3.3329089405 - 17.4 -18.8505475 0 NA 3.8693758985 - 18 -19.7302351 0 NA 2.4374292302 - 19 -14.6177568 NA NA 0.9772165376 - 19.1 -17.8043866 NA NA 1.1466335913 - 19.2 -15.1641705 0 NA 2.2599126538 - 19.3 -16.6898418 1 NA 4.2114245973 - 20 -12.9059229 NA NA 1.7170160066 - 20.1 -16.8191201 0 NA 1.7562902288 - 20.2 -6.1010131 1 NA 2.2515566566 - 20.3 -7.9415371 0 NA 2.2609123867 - 20.4 -9.3904458 0 NA 3.4913365287 - 20.5 -13.3504189 0 NA 4.1730977828 - 21 -7.6974718 0 NA 1.6936582839 - 21.1 -11.9335526 0 NA 2.9571191233 - 21.2 -12.7064929 NA NA 3.7887385779 - 22 -21.5022909 0 NA 2.4696226232 - 22.1 -12.7745451 0 NA 3.1626627257 - 23 -3.5146508 0 NA 1.5414533857 - 23.1 -4.6724048 NA NA 2.3369736120 - 24 -2.5619821 0 NA 2.8283136466 - 25 -6.2944970 0 NA 0.5381704110 - 25.1 -3.8630505 NA NA 1.6069735331 - 25.2 -14.4205140 1 NA 1.6358226922 - 25.3 -19.6735037 0 NA 3.2646870392 - 25.4 -9.0288933 0 NA 4.0782226040 - 25.5 -9.0509738 NA NA 4.1560292873 - 26 -19.7340685 NA NA 0.2412706357 - 26.1 -14.1692728 0 NA 2.4451737676 - 26.2 -17.2819976 0 NA 3.5988757887 - 26.3 -24.6265576 0 NA 4.1822362854 - 27 -7.3354999 0 NA 3.6955824879 - 27.1 -11.1488468 0 NA 4.2451434687 - 28 -11.7996597 NA NA 0.5746519344 - 28.1 -8.2030122 0 NA 2.7943964268 - 28.2 -26.4317815 0 NA 4.2108539480 - 28.3 -18.5016071 0 NA 4.4705521734 - 29 -5.8551395 0 NA 1.1898884235 - 29.1 -2.0209442 0 NA 1.7624059319 - 29.2 -5.6368080 0 NA 2.0210406382 - 29.3 -3.8110961 0 NA 3.4078777023 - 30 -12.7217702 NA NA 2.2635366488 - 30.1 -17.0170140 0 NA 3.5938334477 - 30.2 -25.4236089 0 NA 3.6138710892 - 31 -17.0783921 0 NA 4.3988140998 - 32 -18.4338764 0 NA 1.6745209007 - 32.1 -19.4317212 0 NA 2.9128167813 - 32.2 -19.4738978 NA NA 2.9676558380 - 32.3 -21.4922645 NA NA 4.2099863547 - 33 2.0838099 0 NA 0.0093385763 - 33.1 -13.3172274 1 NA 3.4591242753 - 34 -10.0296691 NA NA 1.4998774312 - 34.1 -25.9426553 0 NA 3.8242761395 - 34.2 -18.5688138 NA NA 3.9072251692 - 34.3 -15.4173859 NA NA 3.9582124643 - 35 -14.3958113 0 NA 1.3294299203 - 35.1 -12.9457541 0 NA 1.5276966314 - 35.2 -16.1380691 NA NA 4.5025920868 - 36 -12.8166968 NA NA 0.7123168337 - 36.1 -14.3989481 NA NA 1.7972493160 - 36.2 -12.2436943 0 NA 1.8262697803 - 36.3 -15.0104638 0 NA 4.2840119381 - 36.4 -10.1775457 0 NA 4.6194464504 - 37 -15.2223495 0 NA 2.0018732361 - 37.1 -14.7526195 0 NA 3.6656836793 - 37.2 -19.8168430 0 NA 3.9663937816 - 38 -2.7065118 0 NA 0.9826511063 - 39 -8.7288138 1 NA 0.6921808305 - 39.1 -9.2746473 0 NA 0.9027792048 - 39.2 -18.2695344 NA NA 1.3055654289 - 39.3 -13.8219083 NA NA 1.5412842878 - 39.4 -16.2254704 0 NA 3.1834997435 - 39.5 -21.7283648 1 NA 4.1394166439 - 40 1.8291916 0 NA 1.1330395646 - 40.1 -6.6916432 1 NA 2.6940994046 - 40.2 -1.6278171 0 NA 3.0396614212 - 40.3 -10.5749790 NA NA 4.6762977762 - 41 -3.1556121 0 NA 1.9337158254 - 41.1 -11.5895327 NA NA 3.1956304458 - 41.2 -18.9352091 0 NA 3.2846923557 - 41.3 -15.9788960 NA NA 3.3813529415 - 41.4 -9.6070508 0 NA 3.5482964432 - 42 -5.2159485 0 NA 0.4859252973 - 42.1 -15.9878743 1 NA 4.3293134298 - 43 -16.6104361 0 NA 0.5616614548 - 43.1 -9.5549441 1 NA 1.0743579536 - 43.2 -14.2003491 0 NA 2.6131797966 - 44 -8.1969033 0 NA 0.7662644819 - 44.1 -19.9270197 0 NA 2.6490291790 - 44.2 -22.6521171 0 NA 3.3371910988 - 44.3 -21.1903736 0 NA 4.1154200875 - 45 -0.5686627 NA NA 0.1957449992 - 45.1 -7.5645740 1 NA 1.9963831536 - 46 -19.1624789 0 NA 1.3477755385 - 46.1 -18.4487574 0 NA 2.8565793915 - 46.2 -15.8222682 0 NA 4.4160729996 - 47 -5.4165074 0 NA 0.6012621359 - 47.1 -15.0975029 0 NA 2.4097121472 - 47.2 -12.9971413 0 NA 2.9975794035 - 47.3 -10.6844521 NA NA 3.1829649757 - 47.4 -18.2214784 0 NA 4.6201055450 - 48 -8.3101471 1 NA 2.8607365978 - 48.1 -18.3854275 1 NA 2.9098354396 - 49 -13.0130319 NA NA 2.7179756400 - 50 -10.4579977 0 NA 1.1762060679 - 51 -19.3157621 0 NA 1.4304436720 - 52 -4.4747188 0 NA 2.1266646020 - 52.1 -4.3163827 0 NA 3.1000545993 - 52.2 -6.9761408 0 NA 3.1268477370 - 52.3 -20.1764756 0 NA 3.5711459327 - 52.4 -8.9036692 0 NA 4.7983659909 - 52.5 -5.6949642 0 NA 4.9818264414 - 53 -10.3141887 0 NA 0.4965799209 - 53.1 -8.2642654 0 NA 3.5505357443 - 53.2 -9.1691554 NA NA 4.5790420019 - 54 -6.2198754 NA NA 1.4034724841 - 54.1 -15.7192609 NA NA 1.8812377600 - 54.2 -13.0978998 NA NA 2.5107589352 - 54.3 -5.1195299 NA NA 2.7848406672 - 54.4 -16.5771751 0 NA 4.0143877396 - 55 -5.7348534 0 NA 0.6118522980 - 55.1 -7.3217494 0 NA 0.7463747414 - 55.2 -12.2171938 NA NA 2.8201208171 - 55.3 -12.9821266 NA NA 3.1326431572 - 55.4 -14.8599983 0 NA 3.2218102901 - 56 -14.1764282 0 NA 1.2231332215 - 56.1 -12.5343602 NA NA 2.3573202139 - 56.2 -8.4573382 NA NA 2.5674936292 - 56.3 -12.4633969 1 NA 2.9507164378 - 56.4 -17.3841863 0 NA 3.2272730360 - 56.5 -14.8147645 0 NA 3.4175522043 - 57 -3.1403293 0 NA 0.2370331448 - 57.1 -11.1509248 0 NA 0.2481445030 - 57.2 -6.3940143 0 NA 1.1405586067 - 57.3 -9.3473241 NA NA 2.1153886721 - 58 -12.0245677 0 NA 1.2210099772 - 58.1 -9.2112246 NA NA 1.6334245703 - 58.2 -1.2071742 1 NA 1.6791862890 - 58.3 -11.0141711 1 NA 2.6320121693 - 58.4 -5.3721214 0 NA 2.8477731440 - 58.5 -7.8523047 0 NA 3.5715569824 - 59 -13.2946560 NA NA 1.9023998594 - 59.1 -10.0530648 1 NA 4.9736620474 - 60 -19.2209402 0 NA 2.8854503250 - 61 -4.6699914 NA NA 0.7213630795 - 61.1 -3.5981894 1 NA 2.3186947661 - 61.2 -1.4713611 1 NA 2.5077313243 - 61.3 -3.8819786 0 NA 3.1731073430 - 61.4 0.1041413 0 NA 3.6022726283 - 62 -2.8591600 NA NA 0.5336771999 - 62.1 -6.9461986 1 NA 0.6987666548 - 62.2 -16.7910593 0 NA 3.4584309917 - 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72.3 -27.2843801 0 NA 3.4853593935 - 72.4 -20.8541617 0 NA 3.6884259700 - 72.5 -12.8948965 0 NA 4.0854155901 - 73 -2.6091307 0 NA 4.6019889915 - 74 -8.2790175 0 NA 1.4626806753 - 75 -12.5029612 NA NA 3.2524286874 - 76 -6.0061671 0 NA 1.8074807397 - 76.1 -8.8149114 0 NA 4.2685073183 - 76.2 -11.8359043 0 NA 4.9688734859 - 77 0.4772521 NA NA 0.8459033852 - 78 -9.4105229 0 NA 0.8231094317 - 79 -1.0217265 NA NA 0.0583819521 - 79.1 -11.8125257 0 NA 2.4406372628 - 79.2 -10.5465186 NA NA 3.2962526032 - 80 -12.7366807 NA NA 0.8985060186 - 80.1 -9.0584783 0 NA 1.3434670598 - 80.2 -16.6381566 NA NA 2.8025900386 - 81 0.5547913 0 NA 0.0101324962 - 81.1 -4.0892715 0 NA 0.9421709494 - 81.2 1.8283303 NA NA 3.0542453879 - 81.3 -5.2166381 0 NA 3.3456630446 - 82 -3.0749381 NA NA 1.3791010005 - 82.1 -10.5506696 0 NA 1.7601010622 - 82.2 -18.2226347 1 NA 2.6233131927 - 83 -12.5872635 NA NA 0.0537394290 - 83.1 -11.9756502 0 NA 2.9061570496 - 83.2 -10.6744217 0 NA 3.1189457362 - 83.3 -19.2714012 NA NA 4.7663642222 - 84 -2.6320312 0 NA 2.7254060237 - 84.1 -9.8140094 NA NA 3.3364784659 - 85 -12.3886736 1 NA 0.2977756259 - 85.1 -12.9196365 NA NA 1.7394116637 - 85.2 -9.6433248 0 NA 2.6846330194 - 85.3 -6.3296340 0 NA 3.1608762743 - 85.4 -7.0405525 0 NA 3.9452053758 - 85.5 -13.6714939 0 NA 4.5092553482 - 86 -10.8756412 0 NA 0.8476278360 - 86.1 -12.0055331 NA NA 1.0118629411 - 86.2 -13.3724699 NA NA 1.2511159515 - 86.3 -13.3252145 0 NA 2.1870554925 - 86.4 -14.9191290 NA NA 2.4532935000 - 86.5 -17.7515546 0 NA 3.8206058508 - 87 -10.7027963 NA NA 2.7069531474 - 87.1 -22.4941954 NA NA 3.4462517721 - 87.2 -14.9616716 NA NA 4.5241666853 - 88 -2.2264493 0 NA 0.0005892443 - 88.1 -8.9626474 NA NA 0.7116099866 - 88.2 -2.5095281 0 NA 2.4952722900 - 88.3 -16.3345673 0 NA 3.2995816297 - 89 -11.0459647 0 NA 0.6462086167 - 90 -4.5610239 0 NA 0.1696030737 - 90.1 -11.7036651 0 NA 2.5980385230 - 90.2 -5.3838521 0 NA 2.6651392167 - 90.3 -4.1636999 NA NA 3.1242690247 - 91 -7.1462503 0 NA 0.6382618390 - 91.1 -12.8374475 0 NA 2.6224059286 - 91.2 -18.2576707 0 NA 4.7772527603 - 92 -6.4119222 0 NA 0.0737052364 - 93 5.2122168 NA NA 0.2788909199 - 93.1 3.1211725 0 NA 1.0357759963 - 93.2 -3.6841177 NA NA 2.4916551099 - 93.3 2.6223542 0 NA 2.8876129608 - 93.4 -11.1877696 0 NA 4.4639474002 - 94 -6.9602492 NA NA 0.8488043118 - 94.1 -7.4318416 0 NA 1.0552454425 - 94.2 -4.3498045 0 NA 1.9445500884 - 94.3 -11.6340088 NA NA 3.0710722448 - 94.4 -12.9357964 0 NA 3.0872731935 - 94.5 -14.7648530 1 NA 4.3805759016 - 95 -12.8849309 0 NA 2.0199063048 - 95.1 -9.7451502 NA NA 4.0184444457 - 95.2 -0.8535063 0 NA 4.5596531732 - 96 -4.9139832 0 NA 0.0311333477 - 96.1 -3.9582653 0 NA 0.1324267720 - 96.2 -9.6555492 0 NA 0.6701303425 - 96.3 -11.8690793 NA NA 2.1775037691 - 96.4 -11.0224373 1 NA 2.2246142488 - 96.5 -10.9530403 1 NA 4.2377650598 - 97 -9.8540471 0 NA 1.1955102731 - 97.1 -19.2262840 0 NA 4.9603108643 - 98 -11.9651231 0 NA 0.2041732438 - 98.1 -2.6515128 0 NA 0.4309578973 - 98.2 -12.2606382 1 NA 3.5172611906 - 99 -11.4720500 0 NA 0.3531786101 - 99.1 -14.0596866 0 NA 4.6789444226 - 99.2 -17.3939469 0 NA 4.9927084171 - 100 1.1005874 NA NA 1.0691387602 - 100.1 -3.8226248 NA NA 1.5109344281 - 100.2 -0.9123182 0 NA 2.1502332564 - 100.3 -15.8389474 NA NA 3.8745574222 - 100.4 -12.8093826 0 NA 4.6567608765 - - $m6a$spM_id - center scale - C2 -0.6240921 0.68571078 - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m6a$spM_lvlone - center scale - y -11.17337 6.249662 - b2 NA NA - b21 NA NA - time 2.53394 1.381809 - - $m6a$mu_reg_norm - [1] 0 - - $m6a$tau_reg_norm - [1] 1e-04 - - $m6a$shape_tau_norm - [1] 0.01 - - $m6a$rate_tau_norm - [1] 0.01 - - $m6a$mu_reg_binom - [1] 0 - - $m6a$tau_reg_binom - [1] 1e-04 - - $m6a$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m6a$shape_diag_RinvD - [1] "0.01" - - $m6a$rate_diag_RinvD - [1] "0.001" - - - $m6b - $m6b$M_id - C2 (Intercept) B11 - 1 -1.381594459 1 1 - 2 0.344426024 1 1 - 3 NA 1 1 - 4 -0.228910007 1 0 - 5 NA 1 1 - 6 -2.143955482 1 1 - 7 -1.156567023 1 1 - 8 -0.598827660 1 0 - 9 NA 1 1 - 10 -1.006719032 1 1 - 11 0.239801450 1 0 - 12 -1.064969789 1 1 - 13 -0.538082688 1 1 - 14 NA 1 1 - 15 -1.781049276 1 1 - 16 NA 1 1 - 17 NA 1 1 - 18 -0.014579883 1 1 - 19 -2.121550136 1 1 - 20 NA 1 0 - 21 -0.363239698 1 1 - 22 -0.121568514 1 1 - 23 -0.951271111 1 1 - 24 NA 1 0 - 25 -0.974288621 1 1 - 26 -1.130632418 1 1 - 27 0.114339868 1 0 - 28 0.238334648 1 1 - 29 0.840744958 1 1 - 30 NA 1 1 - 31 NA 1 1 - 32 -1.466312154 1 1 - 33 -0.637352277 1 1 - 34 NA 1 1 - 35 NA 1 1 - 36 NA 1 0 - 37 NA 1 0 - 38 NA 1 1 - 39 0.006728205 1 1 - 40 NA 1 1 - 41 -1.663281353 1 1 - 42 0.161184794 1 1 - 43 0.457939180 1 1 - 44 -0.307070331 1 1 - 45 NA 1 0 - 46 -1.071668276 1 1 - 47 -0.814751321 1 0 - 48 -0.547630662 1 0 - 49 NA 1 1 - 50 -1.350213782 1 1 - 51 0.719054706 1 1 - 52 NA 1 0 - 53 -1.207130750 1 1 - 54 NA 1 1 - 55 -0.408600991 1 1 - 56 -0.271380529 1 1 - 57 -1.361925974 1 1 - 58 NA 1 1 - 59 NA 1 1 - 60 -0.323712205 1 1 - 61 NA 1 0 - 62 NA 1 1 - 63 -1.386906880 1 1 - 64 NA 1 1 - 65 NA 1 1 - 66 -0.565191691 1 0 - 67 -0.382899912 1 0 - 68 NA 1 1 - 69 -0.405642769 1 1 - 70 NA 1 1 - 71 -0.843748427 1 1 - 72 0.116003683 1 1 - 73 -0.778634325 1 1 - 74 NA 1 0 - 75 NA 1 1 - 76 NA 1 1 - 77 -0.632974758 1 1 - 78 NA 1 1 - 79 -0.778064615 1 1 - 80 NA 1 1 - 81 NA 1 1 - 82 -0.246123253 1 1 - 83 -1.239659782 1 0 - 84 -0.467772280 1 0 - 85 NA 1 1 - 86 -2.160485036 1 1 - 87 -0.657675572 1 1 - 88 NA 1 1 - 89 -0.696710744 1 1 - 90 NA 1 0 - 91 -0.179395847 1 1 - 92 -0.441545568 1 1 - 93 -0.685799334 1 0 - 94 NA 1 1 - 95 0.191929445 1 0 - 96 NA 1 0 - 97 -0.069760671 1 1 - 98 NA 1 1 - 99 NA 1 1 - 100 NA 1 1 - - $m6b$M_lvlone - b1 c1 time I(time^2) - 1 0 0.7592026489 0.5090421822 2.591239e-01 - 1.1 1 0.9548337990 0.6666076288 4.443657e-01 - 1.2 1 0.5612235156 2.1304941282 4.539005e+00 - 1.3 0 1.1873391025 2.4954441458 6.227241e+00 - 2 1 0.9192204198 3.0164990982 9.099267e+00 - 2.1 1 -0.1870730476 3.2996806887 1.088789e+01 - 2.2 1 1.2517512331 4.1747569619 1.742860e+01 - 3 1 -0.0605087604 0.8478727890 7.188883e-01 - 3.1 0 0.3788637747 3.0654308549 9.396866e+00 - 3.2 0 0.9872578281 4.7381553578 2.245012e+01 - 4 1 1.4930175328 0.3371432109 1.136655e-01 - 4.1 1 -0.7692526880 1.0693019140 1.143407e+00 - 4.2 0 0.9180841450 2.6148973033 6.837688e+00 - 4.3 1 -0.0541170782 3.1336532847 9.819783e+00 - 5 0 -0.1376784521 1.0762525082 1.158319e+00 - 5.1 1 -0.2740585866 1.7912546196 3.208593e+00 - 5.2 1 0.4670496929 2.7960080339 7.817661e+00 - 5.3 1 0.1740288049 2.8119940578 7.907311e+00 - 6 0 0.9868044683 1.7815462884 3.173907e+00 - 7 1 -0.1280320918 3.3074087673 1.093895e+01 - 7.1 0 0.4242971219 3.7008403614 1.369622e+01 - 7.2 1 0.0777182491 4.7716691741 2.276883e+01 - 8 0 -0.5791408712 1.1246398522 1.264815e+00 - 8.1 1 0.3128604232 1.8027009873 3.249731e+00 - 8.2 1 0.6258446356 1.8175825174 3.303606e+00 - 8.3 0 -0.1040137707 2.8384267003 8.056666e+00 - 8.4 0 0.0481450285 3.3630275307 1.130995e+01 - 8.5 1 0.3831763675 4.4360849704 1.967885e+01 - 9 1 -0.1757592269 0.9607803822 9.230989e-01 - 9.1 1 -0.1791541200 2.9177753383 8.513413e+00 - 9.2 0 -0.0957042935 4.8100892501 2.313696e+01 - 10 1 -0.5598409704 2.2975509102 5.278740e+00 - 10.1 1 -0.2318340451 4.1734118364 1.741737e+01 - 11 1 0.5086859475 1.1832662905 1.400119e+00 - 11.1 1 0.4951758188 1.2346051680 1.524250e+00 - 11.2 1 -1.1022162541 1.6435316263 2.701196e+00 - 11.3 1 -0.0611636705 3.3859017969 1.146433e+01 - 11.4 1 -0.4971774316 4.8118087661 2.315350e+01 - 12 1 -0.2433996286 0.9591987054 9.200622e-01 - 13 0 0.8799673116 0.0619085738 3.832672e-03 - 13.1 1 0.1079022586 3.5621061502 1.268860e+01 - 14 0 0.9991752617 4.0364430007 1.629287e+01 - 14.1 1 -0.1094019046 4.4710561272 1.999034e+01 - 14.2 0 0.1518967560 4.6359198843 2.149175e+01 - 14.3 0 0.3521012473 4.6886152599 2.198311e+01 - 15 0 0.3464447888 0.5402063532 2.918229e-01 - 15.1 0 -0.4767313971 1.1893180816 1.414477e+00 - 15.2 0 0.5759767791 1.5094739688 2.278512e+00 - 15.3 1 -0.1713452662 4.9193474615 2.419998e+01 - 16 1 0.4564754473 1.2417913869 1.542046e+00 - 16.1 0 1.0652558311 2.5675726333 6.592429e+00 - 16.2 1 0.6971872493 2.6524101500 7.035280e+00 - 16.3 1 0.5259331838 3.5585018690 1.266294e+01 - 16.4 1 0.2046601798 3.7612454291 1.414697e+01 - 16.5 0 1.0718540464 3.9851612889 1.588151e+01 - 17 0 0.6048676222 1.5925356350 2.536170e+00 - 17.1 0 0.2323298304 2.4374032998 5.940935e+00 - 17.2 1 1.2617499032 3.0256489082 9.154551e+00 - 17.3 0 -0.3913230895 3.3329089405 1.110828e+01 - 17.4 1 0.9577299112 3.8693758985 1.497207e+01 - 18 1 -0.0050324072 2.4374292302 5.941061e+00 - 19 1 -0.4187468937 0.9772165376 9.549522e-01 - 19.1 1 -0.4478828944 1.1466335913 1.314769e+00 - 19.2 1 -1.1966721302 2.2599126538 5.107205e+00 - 19.3 1 -0.5877091668 4.2114245973 1.773610e+01 - 20 0 0.6838223064 1.7170160066 2.948144e+00 - 20.1 1 0.3278571109 1.7562902288 3.084555e+00 - 20.2 0 -0.8489831990 2.2515566566 5.069507e+00 - 20.3 0 1.3169975191 2.2609123867 5.111725e+00 - 20.4 0 0.0444804531 3.4913365287 1.218943e+01 - 20.5 0 -0.4535207652 4.1730977828 1.741475e+01 - 21 1 -0.4030302960 1.6936582839 2.868478e+00 - 21.1 1 -0.4069674045 2.9571191233 8.744554e+00 - 21.2 0 1.0650265940 3.7887385779 1.435454e+01 - 22 0 -0.0673274516 2.4696226232 6.099036e+00 - 22.1 1 0.9601388170 3.1626627257 1.000244e+01 - 23 1 0.5556634840 1.5414533857 2.376079e+00 - 23.1 1 1.4407865964 2.3369736120 5.461446e+00 - 24 0 0.3856376411 2.8283136466 7.999358e+00 - 25 0 0.3564400705 0.5381704110 2.896274e-01 - 25.1 1 0.0982553434 1.6069735331 2.582364e+00 - 25.2 1 0.1928682598 1.6358226922 2.675916e+00 - 25.3 0 -0.0192488594 3.2646870392 1.065818e+01 - 25.4 0 0.4466012931 4.0782226040 1.663190e+01 - 25.5 0 1.1425193342 4.1560292873 1.727258e+01 - 26 1 0.5341531449 0.2412706357 5.821152e-02 - 26.1 1 1.2268695927 2.4451737676 5.978875e+00 - 26.2 1 0.3678294939 3.5988757887 1.295191e+01 - 26.3 0 0.5948516018 4.1822362854 1.749110e+01 - 27 1 -0.3342844147 3.6955824879 1.365733e+01 - 27.1 1 -0.4835141229 4.2451434687 1.802124e+01 - 28 1 -0.7145915499 0.5746519344 3.302248e-01 - 28.1 0 0.5063671955 2.7943964268 7.808651e+00 - 28.2 1 -0.2067413142 4.2108539480 1.773129e+01 - 28.3 1 0.1196789973 4.4705521734 1.998584e+01 - 29 1 0.1392699487 1.1898884235 1.415834e+00 - 29.1 0 0.7960234776 1.7624059319 3.106075e+00 - 29.2 0 1.0398214352 2.0210406382 4.084605e+00 - 29.3 1 0.0813246429 3.4078777023 1.161363e+01 - 30 1 -0.3296323050 2.2635366488 5.123598e+00 - 30.1 1 1.3635850954 3.5938334477 1.291564e+01 - 30.2 1 0.7354171050 3.6138710892 1.306006e+01 - 31 0 0.3708398217 4.3988140998 1.934957e+01 - 32 1 -0.0474059668 1.6745209007 2.804020e+00 - 32.1 1 1.2507771489 2.9128167813 8.484502e+00 - 32.2 1 0.1142915519 2.9676558380 8.806981e+00 - 32.3 1 0.6773270619 4.2099863547 1.772399e+01 - 33 0 0.1774293842 0.0093385763 8.720901e-05 - 33.1 0 0.6159606291 3.4591242753 1.196554e+01 - 34 1 0.8590979166 1.4998774312 2.249632e+00 - 34.1 0 0.0546216775 3.8242761395 1.462509e+01 - 34.2 1 -0.0897224473 3.9072251692 1.526641e+01 - 34.3 1 0.4163395571 3.9582124643 1.566745e+01 - 35 1 -1.4693520528 1.3294299203 1.767384e+00 - 35.1 0 -0.3031734330 1.5276966314 2.333857e+00 - 35.2 1 -0.6045512101 4.5025920868 2.027334e+01 - 36 0 0.9823048960 0.7123168337 5.073953e-01 - 36.1 0 1.4466051416 1.7972493160 3.230105e+00 - 36.2 1 1.1606752905 1.8262697803 3.335261e+00 - 36.3 0 0.8373091576 4.2840119381 1.835276e+01 - 36.4 1 0.2640591685 4.6194464504 2.133929e+01 - 37 1 0.1177313455 2.0018732361 4.007496e+00 - 37.1 0 -0.1415483779 3.6656836793 1.343724e+01 - 37.2 0 0.0054610124 3.9663937816 1.573228e+01 - 38 1 0.8078948077 0.9826511063 9.656032e-01 - 39 1 0.9876451040 0.6921808305 4.791143e-01 - 39.1 0 -0.3431222274 0.9027792048 8.150103e-01 - 39.2 0 -1.7909380751 1.3055654289 1.704501e+00 - 39.3 0 -0.1798746191 1.5412842878 2.375557e+00 - 39.4 1 -0.1850961689 3.1834997435 1.013467e+01 - 39.5 1 0.4544226146 4.1394166439 1.713477e+01 - 40 0 0.5350190436 1.1330395646 1.283779e+00 - 40.1 0 0.4189342752 2.6940994046 7.258172e+00 - 40.2 0 0.4211994981 3.0396614212 9.239542e+00 - 40.3 1 0.0916687506 4.6762977762 2.186776e+01 - 41 1 -0.1035047421 1.9337158254 3.739257e+00 - 41.1 1 -0.4684202411 3.1956304458 1.021205e+01 - 41.2 0 0.5972615368 3.2846923557 1.078920e+01 - 41.3 1 0.9885613862 3.3813529415 1.143355e+01 - 41.4 1 -0.3908036794 3.5482964432 1.259041e+01 - 42 1 -0.0338893961 0.4859252973 2.361234e-01 - 42.1 1 -0.4498363172 4.3293134298 1.874295e+01 - 43 0 0.8965546110 0.5616614548 3.154636e-01 - 43.1 0 0.6199122090 1.0743579536 1.154245e+00 - 43.2 1 0.1804894429 2.6131797966 6.828709e+00 - 44 1 1.3221409285 0.7662644819 5.871613e-01 - 44.1 0 0.3416426284 2.6490291790 7.017356e+00 - 44.2 0 0.5706610068 3.3371910988 1.113684e+01 - 44.3 1 1.2679497430 4.1154200875 1.693668e+01 - 45 1 0.1414983160 0.1957449992 3.831610e-02 - 45.1 0 0.7220892521 1.9963831536 3.985546e+00 - 46 1 1.5391054233 1.3477755385 1.816499e+00 - 46.1 0 0.3889107049 2.8565793915 8.160046e+00 - 46.2 1 0.1248719493 4.4160729996 1.950170e+01 - 47 0 0.2014101100 0.6012621359 3.615162e-01 - 47.1 0 0.2982973539 2.4097121472 5.806713e+00 - 47.2 1 1.1518107179 2.9975794035 8.985482e+00 - 47.3 0 0.5196802157 3.1829649757 1.013127e+01 - 47.4 0 0.3702301552 4.6201055450 2.134538e+01 - 48 0 -0.2128602862 2.8607365978 8.183814e+00 - 48.1 1 -0.5337239976 2.9098354396 8.467142e+00 - 49 0 -0.5236770035 2.7179756400 7.387392e+00 - 50 1 0.3897705981 1.1762060679 1.383461e+00 - 51 1 -0.7213343736 1.4304436720 2.046169e+00 - 52 1 0.3758235358 2.1266646020 4.522702e+00 - 52.1 1 0.7138067080 3.1000545993 9.610339e+00 - 52.2 0 0.8872895233 3.1268477370 9.777177e+00 - 52.3 0 -0.9664587437 3.5711459327 1.275308e+01 - 52.4 1 0.0254566848 4.7983659909 2.302432e+01 - 52.5 1 0.4155259424 4.9818264414 2.481859e+01 - 53 1 0.5675736897 0.4965799209 2.465916e-01 - 53.1 1 -0.3154088781 3.5505357443 1.260630e+01 - 53.2 1 0.2162315769 4.5790420019 2.096763e+01 - 54 0 -0.0880802382 1.4034724841 1.969735e+00 - 54.1 1 0.4129127672 1.8812377600 3.539056e+00 - 54.2 0 1.0119546775 2.5107589352 6.303910e+00 - 54.3 1 -0.1112901990 2.7848406672 7.755338e+00 - 54.4 0 0.8587727145 4.0143877396 1.611531e+01 - 55 1 -0.0116453589 0.6118522980 3.743632e-01 - 55.1 1 0.5835528661 0.7463747414 5.570753e-01 - 55.2 1 -1.0010857254 2.8201208171 7.953081e+00 - 55.3 0 -0.4796526070 3.1326431572 9.813453e+00 - 55.4 1 -0.1202746964 3.2218102901 1.038006e+01 - 56 0 0.5176377612 1.2231332215 1.496055e+00 - 56.1 1 -1.1136932588 2.3573202139 5.556959e+00 - 56.2 1 -0.0168103281 2.5674936292 6.592024e+00 - 56.3 0 0.3933023606 2.9507164378 8.706727e+00 - 56.4 0 0.3714625139 3.2272730360 1.041529e+01 - 56.5 1 0.7811448179 3.4175522043 1.167966e+01 - 57 1 -1.0868304872 0.2370331448 5.618471e-02 - 57.1 1 0.8018626997 0.2481445030 6.157569e-02 - 57.2 0 -0.1159517011 1.1405586067 1.300874e+00 - 57.3 0 0.6785562445 2.1153886721 4.474869e+00 - 58 1 1.6476207996 1.2210099772 1.490865e+00 - 58.1 1 0.3402652711 1.6334245703 2.668076e+00 - 58.2 1 -0.1111300753 1.6791862890 2.819667e+00 - 58.3 1 -0.5409234285 2.6320121693 6.927488e+00 - 58.4 1 -0.1271327672 2.8477731440 8.109812e+00 - 58.5 1 0.8713264822 3.5715569824 1.275602e+01 - 59 0 0.4766421367 1.9023998594 3.619125e+00 - 59.1 1 1.0028089765 4.9736620474 2.473731e+01 - 60 0 0.5231452932 2.8854503250 8.325824e+00 - 61 1 -0.7190130614 0.7213630795 5.203647e-01 - 61.1 1 0.8353702312 2.3186947661 5.376345e+00 - 61.2 1 1.0229058138 2.5077313243 6.288716e+00 - 61.3 0 1.1717723589 3.1731073430 1.006861e+01 - 61.4 1 -0.0629201596 3.6022726283 1.297637e+01 - 62 1 -0.3979137604 0.5336771999 2.848114e-01 - 62.1 0 0.6830738372 0.6987666548 4.882748e-01 - 62.2 0 0.4301745954 3.4584309917 1.196074e+01 - 62.3 1 -0.0333139957 4.8028772371 2.306763e+01 - 63 0 0.3345678035 2.8097350930 7.894611e+00 - 63.1 1 0.3643769511 3.9653754211 1.572420e+01 - 64 1 0.3949911859 4.1191305732 1.696724e+01 - 65 1 1.2000091513 0.7076152589 5.007194e-01 - 65.1 1 0.0110122646 2.0252246363 4.101535e+00 - 65.2 0 -0.5776452043 3.1127382827 9.689140e+00 - 65.3 0 -0.1372183563 3.1969087943 1.022023e+01 - 66 1 -0.5081302805 3.4943454154 1.221045e+01 - 66.1 0 -0.1447837412 3.7677437009 1.419589e+01 - 66.2 0 0.1906241379 3.9486138616 1.559155e+01 - 67 0 1.6716027681 4.1728388879 1.741258e+01 - 68 0 0.5691848839 0.1291919907 1.669057e-02 - 68.1 0 0.1004860389 1.7809643946 3.171834e+00 - 68.2 0 -0.0061241827 2.0493205660 4.199715e+00 - 68.3 0 0.7443745962 2.9406870750 8.647640e+00 - 68.4 1 0.8726923437 4.0406670363 1.632699e+01 - 69 1 0.0381382683 4.1451198701 1.718202e+01 - 70 1 0.8126204217 0.1992557163 3.970284e-02 - 70.1 1 0.4691503050 0.4829774413 2.332672e-01 - 71 1 -0.5529062591 0.7741605386 5.993245e-01 - 71.1 1 -0.1103252087 1.4883817220 2.215280e+00 - 71.2 0 1.7178492547 4.0758526395 1.661257e+01 - 71.3 0 -1.0118346755 4.7048238723 2.213537e+01 - 71.4 0 1.8623785017 4.7242791823 2.231881e+01 - 72 1 -0.4521659275 0.9321196121 8.688470e-01 - 72.1 1 0.1375317317 1.1799991806 1.392398e+00 - 72.2 1 -0.4170988856 1.8917567329 3.578744e+00 - 72.3 0 0.7107266765 3.4853593935 1.214773e+01 - 72.4 0 0.1451969143 3.6884259700 1.360449e+01 - 72.5 1 1.6298050306 4.0854155901 1.669062e+01 - 73 1 -0.0307469467 4.6019889915 2.117830e+01 - 74 1 0.3730017941 1.4626806753 2.139435e+00 - 75 0 -0.4908003566 3.2524286874 1.057829e+01 - 76 1 -0.9888876620 1.8074807397 3.266987e+00 - 76.1 1 0.0003798292 4.2685073183 1.822015e+01 - 76.2 1 -0.8421863763 4.9688734859 2.468970e+01 - 77 1 -0.4986802480 0.8459033852 7.155525e-01 - 78 1 0.0417330969 0.8231094317 6.775091e-01 - 79 0 -0.3767450660 0.0583819521 3.408452e-03 - 79.1 1 0.1516000028 2.4406372628 5.956710e+00 - 79.2 0 -0.1888160741 3.2962526032 1.086528e+01 - 80 1 -0.0041558414 0.8985060186 8.073131e-01 - 80.1 0 -0.0329337062 1.3434670598 1.804904e+00 - 80.2 1 0.5046816157 2.8025900386 7.854511e+00 - 81 1 -0.9493950353 0.0101324962 1.026675e-04 - 81.1 1 0.2443038954 0.9421709494 8.876861e-01 - 81.2 1 0.6476958410 3.0542453879 9.328415e+00 - 81.3 1 0.4182528210 3.3456630446 1.119346e+01 - 82 1 1.1088801952 1.3791010005 1.901920e+00 - 82.1 1 0.9334157763 1.7601010622 3.097956e+00 - 82.2 0 0.4958140634 2.6233131927 6.881772e+00 - 83 1 0.5104724530 0.0537394290 2.887926e-03 - 83.1 0 -0.0513309106 2.9061570496 8.445749e+00 - 83.2 0 -0.2067792494 3.1189457362 9.727823e+00 - 83.3 1 -0.0534169155 4.7663642222 2.271823e+01 - 84 1 -0.0255753653 2.7254060237 7.427838e+00 - 84.1 0 -1.8234189877 3.3364784659 1.113209e+01 - 85 0 -0.0114038622 0.2977756259 8.867032e-02 - 85.1 0 -0.0577615939 1.7394116637 3.025553e+00 - 85.2 1 -0.2241856342 2.6846330194 7.207254e+00 - 85.3 1 -0.0520175929 3.1608762743 9.991139e+00 - 85.4 1 0.2892733846 3.9452053758 1.556465e+01 - 85.5 1 -0.3740417009 4.5092553482 2.033338e+01 - 86 0 0.4293735089 0.8476278360 7.184729e-01 - 86.1 1 -0.1363456521 1.0118629411 1.023867e+00 - 86.2 1 0.1230989293 1.2511159515 1.565291e+00 - 86.3 0 0.3305413955 2.1870554925 4.783212e+00 - 86.4 1 2.6003411822 2.4532935000 6.018649e+00 - 86.5 0 -0.1420690052 3.8206058508 1.459703e+01 - 87 0 1.0457427869 2.7069531474 7.327595e+00 - 87.1 1 -0.2973007190 3.4462517721 1.187665e+01 - 87.2 0 0.4396872616 4.5241666853 2.046808e+01 - 88 0 -0.0601928334 0.0005892443 3.472088e-07 - 88.1 0 -1.0124347595 0.7116099866 5.063888e-01 - 88.2 0 0.5730917016 2.4952722900 6.226384e+00 - 88.3 0 -0.0029455332 3.2995816297 1.088724e+01 - 89 1 1.5465903721 0.6462086167 4.175856e-01 - 90 0 0.0626760573 0.1696030737 2.876520e-02 - 90.1 1 1.1896872985 2.5980385230 6.749804e+00 - 90.2 1 0.2597888783 2.6651392167 7.102967e+00 - 90.3 0 0.6599799887 3.1242690247 9.761057e+00 - 91 0 1.1213651365 0.6382618390 4.073782e-01 - 91.1 0 1.2046371625 2.6224059286 6.877013e+00 - 91.2 1 0.3395603754 4.7772527603 2.282214e+01 - 92 1 0.4674939332 0.0737052364 5.432462e-03 - 93 0 0.2677965647 0.2788909199 7.778015e-02 - 93.1 1 1.6424445368 1.0357759963 1.072832e+00 - 93.2 0 0.7101700066 2.4916551099 6.208345e+00 - 93.3 1 1.1222322893 2.8876129608 8.338309e+00 - 93.4 0 1.4628960401 4.4639474002 1.992683e+01 - 94 1 -0.2904211940 0.8488043118 7.204688e-01 - 94.1 0 0.0147813580 1.0552454425 1.113543e+00 - 94.2 1 -0.4536774482 1.9445500884 3.781275e+00 - 94.3 0 0.6793464917 3.0710722448 9.431485e+00 - 94.4 0 -0.9411356550 3.0872731935 9.531256e+00 - 94.5 0 0.5683867264 4.3805759016 1.918945e+01 - 95 1 0.2375652188 2.0199063048 4.080021e+00 - 95.1 1 0.0767152977 4.0184444457 1.614790e+01 - 95.2 0 -0.6886731251 4.5596531732 2.079044e+01 - 96 1 0.7813892121 0.0311333477 9.692853e-04 - 96.1 0 0.3391519695 0.1324267720 1.753685e-02 - 96.2 0 -0.4857246503 0.6701303425 4.490747e-01 - 96.3 0 0.8771471244 2.1775037691 4.741523e+00 - 96.4 0 1.9030768981 2.2246142488 4.948909e+00 - 96.5 1 -0.1684332749 4.2377650598 1.795865e+01 - 97 0 1.3775130083 1.1955102731 1.429245e+00 - 97.1 0 -1.7323228619 4.9603108643 2.460468e+01 - 98 0 -1.2648518889 0.2041732438 4.168671e-02 - 98.1 0 -0.9042716241 0.4309578973 1.857247e-01 - 98.2 0 -0.1560385207 3.5172611906 1.237113e+01 - 99 1 0.7993356425 0.3531786101 1.247351e-01 - 99.1 1 1.0355522332 4.6789444226 2.189252e+01 - 99.2 1 -0.1150895843 4.9927084171 2.492714e+01 - 100 0 0.0369067906 1.0691387602 1.143058e+00 - 100.1 0 1.6023713093 1.5109344281 2.282923e+00 - 100.2 1 0.8861545820 2.1502332564 4.623503e+00 - 100.3 1 0.1277046316 3.8745574222 1.501220e+01 - 100.4 1 -0.0834577654 4.6567608765 2.168542e+01 - - $m6b$spM_id - center scale - C2 -0.6240921 0.6857108 - (Intercept) NA NA - B11 NA NA - - $m6b$spM_lvlone - center scale - b1 NA NA - c1 0.2559996 0.6718095 - time 2.5339403 1.3818094 - I(time^2) 8.3244468 7.0900029 - - $m6b$mu_reg_norm - [1] 0 - - $m6b$tau_reg_norm - [1] 1e-04 - - $m6b$shape_tau_norm - [1] 0.01 - - $m6b$rate_tau_norm - [1] 0.01 - - $m6b$mu_reg_binom - [1] 0 - - $m6b$tau_reg_binom - [1] 1e-04 - - $m6b$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m6b$shape_diag_RinvD - [1] "0.01" - - $m6b$rate_diag_RinvD - [1] "0.001" - - $m6b$RinvD_b1_id - [,1] [,2] - [1,] NA 0 - [2,] 0 NA - - $m6b$KinvD_b1_id - id - 3 - - - $m7a - $m7a$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m7a$M_lvlone - y ns(time, df = 2)1 ns(time, df = 2)2 time - 1 -13.0493856 0.149679884 -0.100552161 0.5090421822 - 1.1 -9.3335901 0.194627180 -0.129464178 0.6666076288 - 1.2 -22.3469852 0.520751993 -0.255001297 2.1304941282 - 1.3 -15.0417337 0.560875996 -0.221882653 2.4954441458 - 2 -12.0655434 0.578228925 -0.112131092 3.0164990982 - 2.1 -15.8674476 0.569154825 -0.023537063 3.2996806887 - 2.2 -7.8800006 0.481017405 0.344239525 4.1747569619 - 3 -11.4820604 0.244887044 -0.160459809 0.8478727890 - 3.1 -10.5983220 0.577508632 -0.098148611 3.0654308549 - 3.2 -22.4519157 0.394656259 0.627350944 4.7381553578 - 4 -1.2697775 0.099645844 -0.067449012 0.3371432109 - 4.1 -11.1215184 0.303598405 -0.194123505 1.0693019140 - 4.2 -3.6134138 0.569124392 -0.203401494 2.6148973033 - 4.3 -14.5982385 0.575895782 -0.077701736 3.1336532847 - 5 -6.8457515 0.305385958 -0.195093572 1.0762525082 - 5.1 -7.0551214 0.465582593 -0.257830256 1.7912546196 - 5.2 -12.3418980 0.576686171 -0.167647096 2.7960080339 - 5.3 -9.2366906 0.577072492 -0.164051419 2.8119940578 - 6 -5.1648211 0.463778516 -0.257558879 1.7815462884 - 7 -10.0599502 0.568748124 -0.020870495 3.3074087673 - 7.1 -18.3267285 0.538303487 0.130118265 3.7008403614 - 7.2 -12.5138426 0.389177122 0.644726730 4.7716691741 - 8 -1.6305331 0.317728152 -0.201687154 1.1246398522 - 8.1 -9.6520453 0.467694470 -0.258126481 1.8027009873 - 8.2 -1.5278462 0.470415425 -0.258472942 1.8175825174 - 8.3 -7.4172211 0.577614497 -0.157954646 2.8384267003 - 8.4 -7.1238609 0.565587961 -0.001314572 3.3630275307 - 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70.1 -7.8681167 0.142152150 -0.095624828 0.4829774413 - 71 -10.3352703 0.224660082 -0.148186027 0.7741605386 - 71.1 -7.5699888 0.404061694 -0.241169325 1.4883817220 - 71.2 -18.4680702 0.494456145 0.297232965 4.0758526395 - 71.3 -21.4316644 0.400083109 0.610104851 4.7048238723 - 71.4 -8.1137650 0.396918394 0.620166727 4.7242791823 - 72 -9.1848162 0.267608644 -0.173868105 0.9321196121 - 72.1 -23.7538846 0.331622374 -0.208876763 1.1799991806 - 72.2 -26.3421306 0.483553052 -0.259535840 1.8917567329 - 72.3 -27.2843801 0.557250123 0.043867968 3.4853593935 - 72.4 -20.8541617 0.539531742 0.124935350 3.6884259700 - 72.5 -12.8948965 0.493186596 0.301731319 4.0854155901 - 73 -2.6091307 0.416653462 0.557166987 4.6019889915 - 74 -8.2790175 0.398374343 -0.239025266 1.4626806753 - 75 -12.5029612 0.571465811 -0.039566472 3.2524286874 - 76 -6.0061671 0.468571460 -0.258242539 1.8074807397 - 76.1 -8.8149114 0.467692099 0.389714552 4.2685073183 - 76.2 -11.8359043 0.356636147 0.747440214 4.9688734859 - 77 0.4772521 0.244350694 -0.160138241 0.8459033852 - 78 -9.4105229 0.238126409 -0.156390504 0.8231094317 - 79 -1.0217265 0.017185170 -0.011698166 0.0583819521 - 79.1 -11.8125257 0.556250717 -0.229046871 2.4406372628 - 79.2 -10.5465186 0.569332668 -0.024715909 3.2962526032 - 80 -12.7366807 0.258596092 -0.168601439 0.8985060186 - 80.1 -9.0584783 0.371128894 -0.227727295 1.3434670598 - 80.2 -16.6381566 0.576850604 -0.166175033 2.8025900386 - 81 0.5547913 0.002838132 -0.001932272 0.0101324962 - 81.1 -4.0892715 0.270289449 -0.175420790 0.9421709494 - 81.2 1.8283303 0.577705879 -0.101395894 3.0542453879 - 81.3 -5.2166381 0.566618135 -0.007488169 3.3456630446 - 82 -3.0749381 0.379418082 -0.231331634 1.3791010005 - 82.1 -10.5506696 0.459751865 -0.256894448 1.7601010622 - 82.2 -18.2226347 0.569608491 -0.201947653 2.6233131927 - 83 -12.5872635 0.015804994 -0.010758945 0.0537394290 - 83.1 -11.9756502 0.578462150 -0.141485904 2.9061570496 - 83.2 -10.6744217 0.576302350 -0.082201802 3.1189457362 - 83.3 -19.2714012 0.390045811 0.641974120 4.7663642222 - 84 -2.6320312 0.574443229 -0.182687721 2.7254060237 - 84.1 -9.8140094 0.567147130 -0.010728700 3.3364784659 - 85 -12.3886736 0.088076477 -0.059694578 0.2977756259 - 85.1 -12.9196365 0.455813463 -0.256169485 1.7394116637 - 85.2 -9.6433248 0.572741560 -0.190738107 2.6846330194 - 85.3 -6.3296340 0.575059310 -0.069241031 3.1608762743 - 85.4 -7.0405525 0.511105405 0.236864567 3.9452053758 - 85.5 -13.6714939 0.431317668 0.509864701 4.5092553482 - 86 -10.8756412 0.244820346 -0.160419833 0.8476278360 - 86.1 -12.0055331 0.288690155 -0.185894081 1.0118629411 - 86.2 -13.3724699 0.349098660 -0.217529698 1.2511159515 - 86.3 -13.3252145 0.528356038 -0.252035671 2.1870554925 - 86.4 -14.9191290 0.557364765 -0.227464370 2.4532935000 - 86.5 -17.7515546 0.525665875 0.181352479 3.8206058508 - 87 -10.7027963 0.573710436 -0.186389542 2.7069531474 - 87.1 -22.4941954 0.560117024 0.029108727 3.4462517721 - 87.2 -14.9616716 0.428980289 0.517438583 4.5241666853 - 88 -2.2264493 0.000000000 0.000000000 0.0005892443 - 88.1 -8.9626474 0.207264162 -0.137408217 0.7116099866 - 88.2 -2.5095281 0.560862306 -0.221906390 2.4952722900 - 88.3 -16.3345673 0.569159986 -0.023571162 3.2995816297 - 89 -11.0459647 0.188867523 -0.125814019 0.6462086167 - 90 -4.5610239 0.050208031 -0.034133323 0.1696030737 - 90.1 -11.7036651 0.568115713 -0.206252945 2.5980385230 - 90.2 -5.3838521 0.571820822 -0.194419415 2.6651392167 - 90.3 -4.1636999 0.576158893 -0.080578820 3.1242690247 - 91 -7.1462503 0.186618632 -0.124384029 0.6382618390 - 91.1 -12.8374475 0.569556927 -0.202105359 2.6224059286 - 91.2 -18.2576707 0.388262274 0.647624750 4.7772527603 - 92 -6.4119222 0.021739973 -0.014797168 0.0737052364 - 93 5.2122168 0.082514929 -0.055956459 0.2788909199 - 93.1 3.1211725 0.294925898 -0.189365721 1.0357759963 - 93.2 -3.6841177 0.560572971 -0.222404132 2.4916551099 - 93.3 2.6223542 0.578306553 -0.146114569 2.8876129608 - 93.4 -11.1877696 0.438366385 0.486935041 4.4639474002 - 94 -6.9602492 0.245140656 -0.160611785 0.8488043118 - 94.1 -7.4318416 0.299972348 -0.192144469 1.0552454425 - 94.2 -4.3498045 0.492461920 -0.259601249 1.9445500884 - 94.3 -11.6340088 0.577401892 -0.096499489 3.0710722448 - 94.4 -12.9357964 0.577068448 -0.091721448 3.0872731935 - 94.5 -14.7648530 0.451106514 0.445102199 4.3805759016 - 95 -12.8849309 0.504518977 -0.258663317 2.0199063048 - 95.1 -9.7451502 0.501935320 0.270450881 4.0184444457 - 95.2 -0.8535063 0.423384987 0.535514358 4.5596531732 - 96 -4.9139832 0.009083415 -0.006183955 0.0311333477 - 96.1 -3.9582653 0.039181462 -0.026652251 0.1324267720 - 96.2 -9.6555492 0.195619870 -0.130091480 0.6701303425 - 96.3 -11.8690793 0.527105916 -0.252589672 2.1775037691 - 96.4 -11.0224373 0.533135775 -0.249644636 2.2246142488 - 96.5 -10.9530403 0.472120477 0.374710585 4.2377650598 - 97 -9.8540471 0.335470540 -0.210821063 1.1955102731 - 97.1 -19.2262840 0.358054995 0.742971132 4.9603108643 - 98 -11.9651231 0.060447025 -0.041067134 0.2041732438 - 98.1 -2.6515128 0.127061111 -0.085685639 0.4309578973 - 98.2 -12.2606382 0.554775889 0.056119803 3.5172611906 - 99 -11.4720500 0.104348118 -0.070591602 0.3531786101 - 99.1 -14.0596866 0.404279323 0.596741645 4.6789444226 - 99.2 -17.3939469 0.352686062 0.759881258 4.9927084171 - 100 1.1005874 0.303556402 -0.194100667 1.0691387602 - 100.1 -3.8226248 0.408995749 -0.242962199 1.5109344281 - 100.2 -0.9123182 0.523460505 -0.254052065 2.1502332564 - 100.3 -15.8389474 0.519529301 0.205126241 3.8745574222 - 100.4 -12.8093826 0.407863056 0.585307533 4.6567608765 - - $m7a$spM_lvlone - center scale - y -11.173370994 6.2496619 - ns(time, df = 2)1 0.430938966 0.1552899 - ns(time, df = 2)2 -0.008514259 0.2716805 - time 2.533940277 1.3818094 - - $m7a$mu_reg_norm - [1] 0 - - $m7a$tau_reg_norm - [1] 1e-04 - - $m7a$shape_tau_norm - [1] 0.01 - - $m7a$rate_tau_norm - [1] 0.01 - - $m7a$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m7a$shape_diag_RinvD - [1] "0.01" - - $m7a$rate_diag_RinvD - [1] "0.001" - - $m7a$RinvD_y_id - [,1] [,2] [,3] - [1,] NA 0 0 - [2,] 0 NA 0 - [3,] 0 0 NA - - $m7a$KinvD_y_id - id - 4 - - - $m7b - $m7b$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m7b$M_lvlone - y bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 - 1 -13.0493856 2.464812e-01 2.795125e-02 1.056568e-03 - 1.1 -9.3335901 3.005702e-01 4.627383e-02 2.374673e-03 - 1.2 -22.3469852 4.207566e-01 3.131043e-01 7.766508e-02 - 1.3 -15.0417337 3.751809e-01 3.748189e-01 1.248191e-01 - 2 -12.0655434 2.840211e-01 4.334471e-01 2.204958e-01 - 2.1 -15.8674476 2.280284e-01 4.443439e-01 2.886212e-01 - 2.2 -7.8800006 6.734283e-02 3.436638e-01 5.845947e-01 - 3 -11.4820604 3.510022e-01 7.175155e-02 4.889129e-03 - 3.1 -10.5983220 2.745130e-01 4.365427e-01 2.314032e-01 - 3.2 -22.4519157 7.402499e-03 1.377702e-01 8.546947e-01 - 4 -1.2697775 1.759000e-01 1.271593e-02 3.064145e-04 - 4.1 -11.1215184 3.966925e-01 1.080567e-01 9.811334e-03 - 4.2 -3.6134138 3.564330e-01 3.918838e-01 1.436202e-01 - 4.3 -14.5982385 2.611080e-01 4.400451e-01 2.472026e-01 - 5 -6.8457515 3.978591e-01 1.092729e-01 1.000401e-02 - 5.1 -7.0551214 4.425632e-01 2.475384e-01 4.615178e-02 - 5.2 -12.3418980 3.252782e-01 4.139339e-01 1.755844e-01 - 5.3 -9.2366906 3.223943e-01 4.156349e-01 1.786139e-01 - 6 -5.1648211 4.428374e-01 2.456041e-01 4.540519e-02 - 7 -10.0599502 2.264807e-01 4.443903e-01 2.906542e-01 - 7.1 -18.3267285 1.489135e-01 4.265274e-01 4.072291e-01 - 7.2 -12.5138426 5.621107e-03 1.213303e-01 8.729618e-01 - 8 -1.6305331 4.055465e-01 1.178507e-01 1.141571e-02 - 8.1 -9.6520453 4.422130e-01 2.498167e-01 4.704249e-02 - 8.2 -1.5278462 4.417145e-01 2.527749e-01 4.821755e-02 - 8.3 -7.4172211 3.175842e-01 4.183540e-01 1.836994e-01 - 8.4 -7.1238609 2.153406e-01 4.443014e-01 3.055682e-01 - 8.5 -8.8706950 3.313840e-02 2.640659e-01 7.014095e-01 - 9 -0.1634429 3.764000e-01 8.963850e-02 7.115710e-03 - 9.1 -2.6034300 3.028578e-01 4.257933e-01 1.995435e-01 - 9.2 -6.7272369 3.867756e-03 1.018621e-01 8.942212e-01 - 10 -6.4172202 4.023356e-01 3.428926e-01 9.741064e-02 - 10.1 -11.4834569 6.754273e-02 3.440071e-01 5.840297e-01 - 11 -8.7911356 4.138618e-01 1.284873e-01 1.329669e-02 - 11.1 -19.6645080 4.202663e-01 1.379992e-01 1.510454e-02 - 11.2 -20.2030932 4.443906e-01 2.179963e-01 3.564611e-02 - 11.3 -21.3082176 2.107621e-01 4.440456e-01 3.118469e-01 - 11.4 -14.5802901 3.796619e-03 1.009751e-01 8.951806e-01 - 12 -15.2006287 3.760749e-01 8.937848e-02 7.080604e-03 - 13 0.8058816 3.594997e-02 4.470732e-04 1.853264e-06 - 13.1 -13.6379208 1.757678e-01 4.375779e-01 3.631200e-01 - 14 -15.3422873 8.899359e-02 3.755915e-01 5.283862e-01 - 14.1 -10.0965208 2.933469e-02 2.513931e-01 7.181312e-01 - 14.2 -16.6452027 1.422882e-02 1.848582e-01 8.005479e-01 - 14.3 -15.8389733 1.045369e-02 1.611585e-01 8.281618e-01 - 15 -8.9424594 2.579648e-01 3.126382e-02 1.262997e-03 - 15.1 -22.0101983 4.146589e-01 1.295994e-01 1.350186e-02 - 15.2 -7.3975599 4.414565e-01 1.912323e-01 2.761298e-02 - 15.3 -10.3567334 6.383397e-04 4.279986e-02 9.565586e-01 - 16 -1.9691302 4.210987e-01 1.393442e-01 1.536996e-02 - 16.1 -9.9308357 3.640505e-01 3.853440e-01 1.359610e-01 - 16.2 -6.9626923 3.502297e-01 3.968497e-01 1.498918e-01 - 16.3 -3.2862557 1.764758e-01 4.377929e-01 3.620187e-01 - 16.4 -3.3972355 1.375222e-01 4.199669e-01 4.274999e-01 - 16.5 -11.5767835 9.753912e-02 3.857404e-01 5.084991e-01 - 17 -10.5474144 4.438097e-01 2.077898e-01 3.242877e-02 - 17.1 -7.6215009 3.836846e-01 3.658929e-01 1.163087e-01 - 17.2 -16.5386939 2.822509e-01 4.340620e-01 2.225087e-01 - 17.3 -20.0004774 2.213728e-01 4.444423e-01 2.974303e-01 - 17.4 -18.8505475 1.177221e-01 4.054379e-01 4.654462e-01 - 18 -19.7302351 3.836809e-01 3.658970e-01 1.163125e-01 - 19 -14.6177568 3.797281e-01 9.235553e-02 7.487412e-03 - 19.1 -17.8043866 4.087929e-01 1.218111e-01 1.209900e-02 - 19.2 -15.1641705 4.068733e-01 3.363802e-01 9.270014e-02 - 19.3 -16.6898418 6.198011e-02 3.340502e-01 6.001364e-01 - 20 -12.9059229 4.441175e-01 2.327127e-01 4.064630e-02 - 20.1 -16.8191201 4.434516e-01 2.405648e-01 4.350076e-02 - 20.2 -6.1010131 4.078512e-01 3.349176e-01 9.167540e-02 - 20.3 -7.9415371 4.067556e-01 3.365548e-01 9.282325e-02 - 20.4 -9.3904458 1.897411e-01 4.411553e-01 3.419010e-01 - 20.5 -13.3504189 6.758944e-02 3.440872e-01 5.838978e-01 - 21 -7.6974718 4.443436e-01 2.280367e-01 3.900939e-02 - 21.1 -11.9335526 2.954126e-01 4.290631e-01 2.077265e-01 - 21.2 -12.7064929 1.324112e-01 4.166163e-01 4.369447e-01 - 22 -21.5022909 3.790159e-01 3.708962e-01 1.209835e-01 - 22.1 -12.7745451 2.553655e-01 4.412374e-01 2.541330e-01 - 23 -3.5146508 4.425730e-01 1.975933e-01 2.940615e-02 - 23.1 -4.6724048 3.973563e-01 3.495745e-01 1.025128e-01 - 24 -2.5619821 3.194306e-01 4.173276e-01 1.817425e-01 - 25 -6.2944970 2.572266e-01 3.104254e-02 1.248755e-03 - 25.1 -3.8630505 4.440397e-01 2.106776e-01 3.331912e-02 - 25.2 -14.4205140 4.443440e-01 2.164524e-01 3.514669e-02 - 25.3 -19.6735037 2.350324e-01 4.439580e-01 2.795340e-01 - 25.4 -9.0288933 8.222967e-02 3.666568e-01 5.449664e-01 - 25.5 -9.0509738 7.014574e-02 3.483850e-01 5.767615e-01 - 26 -19.7340685 1.310265e-01 6.637071e-03 1.120657e-04 - 26.1 -14.1692728 3.825707e-01 3.671104e-01 1.174249e-01 - 26.2 -17.2819976 1.685712e-01 4.351796e-01 3.744833e-01 - 26.3 -24.6265576 6.623537e-02 3.417427e-01 5.877428e-01 - 27 -7.3354999 1.499147e-01 4.270472e-01 4.054956e-01 - 27.1 -11.1488468 5.720004e-02 3.247727e-01 6.146692e-01 - 28 -11.7996597 2.702018e-01 3.510883e-02 1.520629e-03 - 28.1 -8.2030122 3.255678e-01 4.137601e-01 1.752808e-01 - 28.2 -26.4317815 6.206227e-02 3.342036e-01 5.998924e-01 - 28.3 -18.5016071 2.938809e-02 2.515792e-01 7.178884e-01 - 29 -5.8551395 4.147335e-01 1.297043e-01 1.352130e-02 - 29.1 -2.0209442 4.433161e-01 2.417859e-01 4.395693e-02 - 29.2 -5.6368080 4.302445e-01 2.925253e-01 6.629647e-02 - 29.3 -3.8110961 2.063674e-01 4.436772e-01 3.179595e-01 - 30 -12.7217702 4.064458e-01 3.370127e-01 9.314693e-02 - 30.1 -17.0170140 1.695551e-01 4.355307e-01 3.729111e-01 - 30.2 -25.4236089 1.656511e-01 4.340933e-01 3.791846e-01 - 31 -17.0783921 3.740780e-02 2.770323e-01 6.838761e-01 - 32 -18.4338764 4.444327e-01 2.242037e-01 3.770147e-02 - 32.1 -19.4317212 3.037898e-01 4.253611e-01 1.985277e-01 - 32.2 -19.4738978 2.934042e-01 4.298900e-01 2.099554e-01 - 32.3 -21.4922645 6.218727e-02 3.344366e-01 5.995216e-01 - 33 2.0838099 5.239472e-03 9.198973e-06 5.383564e-09 - 33.1 -13.3172274 1.961435e-01 4.423422e-01 3.325230e-01 - 34 -10.0296691 4.410692e-01 1.893277e-01 2.708947e-02 - 34.1 -25.9426553 1.258797e-01 4.119406e-01 4.493576e-01 - 34.2 -18.5688138 1.109981e-01 3.994804e-01 4.792410e-01 - 34.3 -15.4173859 1.021312e-01 3.907186e-01 4.982514e-01 - 35 -14.3958113 4.300106e-01 1.559848e-01 1.886096e-02 - 35.1 -12.9457541 4.421254e-01 1.948544e-01 2.862555e-02 - 35.2 -16.1380691 2.607777e-02 2.395395e-01 7.334364e-01 - 36 -12.8166968 3.144468e-01 5.228505e-02 2.897921e-03 - 36.1 -14.3989481 4.423834e-01 2.487319e-01 4.661685e-02 - 36.2 -12.2436943 4.414010e-01 2.544996e-01 4.891246e-02 - 36.3 -15.0104638 5.187736e-02 3.135513e-01 6.317103e-01 - 36.4 -10.1775457 1.551774e-02 1.920212e-01 7.920430e-01 - 37 -15.2223495 4.316782e-01 2.888526e-01 6.442751e-02 - 37.1 -14.7526195 1.556358e-01 4.298489e-01 3.957315e-01 - 37.2 -19.8168430 1.007300e-01 3.892329e-01 5.013478e-01 - 38 -2.7065118 3.808083e-01 9.325985e-02 7.613103e-03 - 39 -8.7288138 3.084321e-01 4.960067e-02 2.658853e-03 - 39.1 -9.2746473 3.639117e-01 8.027461e-02 5.902543e-03 - 39.2 -18.2695344 4.278081e-01 1.514124e-01 1.786294e-02 - 39.3 -13.8219083 4.425678e-01 1.975596e-01 2.939647e-02 - 39.4 -16.2254704 2.512281e-01 4.419813e-01 2.591901e-01 - 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81.3 3.3456630446 - 82 1.3791010005 - 82.1 1.7601010622 - 82.2 2.6233131927 - 83 0.0537394290 - 83.1 2.9061570496 - 83.2 3.1189457362 - 83.3 4.7663642222 - 84 2.7254060237 - 84.1 3.3364784659 - 85 0.2977756259 - 85.1 1.7394116637 - 85.2 2.6846330194 - 85.3 3.1608762743 - 85.4 3.9452053758 - 85.5 4.5092553482 - 86 0.8476278360 - 86.1 1.0118629411 - 86.2 1.2511159515 - 86.3 2.1870554925 - 86.4 2.4532935000 - 86.5 3.8206058508 - 87 2.7069531474 - 87.1 3.4462517721 - 87.2 4.5241666853 - 88 0.0005892443 - 88.1 0.7116099866 - 88.2 2.4952722900 - 88.3 3.2995816297 - 89 0.6462086167 - 90 0.1696030737 - 90.1 2.5980385230 - 90.2 2.6651392167 - 90.3 3.1242690247 - 91 0.6382618390 - 91.1 2.6224059286 - 91.2 4.7772527603 - 92 0.0737052364 - 93 0.2788909199 - 93.1 1.0357759963 - 93.2 2.4916551099 - 93.3 2.8876129608 - 93.4 4.4639474002 - 94 0.8488043118 - 94.1 1.0552454425 - 94.2 1.9445500884 - 94.3 3.0710722448 - 94.4 3.0872731935 - 94.5 4.3805759016 - 95 2.0199063048 - 95.1 4.0184444457 - 95.2 4.5596531732 - 96 0.0311333477 - 96.1 0.1324267720 - 96.2 0.6701303425 - 96.3 2.1775037691 - 96.4 2.2246142488 - 96.5 4.2377650598 - 97 1.1955102731 - 97.1 4.9603108643 - 98 0.2041732438 - 98.1 0.4309578973 - 98.2 3.5172611906 - 99 0.3531786101 - 99.1 4.6789444226 - 99.2 4.9927084171 - 100 1.0691387602 - 100.1 1.5109344281 - 100.2 2.1502332564 - 100.3 3.8745574222 - 100.4 4.6567608765 - - $m7b$spM_lvlone - center scale - y -11.1733710 6.2496619 - bs(time, df = 3)1 0.2549546 0.1475335 - bs(time, df = 3)2 0.2657250 0.1531363 - bs(time, df = 3)3 0.2453352 0.2691884 - time 2.5339403 1.3818094 - - $m7b$mu_reg_norm - [1] 0 - - $m7b$tau_reg_norm - [1] 1e-04 - - $m7b$shape_tau_norm - [1] 0.01 - - $m7b$rate_tau_norm - [1] 0.01 - - $m7b$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m7b$shape_diag_RinvD - [1] "0.01" - - $m7b$rate_diag_RinvD - [1] "0.001" - - $m7b$RinvD_y_id - [,1] [,2] [,3] [,4] - [1,] NA 0 0 0 - [2,] 0 NA 0 0 - [3,] 0 0 NA 0 - [4,] 0 0 0 NA - - $m7b$KinvD_y_id - id - 5 - - - $m7c - $m7c$M_id - (Intercept) C1 - 1 1 0.7175865 - 2 1 0.7507170 - 3 1 0.7255954 - 4 1 0.7469352 - 5 1 0.7139120 - 6 1 0.7332505 - 7 1 0.7345929 - 8 1 0.7652589 - 9 1 0.7200622 - 10 1 0.7423879 - 11 1 0.7437448 - 12 1 0.7446470 - 13 1 0.7530186 - 14 1 0.7093137 - 15 1 0.7331192 - 16 1 0.7011390 - 17 1 0.7432395 - 18 1 0.7545191 - 19 1 0.7528487 - 20 1 0.7612865 - 21 1 0.7251719 - 22 1 0.7300630 - 23 1 0.7087249 - 24 1 0.7391938 - 25 1 0.7820641 - 26 1 0.7118298 - 27 1 0.7230857 - 28 1 0.7489353 - 29 1 0.7510888 - 30 1 0.7300717 - 31 1 0.7550721 - 32 1 0.7321898 - 33 1 0.7306414 - 34 1 0.7427216 - 35 1 0.7193042 - 36 1 0.7312888 - 37 1 0.7100436 - 38 1 0.7670184 - 39 1 0.7400449 - 40 1 0.7397304 - 41 1 0.7490966 - 42 1 0.7419274 - 43 1 0.7527810 - 44 1 0.7408315 - 45 1 0.7347550 - 46 1 0.7332398 - 47 1 0.7376481 - 48 1 0.7346179 - 49 1 0.7329402 - 50 1 0.7260436 - 51 1 0.7242910 - 52 1 0.7298067 - 53 1 0.7254741 - 54 1 0.7542067 - 55 1 0.7389952 - 56 1 0.7520638 - 57 1 0.7219958 - 58 1 0.7259632 - 59 1 0.7458606 - 60 1 0.7672421 - 61 1 0.7257179 - 62 1 0.7189892 - 63 1 0.7333356 - 64 1 0.7320243 - 65 1 0.7477711 - 66 1 0.7343974 - 67 1 0.7491624 - 68 1 0.7482736 - 69 1 0.7338267 - 70 1 0.7607742 - 71 1 0.7777600 - 72 1 0.7408143 - 73 1 0.7248271 - 74 1 0.7364916 - 75 1 0.7464926 - 76 1 0.7355430 - 77 1 0.7208449 - 78 1 0.7373573 - 79 1 0.7598079 - 80 1 0.7360415 - 81 1 0.7293932 - 82 1 0.7279309 - 83 1 0.7344643 - 84 1 0.7384350 - 85 1 0.7323716 - 86 1 0.7576597 - 87 1 0.7496139 - 88 1 0.7275239 - 89 1 0.7250648 - 90 1 0.7335262 - 91 1 0.7343980 - 92 1 0.7380425 - 93 1 0.7389460 - 94 1 0.7259951 - 95 1 0.7282840 - 96 1 0.7281676 - 97 1 0.7245642 - 98 1 0.7526938 - 99 1 0.7272309 - 100 1 0.7383460 - - $m7c$M_lvlone - y c1 ns(time, df = 3)1 ns(time, df = 3)2 - 1 -13.0493856 0.7592026489 -0.0731022196 0.222983368 - 1.1 -9.3335901 0.9548337990 -0.0896372079 0.286659651 - 1.2 -22.3469852 0.5612235156 0.1374616725 0.538466292 - 1.3 -15.0417337 1.1873391025 0.3061500570 0.485312041 - 2 -12.0655434 0.9192204198 0.5064248381 0.388851338 - 2.1 -15.8674476 -0.1870730476 0.5543647993 0.348347565 - 2.2 -7.8800006 1.2517512331 0.3402753582 0.338334366 - 3 -11.4820604 -0.0605087604 -0.1024946971 0.354448579 - 3.1 -10.5983220 0.3788637747 0.5187768948 0.380711276 - 3.2 -22.4519157 0.9872578281 0.0174998856 0.391617016 - 4 -1.2697775 1.4930175328 -0.0508146200 0.149748191 - 4.1 -11.1215184 -0.7692526880 -0.1067711172 0.427124949 - 4.2 -3.6134138 0.9180841450 0.3598480506 0.463409709 - 4.3 -14.5982385 -0.0541170782 0.5333652385 0.370048079 - 5 -6.8457515 -0.1376784521 -0.1066695938 0.429197233 - 5.1 -7.0551214 -0.2740585866 0.0046159078 0.552972700 - 5.2 -12.3418980 0.4670496929 0.4342731827 0.428967354 - 5.3 -9.2366906 0.1740288049 0.4402846745 0.425938437 - 6 -5.1648211 0.9868044683 0.0015253168 0.552692434 - 7 -10.0599502 -0.1280320918 0.5547950298 0.347509550 - 7.1 -18.3267285 0.4242971219 0.5158768825 0.324489976 - 7.2 -12.5138426 0.0777182491 -0.0038356900 0.395476156 - 8 -1.6305331 -0.5791408712 -0.1055283475 0.443238494 - 8.1 -9.6520453 0.3128604232 0.0083228157 0.553246856 - 8.2 -1.5278462 0.6258446356 0.0132420073 0.553513388 - 8.3 -7.4172211 -0.1040137707 0.4499876083 0.420954925 - 8.4 -7.1238609 0.0481450285 0.5564354493 0.341948600 - 8.5 -8.8706950 0.3831763675 0.2011700353 0.359621900 - 9 -0.1634429 -0.1757592269 -0.1064248362 0.393057590 - 9.1 -2.6034300 -0.1791541200 0.4771840649 0.406264856 - 9.2 -6.7272369 -0.0957042935 -0.0284447928 0.399948734 - 10 -6.4172202 -0.5598409704 0.2139341504 0.517577740 - 10.1 -11.4834569 -0.2318340451 0.3409274088 0.338245451 - 11 -8.7911356 0.5086859475 -0.1030921083 0.459317031 - 11.1 -19.6645080 0.4951758188 -0.0999663372 0.472517168 - 11.2 -20.2030932 -1.1022162541 -0.0371358422 0.544030949 - 11.3 -21.3082176 -0.0611636705 0.5563809266 0.339897056 - 11.4 -14.5802901 -0.4971774316 -0.0295495187 0.400149982 - 12 -15.2006287 -0.2433996286 -0.1063939605 0.392538168 - 13 0.8058816 0.8799673116 -0.0095916688 0.027579300 - 13.1 -13.6379208 0.1079022586 0.5424520669 0.328456601 - 14 -15.3422873 0.9991752617 0.4032689358 0.330500975 - 14.1 -10.0965208 -0.1094019046 0.1809335363 0.362994245 - 14.2 -16.6452027 0.1518967560 0.0816318440 0.380152387 - 14.3 -15.8389733 0.3521012473 0.0487734088 0.385997930 - 15 -8.9424594 0.3464447888 -0.0767078114 0.235874789 - 15.1 -22.0101983 -0.4767313971 -0.1027727004 0.460916543 - 15.2 -7.3975599 0.5759767791 -0.0657566366 0.527699447 - 15.3 -10.3567334 -0.1713452662 -0.0990627407 0.412872874 - 16 -1.9691302 0.4564754473 -0.0994524289 0.474297164 - 16.1 -9.9308357 1.0652558311 0.3388926675 0.472228980 - 16.2 -6.9626923 0.6971872493 0.3760850293 0.456327895 - 16.3 -3.2862557 0.5259331838 0.5429665219 0.328616483 - 16.4 -3.3972355 0.2046601798 0.5003814501 0.324030412 - 16.5 -11.5767835 1.0718540464 0.4243928946 0.328317423 - 17 -10.5474144 0.6048676222 -0.0490208458 0.538702746 - 17.1 -7.6215009 0.2323298304 0.2793033691 0.495414246 - 17.2 -16.5386939 1.2617499032 0.5088484549 0.387301236 - 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88.1 -0.198244638 0.7116099866 - 88.2 -0.301030746 2.4952722900 - 88.3 -0.079274225 3.2995816297 - 89 -0.181653221 0.6462086167 - 90 -0.049437014 0.1696030737 - 90.1 -0.281151299 2.5980385230 - 90.2 -0.266749122 2.6651392167 - 90.3 -0.139855690 3.1242690247 - 91 -0.179603880 0.6382618390 - 91.1 -0.276049539 2.6224059286 - 91.2 0.610915809 4.7772527603 - 92 -0.021435342 0.0737052364 - 93 -0.081013650 0.2788909199 - 93.1 -0.271814501 1.0357759963 - 93.2 -0.301681670 2.4916551099 - 93.3 -0.211259675 2.8876129608 - 93.4 0.447248720 4.4639474002 - 94 -0.231290551 0.8488043118 - 94.1 -0.275695453 1.0552454425 - 94.2 -0.359604210 1.9445500884 - 94.3 -0.156971195 3.0710722448 - 94.4 -0.151822700 3.0872731935 - 94.5 0.404570621 4.3805759016 - 95 -0.356607715 2.0199063048 - 95.1 0.225844939 4.0184444457 - 95.2 0.496766490 4.5596531732 - 96 -0.008958452 0.0311333477 - 96.1 -0.038605151 0.1324267720 - 96.2 -0.187779516 0.6701303425 - 96.3 -0.345060176 2.1775037691 - 96.4 -0.340252993 2.2246142488 - 96.5 0.332658824 4.2377650598 - 97 -0.301543584 1.1955102731 - 97.1 0.707918494 4.9603108643 - 98 -0.059473644 0.2041732438 - 98.1 -0.123949646 0.4309578973 - 98.2 0.004310743 3.5172611906 - 99 -0.102164665 0.3531786101 - 99.1 0.559124871 4.6789444226 - 99.2 0.725119709 4.9927084171 - 100 -0.278422879 1.0691387602 - 100.1 -0.344163920 1.5109344281 - 100.2 -0.347560939 2.1502332564 - 100.3 0.158668905 3.8745574222 - 100.4 0.547483173 4.6567608765 - - $m7c$spM_id - center scale - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m7c$spM_lvlone - center scale - y -11.17337099 6.2496619 - c1 0.25599956 0.6718095 - ns(time, df = 3)1 0.19883694 0.2502686 - ns(time, df = 3)2 0.38513689 0.1171115 - ns(time, df = 3)3 -0.07137294 0.2891059 - time 2.53394028 1.3818094 - - $m7c$mu_reg_norm - [1] 0 - - $m7c$tau_reg_norm - [1] 1e-04 - - $m7c$shape_tau_norm - [1] 0.01 - - $m7c$rate_tau_norm - [1] 0.01 - - $m7c$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m7c$shape_diag_RinvD - [1] "0.01" - - $m7c$rate_diag_RinvD - [1] "0.001" - - $m7c$RinvD_y_id - [,1] [,2] [,3] [,4] - [1,] NA 0 0 0 - [2,] 0 NA 0 0 - [3,] 0 0 NA 0 - [4,] 0 0 0 NA - - $m7c$KinvD_y_id - id - 5 - - - $m7d - $m7d$M_id - C2 (Intercept) C1 - 1 -1.381594459 1 0.7175865 - 2 0.344426024 1 0.7507170 - 3 NA 1 0.7255954 - 4 -0.228910007 1 0.7469352 - 5 NA 1 0.7139120 - 6 -2.143955482 1 0.7332505 - 7 -1.156567023 1 0.7345929 - 8 -0.598827660 1 0.7652589 - 9 NA 1 0.7200622 - 10 -1.006719032 1 0.7423879 - 11 0.239801450 1 0.7437448 - 12 -1.064969789 1 0.7446470 - 13 -0.538082688 1 0.7530186 - 14 NA 1 0.7093137 - 15 -1.781049276 1 0.7331192 - 16 NA 1 0.7011390 - 17 NA 1 0.7432395 - 18 -0.014579883 1 0.7545191 - 19 -2.121550136 1 0.7528487 - 20 NA 1 0.7612865 - 21 -0.363239698 1 0.7251719 - 22 -0.121568514 1 0.7300630 - 23 -0.951271111 1 0.7087249 - 24 NA 1 0.7391938 - 25 -0.974288621 1 0.7820641 - 26 -1.130632418 1 0.7118298 - 27 0.114339868 1 0.7230857 - 28 0.238334648 1 0.7489353 - 29 0.840744958 1 0.7510888 - 30 NA 1 0.7300717 - 31 NA 1 0.7550721 - 32 -1.466312154 1 0.7321898 - 33 -0.637352277 1 0.7306414 - 34 NA 1 0.7427216 - 35 NA 1 0.7193042 - 36 NA 1 0.7312888 - 37 NA 1 0.7100436 - 38 NA 1 0.7670184 - 39 0.006728205 1 0.7400449 - 40 NA 1 0.7397304 - 41 -1.663281353 1 0.7490966 - 42 0.161184794 1 0.7419274 - 43 0.457939180 1 0.7527810 - 44 -0.307070331 1 0.7408315 - 45 NA 1 0.7347550 - 46 -1.071668276 1 0.7332398 - 47 -0.814751321 1 0.7376481 - 48 -0.547630662 1 0.7346179 - 49 NA 1 0.7329402 - 50 -1.350213782 1 0.7260436 - 51 0.719054706 1 0.7242910 - 52 NA 1 0.7298067 - 53 -1.207130750 1 0.7254741 - 54 NA 1 0.7542067 - 55 -0.408600991 1 0.7389952 - 56 -0.271380529 1 0.7520638 - 57 -1.361925974 1 0.7219958 - 58 NA 1 0.7259632 - 59 NA 1 0.7458606 - 60 -0.323712205 1 0.7672421 - 61 NA 1 0.7257179 - 62 NA 1 0.7189892 - 63 -1.386906880 1 0.7333356 - 64 NA 1 0.7320243 - 65 NA 1 0.7477711 - 66 -0.565191691 1 0.7343974 - 67 -0.382899912 1 0.7491624 - 68 NA 1 0.7482736 - 69 -0.405642769 1 0.7338267 - 70 NA 1 0.7607742 - 71 -0.843748427 1 0.7777600 - 72 0.116003683 1 0.7408143 - 73 -0.778634325 1 0.7248271 - 74 NA 1 0.7364916 - 75 NA 1 0.7464926 - 76 NA 1 0.7355430 - 77 -0.632974758 1 0.7208449 - 78 NA 1 0.7373573 - 79 -0.778064615 1 0.7598079 - 80 NA 1 0.7360415 - 81 NA 1 0.7293932 - 82 -0.246123253 1 0.7279309 - 83 -1.239659782 1 0.7344643 - 84 -0.467772280 1 0.7384350 - 85 NA 1 0.7323716 - 86 -2.160485036 1 0.7576597 - 87 -0.657675572 1 0.7496139 - 88 NA 1 0.7275239 - 89 -0.696710744 1 0.7250648 - 90 NA 1 0.7335262 - 91 -0.179395847 1 0.7343980 - 92 -0.441545568 1 0.7380425 - 93 -0.685799334 1 0.7389460 - 94 NA 1 0.7259951 - 95 0.191929445 1 0.7282840 - 96 NA 1 0.7281676 - 97 -0.069760671 1 0.7245642 - 98 NA 1 0.7526938 - 99 NA 1 0.7272309 - 100 NA 1 0.7383460 - - $m7d$M_lvlone - y c1 time ns(time, df = 3)1 - 1 -13.0493856 0.7592026489 0.5090421822 -0.0731022196 - 1.1 -9.3335901 0.9548337990 0.6666076288 -0.0896372079 - 1.2 -22.3469852 0.5612235156 2.1304941282 0.1374616725 - 1.3 -15.0417337 1.1873391025 2.4954441458 0.3061500570 - 2 -12.0655434 0.9192204198 3.0164990982 0.5064248381 - 2.1 -15.8674476 -0.1870730476 3.2996806887 0.5543647993 - 2.2 -7.8800006 1.2517512331 4.1747569619 0.3402753582 - 3 -11.4820604 -0.0605087604 0.8478727890 -0.1024946971 - 3.1 -10.5983220 0.3788637747 3.0654308549 0.5187768948 - 3.2 -22.4519157 0.9872578281 4.7381553578 0.0174998856 - 4 -1.2697775 1.4930175328 0.3371432109 -0.0508146200 - 4.1 -11.1215184 -0.7692526880 1.0693019140 -0.1067711172 - 4.2 -3.6134138 0.9180841450 2.6148973033 0.3598480506 - 4.3 -14.5982385 -0.0541170782 3.1336532847 0.5333652385 - 5 -6.8457515 -0.1376784521 1.0762525082 -0.1066695938 - 5.1 -7.0551214 -0.2740585866 1.7912546196 0.0046159078 - 5.2 -12.3418980 0.4670496929 2.7960080339 0.4342731827 - 5.3 -9.2366906 0.1740288049 2.8119940578 0.4402846745 - 6 -5.1648211 0.9868044683 1.7815462884 0.0015253168 - 7 -10.0599502 -0.1280320918 3.3074087673 0.5547950298 - 7.1 -18.3267285 0.4242971219 3.7008403614 0.5158768825 - 7.2 -12.5138426 0.0777182491 4.7716691741 -0.0038356900 - 8 -1.6305331 -0.5791408712 1.1246398522 -0.1055283475 - 8.1 -9.6520453 0.3128604232 1.8027009873 0.0083228157 - 8.2 -1.5278462 0.6258446356 1.8175825174 0.0132420073 - 8.3 -7.4172211 -0.1040137707 2.8384267003 0.4499876083 - 8.4 -7.1238609 0.0481450285 3.3630275307 0.5564354493 - 8.5 -8.8706950 0.3831763675 4.4360849704 0.2011700353 - 9 -0.1634429 -0.1757592269 0.9607803822 -0.1064248362 - 9.1 -2.6034300 -0.1791541200 2.9177753383 0.4771840649 - 9.2 -6.7272369 -0.0957042935 4.8100892501 -0.0284447928 - 10 -6.4172202 -0.5598409704 2.2975509102 0.2139341504 - 10.1 -11.4834569 -0.2318340451 4.1734118364 0.3409274088 - 11 -8.7911356 0.5086859475 1.1832662905 -0.1030921083 - 11.1 -19.6645080 0.4951758188 1.2346051680 -0.0999663372 - 11.2 -20.2030932 -1.1022162541 1.6435316263 -0.0371358422 - 11.3 -21.3082176 -0.0611636705 3.3859017969 0.5563809266 - 11.4 -14.5802901 -0.4971774316 4.8118087661 -0.0295495187 - 12 -15.2006287 -0.2433996286 0.9591987054 -0.1063939605 - 13 0.8058816 0.8799673116 0.0619085738 -0.0095916688 - 13.1 -13.6379208 0.1079022586 3.5621061502 0.5424520669 - 14 -15.3422873 0.9991752617 4.0364430007 0.4032689358 - 14.1 -10.0965208 -0.1094019046 4.4710561272 0.1809335363 - 14.2 -16.6452027 0.1518967560 4.6359198843 0.0816318440 - 14.3 -15.8389733 0.3521012473 4.6886152599 0.0487734088 - 15 -8.9424594 0.3464447888 0.5402063532 -0.0767078114 - 15.1 -22.0101983 -0.4767313971 1.1893180816 -0.1027727004 - 15.2 -7.3975599 0.5759767791 1.5094739688 -0.0657566366 - 15.3 -10.3567334 -0.1713452662 4.9193474615 -0.0990627407 - 16 -1.9691302 0.4564754473 1.2417913869 -0.0994524289 - 16.1 -9.9308357 1.0652558311 2.5675726333 0.3388926675 - 16.2 -6.9626923 0.6971872493 2.6524101500 0.3760850293 - 16.3 -3.2862557 0.5259331838 3.5585018690 0.5429665219 - 16.4 -3.3972355 0.2046601798 3.7612454291 0.5003814501 - 16.5 -11.5767835 1.0718540464 3.9851612889 0.4243928946 - 17 -10.5474144 0.6048676222 1.5925356350 -0.0490208458 - 17.1 -7.6215009 0.2323298304 2.4374032998 0.2793033691 - 17.2 -16.5386939 1.2617499032 3.0256489082 0.5088484549 - 17.3 -20.0004774 -0.3913230895 3.3329089405 0.5558624020 - 17.4 -18.8505475 0.9577299112 3.8693758985 0.4671596304 - 18 -19.7302351 -0.0050324072 2.4374292302 0.2793154325 - 19 -14.6177568 -0.4187468937 0.9772165376 -0.1067031159 - 19.1 -17.8043866 -0.4478828944 1.1466335913 -0.1047524970 - 19.2 -15.1641705 -1.1966721302 2.2599126538 0.1964187590 - 19.3 -16.6898418 -0.5877091668 4.2114245973 0.3222225327 - 20 -12.9059229 0.6838223064 1.7170160066 -0.0177612481 - 20.1 -16.8191201 0.3278571109 1.7562902288 -0.0062817988 - 20.2 -6.1010131 -0.8489831990 2.2515566566 0.1925456183 - 20.3 -7.9415371 1.3169975191 2.2609123867 0.1968825793 - 20.4 -9.3904458 0.0444804531 3.4913365287 0.5508408484 - 20.5 -13.3504189 -0.4535207652 4.1730977828 0.3410795409 - 21 -7.6974718 -0.4030302960 1.6936582839 -0.0242133322 - 21.1 -11.9335526 -0.4069674045 2.9571191233 0.4894906279 - 21.2 -12.7064929 1.0650265940 3.7887385779 0.4925855178 - 22 -21.5022909 -0.0673274516 2.4696226232 0.2942488077 - 22.1 -12.7745451 0.9601388170 3.1626627257 0.5385758261 - 23 -3.5146508 0.5556634840 1.5414533857 -0.0596896592 - 23.1 -4.6724048 1.4407865964 2.3369736120 0.2323619738 - 24 -2.5619821 0.3856376411 2.8283136466 0.4463109938 - 25 -6.2944970 0.3564400705 0.5381704110 -0.0764769279 - 25.1 -3.8630505 0.0982553434 1.6069735331 -0.0457828435 - 25.2 -14.4205140 0.1928682598 1.6358226922 -0.0390131484 - 25.3 -19.6735037 -0.0192488594 3.2646870392 0.5517873130 - 25.4 -9.0288933 0.4466012931 4.0782226040 0.3851356619 - 25.5 -9.0509738 1.1425193342 4.1560292873 0.3492871599 - 26 -19.7340685 0.5341531449 0.2412706357 -0.0370005446 - 26.1 -14.1692728 1.2268695927 2.4451737676 0.2829160492 - 26.2 -17.2819976 0.3678294939 3.5988757887 0.5366821080 - 26.3 -24.6265576 0.5948516018 4.1822362854 0.3366364198 - 27 -7.3354999 -0.3342844147 3.6955824879 0.5171168233 - 27.1 -11.1488468 -0.4835141229 4.2451434687 0.3051619029 - 28 -11.7996597 -0.7145915499 0.5746519344 -0.0805110543 - 28.1 -8.2030122 0.5063671955 2.7943964268 0.4336613409 - 28.2 -26.4317815 -0.2067413142 4.2108539480 0.3225075315 - 28.3 -18.5016071 0.1196789973 4.4705521734 0.1812274493 - 29 -5.8551395 0.1392699487 1.1898884235 -0.1027419307 - 29.1 -2.0209442 0.7960234776 1.7624059319 -0.0044221127 - 29.2 -5.6368080 1.0398214352 2.0210406382 0.0902449711 - 29.3 -3.8110961 0.0813246429 3.4078777023 0.5559364351 - 30 -12.7217702 -0.3296323050 2.2635366488 0.1981005081 - 30.1 -17.0170140 1.3635850954 3.5938334477 0.5375291120 - 30.2 -25.4236089 0.7354171050 3.6138710892 0.5340597966 - 31 -17.0783921 0.3708398217 4.3988140998 0.2223657818 - 32 -18.4338764 -0.0474059668 1.6745209007 -0.0292943991 - 32.1 -19.4317212 1.2507771489 2.9128167813 0.4755751076 - 32.2 -19.4738978 0.1142915519 2.9676558380 0.4926434114 - 32.3 -21.4922645 0.6773270619 4.2099863547 0.3229405911 - 33 2.0838099 0.1774293842 0.0093385763 -0.0013701847 - 33.1 -13.3172274 0.6159606291 3.4591242753 0.5534368438 - 34 -10.0296691 0.8590979166 1.4998774312 -0.0674871387 - 34.1 -25.9426553 0.0546216775 3.8242761395 0.4818424185 - 34.2 -18.5688138 -0.0897224473 3.9072251692 0.4539624195 - 34.3 -15.4173859 0.4163395571 3.9582124643 0.4349720689 - 35 -14.3958113 -1.4693520528 1.3294299203 -0.0916157074 - 35.1 -12.9457541 -0.3031734330 1.5276966314 -0.0623564831 - 35.2 -16.1380691 -0.6045512101 4.5025920868 0.1624134259 - 36 -12.8166968 0.9823048960 0.7123168337 -0.0935638890 - 36.1 -14.3989481 1.4466051416 1.7972493160 0.0065488640 - 36.2 -12.2436943 1.1606752905 1.8262697803 0.0161646973 - 36.3 -15.0104638 0.8373091576 4.2840119381 0.2849748388 - 36.4 -10.1775457 0.2640591685 4.6194464504 0.0918066953 - 37 -15.2223495 0.1177313455 2.0018732361 0.0823235483 - 37.1 -14.7526195 -0.1415483779 3.6656836793 0.5238280708 - 37.2 -19.8168430 0.0054610124 3.9663937816 0.4317992421 - 38 -2.7065118 0.8078948077 0.9826511063 -0.1067779048 - 39 -8.7288138 0.9876451040 0.6921808305 -0.0918869929 - 39.1 -9.2746473 -0.3431222274 0.9027792048 -0.1048343071 - 39.2 -18.2695344 -1.7909380751 1.3055654289 -0.0940424764 - 39.3 -13.8219083 -0.1798746191 1.5412842878 -0.0597229655 - 39.4 -16.2254704 -0.1850961689 3.1834997435 0.5419341500 - 39.5 -21.7283648 0.4544226146 4.1394166439 0.3571597096 - 40 1.8291916 0.5350190436 1.1330395646 -0.1052513278 - 40.1 -6.6916432 0.4189342752 2.6940994046 0.3936778108 - 40.2 -1.6278171 0.4211994981 3.0396614212 0.5124598453 - 40.3 -10.5749790 0.0916687506 4.6762977762 0.0564941376 - 41 -3.1556121 -0.1035047421 1.9337158254 0.0551736455 - 41.1 -11.5895327 -0.4684202411 3.1956304458 0.5437374685 - 41.2 -18.9352091 0.5972615368 3.2846923557 0.5533874833 - 41.3 -15.9788960 0.9885613862 3.3813529415 0.5564251145 - 41.4 -9.6070508 -0.3908036794 3.5482964432 0.5443730748 - 42 -5.2159485 -0.0338893961 0.4859252973 -0.0703321485 - 42.1 -15.9878743 -0.4498363172 4.3293134298 0.2607797560 - 43 -16.6104361 0.8965546110 0.5616614548 -0.0790999084 - 43.1 -9.5549441 0.6199122090 1.0743579536 -0.1066987969 - 43.2 -14.2003491 0.1804894429 2.6131797966 0.3590962645 - 44 -8.1969033 1.3221409285 0.7662644819 -0.0976262102 - 44.1 -19.9270197 0.3416426284 2.6490291790 0.3746366123 - 44.2 -22.6521171 0.5706610068 3.3371910988 0.5559890192 - 44.3 -21.1903736 1.2679497430 4.1154200875 0.3683249841 - 45 -0.5686627 0.1414983160 0.1957449992 -0.0301940136 - 45.1 -7.5645740 0.7220892521 1.9963831536 0.0800764791 - 46 -19.1624789 1.5391054233 1.3477755385 -0.0895971055 - 46.1 -18.4487574 0.3889107049 2.8565793915 0.4564725410 - 46.2 -15.8222682 0.1248719493 4.4160729996 0.2125999596 - 47 -5.4165074 0.2014101100 0.6012621359 -0.0833108913 - 47.1 -15.0975029 0.2982973539 2.4097121472 0.2663955892 - 47.2 -12.9971413 1.1518107179 2.9975794035 0.5012527278 - 47.3 -10.6844521 0.5196802157 3.1829649757 0.5418520608 - 47.4 -18.2214784 0.3702301552 4.6201055450 0.0914005525 - 48 -8.3101471 -0.2128602862 2.8607365978 0.4579365172 - 48.1 -18.3854275 -0.5337239976 2.9098354396 0.4746016472 - 49 -13.0130319 -0.5236770035 2.7179756400 0.4035144992 - 50 -10.4579977 0.3897705981 1.1762060679 -0.1034484387 - 51 -19.3157621 -0.7213343736 1.4304436720 -0.0787988153 - 52 -4.4747188 0.3758235358 2.1266646020 0.1357604672 - 52.1 -4.3163827 0.7138067080 3.1000545993 0.5265788733 - 52.2 -6.9761408 0.8872895233 3.1268477370 0.5320547699 - 52.3 -20.1764756 -0.9664587437 3.5711459327 0.5411213524 - 52.4 -8.9036692 0.0254566848 4.7983659909 -0.0209202834 - 52.5 -5.6949642 0.4155259424 4.9818264414 -0.1396816763 - 53 -10.3141887 0.5675736897 0.4965799209 -0.0716187009 - 53.1 -8.2642654 -0.3154088781 3.5505357443 0.5440708053 - 53.2 -9.1691554 0.2162315769 4.5790420019 0.1165457070 - 54 -6.2198754 -0.0880802382 1.4034724841 -0.0826335785 - 54.1 -15.7192609 0.4129127672 1.8812377600 0.0354880963 - 54.2 -13.0978998 1.0119546775 2.5107589352 0.3131694667 - 54.3 -5.1195299 -0.1112901990 2.7848406672 0.4300121383 - 54.4 -16.5771751 0.8587727145 4.0143877396 0.4125104256 - 55 -5.7348534 -0.0116453589 0.6118522980 -0.0843902223 - 55.1 -7.3217494 0.5835528661 0.7463747414 -0.0962032547 - 55.2 -12.2171938 -1.0010857254 2.8201208171 0.4432998504 - 55.3 -12.9821266 -0.4796526070 3.1326431572 0.5331728179 - 55.4 -14.8599983 -0.1202746964 3.2218102901 0.5472394872 - 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99.2 0.421642124 0.725119709 - 100 0.427076142 -0.278422879 - 100.1 0.527917102 -0.344163920 - 100.2 0.536384181 -0.347560939 - 100.3 0.325078572 0.158668905 - 100.4 0.382446660 0.547483173 - - $m7d$spM_id - center scale - C2 -0.6240921 0.68571078 - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m7d$spM_lvlone - center scale - y -11.17337099 6.2496619 - c1 0.25599956 0.6718095 - time 2.53394028 1.3818094 - ns(time, df = 3)1 0.19883694 0.2502686 - ns(time, df = 3)2 0.38513689 0.1171115 - ns(time, df = 3)3 -0.07137294 0.2891059 - - $m7d$mu_reg_norm - [1] 0 - - $m7d$tau_reg_norm - [1] 1e-04 - - $m7d$shape_tau_norm - [1] 0.01 - - $m7d$rate_tau_norm - [1] 0.01 - - $m7d$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m7d$shape_diag_RinvD - [1] "0.01" - - $m7d$rate_diag_RinvD - [1] "0.001" - - $m7d$RinvD_y_id - [,1] [,2] - [1,] NA 0 - [2,] 0 NA - - $m7d$KinvD_y_id - id - 3 - - - $m7e - $m7e$M_id - C2 (Intercept) C1 - 1 -1.381594459 1 0.7175865 - 2 0.344426024 1 0.7507170 - 3 NA 1 0.7255954 - 4 -0.228910007 1 0.7469352 - 5 NA 1 0.7139120 - 6 -2.143955482 1 0.7332505 - 7 -1.156567023 1 0.7345929 - 8 -0.598827660 1 0.7652589 - 9 NA 1 0.7200622 - 10 -1.006719032 1 0.7423879 - 11 0.239801450 1 0.7437448 - 12 -1.064969789 1 0.7446470 - 13 -0.538082688 1 0.7530186 - 14 NA 1 0.7093137 - 15 -1.781049276 1 0.7331192 - 16 NA 1 0.7011390 - 17 NA 1 0.7432395 - 18 -0.014579883 1 0.7545191 - 19 -2.121550136 1 0.7528487 - 20 NA 1 0.7612865 - 21 -0.363239698 1 0.7251719 - 22 -0.121568514 1 0.7300630 - 23 -0.951271111 1 0.7087249 - 24 NA 1 0.7391938 - 25 -0.974288621 1 0.7820641 - 26 -1.130632418 1 0.7118298 - 27 0.114339868 1 0.7230857 - 28 0.238334648 1 0.7489353 - 29 0.840744958 1 0.7510888 - 30 NA 1 0.7300717 - 31 NA 1 0.7550721 - 32 -1.466312154 1 0.7321898 - 33 -0.637352277 1 0.7306414 - 34 NA 1 0.7427216 - 35 NA 1 0.7193042 - 36 NA 1 0.7312888 - 37 NA 1 0.7100436 - 38 NA 1 0.7670184 - 39 0.006728205 1 0.7400449 - 40 NA 1 0.7397304 - 41 -1.663281353 1 0.7490966 - 42 0.161184794 1 0.7419274 - 43 0.457939180 1 0.7527810 - 44 -0.307070331 1 0.7408315 - 45 NA 1 0.7347550 - 46 -1.071668276 1 0.7332398 - 47 -0.814751321 1 0.7376481 - 48 -0.547630662 1 0.7346179 - 49 NA 1 0.7329402 - 50 -1.350213782 1 0.7260436 - 51 0.719054706 1 0.7242910 - 52 NA 1 0.7298067 - 53 -1.207130750 1 0.7254741 - 54 NA 1 0.7542067 - 55 -0.408600991 1 0.7389952 - 56 -0.271380529 1 0.7520638 - 57 -1.361925974 1 0.7219958 - 58 NA 1 0.7259632 - 59 NA 1 0.7458606 - 60 -0.323712205 1 0.7672421 - 61 NA 1 0.7257179 - 62 NA 1 0.7189892 - 63 -1.386906880 1 0.7333356 - 64 NA 1 0.7320243 - 65 NA 1 0.7477711 - 66 -0.565191691 1 0.7343974 - 67 -0.382899912 1 0.7491624 - 68 NA 1 0.7482736 - 69 -0.405642769 1 0.7338267 - 70 NA 1 0.7607742 - 71 -0.843748427 1 0.7777600 - 72 0.116003683 1 0.7408143 - 73 -0.778634325 1 0.7248271 - 74 NA 1 0.7364916 - 75 NA 1 0.7464926 - 76 NA 1 0.7355430 - 77 -0.632974758 1 0.7208449 - 78 NA 1 0.7373573 - 79 -0.778064615 1 0.7598079 - 80 NA 1 0.7360415 - 81 NA 1 0.7293932 - 82 -0.246123253 1 0.7279309 - 83 -1.239659782 1 0.7344643 - 84 -0.467772280 1 0.7384350 - 85 NA 1 0.7323716 - 86 -2.160485036 1 0.7576597 - 87 -0.657675572 1 0.7496139 - 88 NA 1 0.7275239 - 89 -0.696710744 1 0.7250648 - 90 NA 1 0.7335262 - 91 -0.179395847 1 0.7343980 - 92 -0.441545568 1 0.7380425 - 93 -0.685799334 1 0.7389460 - 94 NA 1 0.7259951 - 95 0.191929445 1 0.7282840 - 96 NA 1 0.7281676 - 97 -0.069760671 1 0.7245642 - 98 NA 1 0.7526938 - 99 NA 1 0.7272309 - 100 NA 1 0.7383460 - - $m7e$M_lvlone - y c1 ns(time, df = 3)1 ns(time, df = 3)2 - 1 -13.0493856 0.7592026489 -0.0731022196 0.222983368 - 1.1 -9.3335901 0.9548337990 -0.0896372079 0.286659651 - 1.2 -22.3469852 0.5612235156 0.1374616725 0.538466292 - 1.3 -15.0417337 1.1873391025 0.3061500570 0.485312041 - 2 -12.0655434 0.9192204198 0.5064248381 0.388851338 - 2.1 -15.8674476 -0.1870730476 0.5543647993 0.348347565 - 2.2 -7.8800006 1.2517512331 0.3402753582 0.338334366 - 3 -11.4820604 -0.0605087604 -0.1024946971 0.354448579 - 3.1 -10.5983220 0.3788637747 0.5187768948 0.380711276 - 3.2 -22.4519157 0.9872578281 0.0174998856 0.391617016 - 4 -1.2697775 1.4930175328 -0.0508146200 0.149748191 - 4.1 -11.1215184 -0.7692526880 -0.1067711172 0.427124949 - 4.2 -3.6134138 0.9180841450 0.3598480506 0.463409709 - 4.3 -14.5982385 -0.0541170782 0.5333652385 0.370048079 - 5 -6.8457515 -0.1376784521 -0.1066695938 0.429197233 - 5.1 -7.0551214 -0.2740585866 0.0046159078 0.552972700 - 5.2 -12.3418980 0.4670496929 0.4342731827 0.428967354 - 5.3 -9.2366906 0.1740288049 0.4402846745 0.425938437 - 6 -5.1648211 0.9868044683 0.0015253168 0.552692434 - 7 -10.0599502 -0.1280320918 0.5547950298 0.347509550 - 7.1 -18.3267285 0.4242971219 0.5158768825 0.324489976 - 7.2 -12.5138426 0.0777182491 -0.0038356900 0.395476156 - 8 -1.6305331 -0.5791408712 -0.1055283475 0.443238494 - 8.1 -9.6520453 0.3128604232 0.0083228157 0.553246856 - 8.2 -1.5278462 0.6258446356 0.0132420073 0.553513388 - 8.3 -7.4172211 -0.1040137707 0.4499876083 0.420954925 - 8.4 -7.1238609 0.0481450285 0.5564354493 0.341948600 - 8.5 -8.8706950 0.3831763675 0.2011700353 0.359621900 - 9 -0.1634429 -0.1757592269 -0.1064248362 0.393057590 - 9.1 -2.6034300 -0.1791541200 0.4771840649 0.406264856 - 9.2 -6.7272369 -0.0957042935 -0.0284447928 0.399948734 - 10 -6.4172202 -0.5598409704 0.2139341504 0.517577740 - 10.1 -11.4834569 -0.2318340451 0.3409274088 0.338245451 - 11 -8.7911356 0.5086859475 -0.1030921083 0.459317031 - 11.1 -19.6645080 0.4951758188 -0.0999663372 0.472517168 - 11.2 -20.2030932 -1.1022162541 -0.0371358422 0.544030949 - 11.3 -21.3082176 -0.0611636705 0.5563809266 0.339897056 - 11.4 -14.5802901 -0.4971774316 -0.0295495187 0.400149982 - 12 -15.2006287 -0.2433996286 -0.1063939605 0.392538168 - 13 0.8058816 0.8799673116 -0.0095916688 0.027579300 - 13.1 -13.6379208 0.1079022586 0.5424520669 0.328456601 - 14 -15.3422873 0.9991752617 0.4032689358 0.330500975 - 14.1 -10.0965208 -0.1094019046 0.1809335363 0.362994245 - 14.2 -16.6452027 0.1518967560 0.0816318440 0.380152387 - 14.3 -15.8389733 0.3521012473 0.0487734088 0.385997930 - 15 -8.9424594 0.3464447888 -0.0767078114 0.235874789 - 15.1 -22.0101983 -0.4767313971 -0.1027727004 0.460916543 - 15.2 -7.3975599 0.5759767791 -0.0657566366 0.527699447 - 15.3 -10.3567334 -0.1713452662 -0.0990627407 0.412872874 - 16 -1.9691302 0.4564754473 -0.0994524289 0.474297164 - 16.1 -9.9308357 1.0652558311 0.3388926675 0.472228980 - 16.2 -6.9626923 0.6971872493 0.3760850293 0.456327895 - 16.3 -3.2862557 0.5259331838 0.5429665219 0.328616483 - 16.4 -3.3972355 0.2046601798 0.5003814501 0.324030412 - 16.5 -11.5767835 1.0718540464 0.4243928946 0.328317423 - 17 -10.5474144 0.6048676222 -0.0490208458 0.538702746 - 17.1 -7.6215009 0.2323298304 0.2793033691 0.495414246 - 17.2 -16.5386939 1.2617499032 0.5088484549 0.387301236 - 17.3 -20.0004774 -0.3913230895 0.5558624020 0.344858135 - 17.4 -18.8505475 0.9577299112 0.4671596304 0.324978786 - 18 -19.7302351 -0.0050324072 0.2793154325 0.495409834 - 19 -14.6177568 -0.4187468937 -0.1067031159 0.398417477 - 19.1 -17.8043866 -0.4478828944 -0.1047524970 0.449392800 - 19.2 -15.1641705 -1.1966721302 0.1964187590 0.522886360 - 19.3 -16.6898418 -0.5877091668 0.3222225327 0.340847993 - 20 -12.9059229 0.6838223064 -0.0177612481 0.549715454 - 20.1 -16.8191201 0.3278571109 -0.0062817988 0.551756621 - 20.2 -6.1010131 -0.8489831990 0.1925456183 0.524021244 - 20.3 -7.9415371 1.3169975191 0.1968825793 0.522749488 - 20.4 -9.3904458 0.0444804531 0.5508408484 0.332148944 - 20.5 -13.3504189 -0.4535207652 0.3410795409 0.338224725 - 21 -7.6974718 -0.4030302960 -0.0242133322 0.548168935 - 21.1 -11.9335526 -0.4069674045 0.4894906279 0.399187414 - 21.2 -12.7064929 1.0650265940 0.4925855178 0.324061287 - 22 -21.5022909 -0.0673274516 0.2942488077 0.489859980 - 22.1 -12.7745451 0.9601388170 0.5385758261 0.365790145 - 23 -3.5146508 0.5556634840 -0.0596896592 0.532269527 - 23.1 -4.6724048 1.4407865964 0.2323619738 0.511693737 - 24 -2.5619821 0.3856376411 0.4463109938 0.422857517 - 25 -6.2944970 0.3564400705 -0.0764769279 0.235036737 - 25.1 -3.8630505 0.0982553434 -0.0457828435 0.540323722 - 25.2 -14.4205140 0.1928682598 -0.0390131484 0.543297023 - 25.3 -19.6735037 -0.0192488594 0.5517873130 0.352345378 - 25.4 -9.0288933 0.4466012931 0.3851356619 0.332578168 - 25.5 -9.0509738 1.1425193342 0.3492871599 0.337117898 - 26 -19.7340685 0.5341531449 -0.0370005446 0.107676329 - 26.1 -14.1692728 1.2268695927 0.2829160492 0.494087736 - 26.2 -17.2819976 0.3678294939 0.5366821080 0.326994020 - 26.3 -24.6265576 0.5948516018 0.3366364198 0.338833066 - 27 -7.3354999 -0.3342844147 0.5171168233 0.324565129 - 27.1 -11.1488468 -0.4835141229 0.3051619029 0.343307911 - 28 -11.7996597 -0.7145915499 -0.0805110543 0.249962259 - 28.1 -8.2030122 0.5063671955 0.4336613409 0.429273232 - 28.2 -26.4317815 -0.2067413142 0.3225075315 0.340807567 - 28.3 -18.5016071 0.1196789973 0.1812274493 0.362944907 - 29 -5.8551395 0.1392699487 -0.1027419307 0.461066695 - 29.1 -2.0209442 0.7960234776 -0.0044221127 0.552010523 - 29.2 -5.6368080 1.0398214352 0.0902449711 0.547810299 - 29.3 -3.8110961 0.0813246429 0.5559364351 0.338052723 - 30 -12.7217702 -0.3296323050 0.1981005081 0.522389104 - 30.1 -17.0170140 1.3635850954 0.5375291120 0.327176583 - 30.2 -25.4236089 0.7354171050 0.5340597966 0.326484504 - 31 -17.0783921 0.3708398217 0.2223657818 0.356147774 - 32 -18.4338764 -0.0474059668 -0.0292943991 0.546719898 - 32.1 -19.4317212 1.2507771489 0.4755751076 0.407167901 - 32.2 -19.4738978 0.1142915519 0.4926434114 0.397320853 - 32.3 -21.4922645 0.6773270619 0.3229405911 0.340746183 - 33 2.0838099 0.1774293842 -0.0013701847 0.003936563 - 33.1 -13.3172274 0.6159606291 0.5534368438 0.334224366 - 34 -10.0296691 0.8590979166 -0.0674871387 0.526248154 - 34.1 -25.9426553 0.0546216775 0.4818424185 0.324316326 - 34.2 -18.5688138 -0.0897224473 0.4539624195 0.325817315 - 34.3 -15.4173859 0.4163395571 0.4349720689 0.327338410 - 35 -14.3958113 -1.4693520528 -0.0916157074 0.494613531 - 35.1 -12.9457541 -0.3031734330 -0.0623564831 0.530354088 - 35.2 -16.1380691 -0.6045512101 0.1624134259 0.366122990 - 36 -12.8166968 0.9823048960 -0.0935638890 0.304360595 - 36.1 -14.3989481 1.4466051416 0.0065488640 0.553123835 - 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83 -12.5872635 0.5104724530 -0.0083163054 0.023907290 - 83.1 -11.9756502 -0.0513309106 0.4733943484 0.408384331 - 83.2 -10.6744217 -0.2067792494 0.5304919537 0.372272641 - 83.3 -19.2714012 -0.0534169155 -0.0004497824 0.394862487 - 84 -2.6320312 -0.0255753653 0.4065375903 0.442422222 - 84.1 -9.8140094 -1.8234189877 0.5559689912 0.344500819 - 85 -12.3886736 -0.0114038622 -0.0452387941 0.132558080 - 85.1 -12.9196365 -0.0577615939 -0.0113130186 0.550966098 - 85.2 -9.6433248 -0.2241856342 0.3897280300 0.450202261 - 85.3 -6.3296340 -0.0520175929 0.5382727768 0.366047208 - 85.4 -7.0405525 0.2892733846 0.4399457097 0.326908671 - 85.5 -13.6714939 -0.3740417009 0.1584688915 0.366794208 - 86 -10.8756412 0.4293735089 -0.1024825180 0.354361579 - 86.1 -12.0055331 -0.1363456521 -0.1070306834 0.409486154 - 86.2 -13.3724699 0.1230989293 -0.0987571246 0.476581575 - 86.3 -13.3252145 0.3305413955 0.1629165477 0.532204554 - 86.4 -14.9191290 2.6003411822 0.2866858473 0.492692574 - 86.5 -17.7515546 -0.1420690052 0.4829861649 0.324278938 - 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100.2 -0.347560939 2.1502332564 - 100.3 0.158668905 3.8745574222 - 100.4 0.547483173 4.6567608765 - - $m7e$spM_id - center scale - C2 -0.6240921 0.68571078 - (Intercept) NA NA - C1 0.7372814 0.01472882 - - $m7e$spM_lvlone - center scale - y -11.17337099 6.2496619 - c1 0.25599956 0.6718095 - ns(time, df = 3)1 0.19883694 0.2502686 - ns(time, df = 3)2 0.38513689 0.1171115 - ns(time, df = 3)3 -0.07137294 0.2891059 - time 2.53394028 1.3818094 - - $m7e$mu_reg_norm - [1] 0 - - $m7e$tau_reg_norm - [1] 1e-04 - - $m7e$shape_tau_norm - [1] 0.01 - - $m7e$rate_tau_norm - [1] 0.01 - - $m7e$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m7e$shape_diag_RinvD - [1] "0.01" - - $m7e$rate_diag_RinvD - [1] "0.001" - - $m7e$RinvD_y_id - [,1] [,2] [,3] [,4] - [1,] NA 0 0 0 - [2,] 0 NA 0 0 - [3,] 0 0 NA 0 - [4,] 0 0 0 NA - - $m7e$KinvD_y_id - id - 5 - - - $m7f - $m7f$M_id - C2 (Intercept) C1 - 1 -1.381594459 1 0.7175865 - 2 0.344426024 1 0.7507170 - 3 NA 1 0.7255954 - 4 -0.228910007 1 0.7469352 - 5 NA 1 0.7139120 - 6 -2.143955482 1 0.7332505 - 7 -1.156567023 1 0.7345929 - 8 -0.598827660 1 0.7652589 - 9 NA 1 0.7200622 - 10 -1.006719032 1 0.7423879 - 11 0.239801450 1 0.7437448 - 12 -1.064969789 1 0.7446470 - 13 -0.538082688 1 0.7530186 - 14 NA 1 0.7093137 - 15 -1.781049276 1 0.7331192 - 16 NA 1 0.7011390 - 17 NA 1 0.7432395 - 18 -0.014579883 1 0.7545191 - 19 -2.121550136 1 0.7528487 - 20 NA 1 0.7612865 - 21 -0.363239698 1 0.7251719 - 22 -0.121568514 1 0.7300630 - 23 -0.951271111 1 0.7087249 - 24 NA 1 0.7391938 - 25 -0.974288621 1 0.7820641 - 26 -1.130632418 1 0.7118298 - 27 0.114339868 1 0.7230857 - 28 0.238334648 1 0.7489353 - 29 0.840744958 1 0.7510888 - 30 NA 1 0.7300717 - 31 NA 1 0.7550721 - 32 -1.466312154 1 0.7321898 - 33 -0.637352277 1 0.7306414 - 34 NA 1 0.7427216 - 35 NA 1 0.7193042 - 36 NA 1 0.7312888 - 37 NA 1 0.7100436 - 38 NA 1 0.7670184 - 39 0.006728205 1 0.7400449 - 40 NA 1 0.7397304 - 41 -1.663281353 1 0.7490966 - 42 0.161184794 1 0.7419274 - 43 0.457939180 1 0.7527810 - 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time 2.53394028 1.3818094 - ns(time, df = 3)1 0.19883694 0.2502686 - ns(time, df = 3)2 0.38513689 0.1171115 - ns(time, df = 3)3 -0.07137294 0.2891059 - - $m7f$mu_reg_norm - [1] 0 - - $m7f$tau_reg_norm - [1] 1e-04 - - $m7f$shape_tau_norm - [1] 0.01 - - $m7f$rate_tau_norm - [1] 0.01 - - $m7f$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m7f$shape_diag_RinvD - [1] "0.01" - - $m7f$rate_diag_RinvD - [1] "0.001" - - $m7f$RinvD_y_id - [,1] [,2] - [1,] NA 0 - [2,] 0 NA - - $m7f$KinvD_y_id - id - 3 - - - $m8a - $m8a$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - - $m8a$M_lvlone - y c2 c1 time - 1 -13.0493856 NA 0.7592026489 0.5090421822 - 1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 - 1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 - 1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 - 2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 - 2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 - 2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 - 3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 - 3.1 -10.5983220 NA 0.3788637747 3.0654308549 - 3.2 -22.4519157 NA 0.9872578281 4.7381553578 - 4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 - 4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 - 4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 - 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16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 - 16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 - 16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 - 17 -10.5474144 NA 0.6048676222 1.5925356350 - 17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 - 17.2 -16.5386939 NA 1.2617499032 3.0256489082 - 17.3 -20.0004774 NA -0.3913230895 3.3329089405 - 17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 - 18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 - 19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 - 19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 - 19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 - 19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 - 20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 - 20.1 -16.8191201 NA 0.3278571109 1.7562902288 - 20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 - 20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 - 20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 - 20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 - 21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 - 21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 - 21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 - 22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 - 22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 - 23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 - 23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 - 24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 - 25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 - 25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 - 25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 - 25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 - 25.4 -9.0288933 NA 0.4466012931 4.0782226040 - 25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 - 26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 - 26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 - 26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 - 26.3 -24.6265576 NA 0.5948516018 4.1822362854 - 27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 - 27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 - 28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 - 28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 - 28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 - 28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 - 29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 - 29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 - 29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 - 29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 - 30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 - 30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 - 30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 - 31 -17.0783921 0.05707733 0.3708398217 4.3988140998 - 32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 - 32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 - 32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 - 32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 - 33 2.0838099 -0.15252819 0.1774293842 0.0093385763 - 33.1 -13.3172274 NA 0.6159606291 3.4591242753 - 34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 - 34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 - 34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 - 34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 - 35 -14.3958113 NA -1.4693520528 1.3294299203 - 35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 - 35.2 -16.1380691 NA -0.6045512101 4.5025920868 - 36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 - 36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 - 36.2 -12.2436943 NA 1.1606752905 1.8262697803 - 36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 - 36.4 -10.1775457 NA 0.2640591685 4.6194464504 - 37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 - 37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 - 37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 - 38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 - 39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 - 39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 - 39.2 -18.2695344 NA -1.7909380751 1.3055654289 - 39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 - 39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 - 39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 - 40 1.8291916 -0.19751956 0.5350190436 1.1330395646 - 40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 - 40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 - 40.3 -10.5749790 NA 0.0916687506 4.6762977762 - 41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 - 41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 - 41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 - 41.3 -15.9788960 NA 0.9885613862 3.3813529415 - 41.4 -9.6070508 NA -0.3908036794 3.5482964432 - 42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 - 42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 - 43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 - 43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 - 43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 - 44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 - 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[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m8a$shape_diag_RinvD - [1] "0.01" - - $m8a$rate_diag_RinvD - [1] "0.001" - - $m8a$RinvD_y_id - [,1] [,2] [,3] - [1,] NA 0 0 - [2,] 0 NA 0 - [3,] 0 0 NA - - $m8a$KinvD_y_id - id - 4 - - - $m8b - $m8b$M_id - (Intercept) - 1 1 - 2 1 - 3 1 - 4 1 - 5 1 - 6 1 - 7 1 - 8 1 - 9 1 - 10 1 - 11 1 - 12 1 - 13 1 - 14 1 - 15 1 - 16 1 - 17 1 - 18 1 - 19 1 - 20 1 - 21 1 - 22 1 - 23 1 - 24 1 - 25 1 - 26 1 - 27 1 - 28 1 - 29 1 - 30 1 - 31 1 - 32 1 - 33 1 - 34 1 - 35 1 - 36 1 - 37 1 - 38 1 - 39 1 - 40 1 - 41 1 - 42 1 - 43 1 - 44 1 - 45 1 - 46 1 - 47 1 - 48 1 - 49 1 - 50 1 - 51 1 - 52 1 - 53 1 - 54 1 - 55 1 - 56 1 - 57 1 - 58 1 - 59 1 - 60 1 - 61 1 - 62 1 - 63 1 - 64 1 - 65 1 - 66 1 - 67 1 - 68 1 - 69 1 - 70 1 - 71 1 - 72 1 - 73 1 - 74 1 - 75 1 - 76 1 - 77 1 - 78 1 - 79 1 - 80 1 - 81 1 - 82 1 - 83 1 - 84 1 - 85 1 - 86 1 - 87 1 - 88 1 - 89 1 - 90 1 - 91 1 - 92 1 - 93 1 - 94 1 - 95 1 - 96 1 - 97 1 - 98 1 - 99 1 - 100 1 - 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40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 - 40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 - 40.3 -10.5749790 NA 0.0916687506 4.6762977762 - 41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 - 41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 - 41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 - 41.3 -15.9788960 NA 0.9885613862 3.3813529415 - 41.4 -9.6070508 NA -0.3908036794 3.5482964432 - 42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 - 42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 - 43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 - 43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 - 43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 - 44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 - 44.1 -19.9270197 NA 0.3416426284 2.6490291790 - 44.2 -22.6521171 NA 0.5706610068 3.3371910988 - 44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 - 45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 - 45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 - 46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 - 46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 - 46.2 -15.8222682 NA 0.1248719493 4.4160729996 - 47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 - 47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 - 47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 - 47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 - 47.4 -18.2214784 NA 0.3702301552 4.6201055450 - 48 -8.3101471 NA -0.2128602862 2.8607365978 - 48.1 -18.3854275 NA -0.5337239976 2.9098354396 - 49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 - 50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 - 51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 - 52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 - 52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 - 52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 - 52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 - 52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 - 52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 - 53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 - 53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 - 53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 - 54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 - 54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 - 54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 - 54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 - 54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 - 55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 - 55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 - 55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 - 55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 - 55.4 -14.8599983 NA -0.1202746964 3.2218102901 - 56 -14.1764282 NA 0.5176377612 1.2231332215 - 56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 - 56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 - 56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 - 56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 - 56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 - 57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 - 57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 - 57.2 -6.3940143 NA -0.1159517011 1.1405586067 - 57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 - 58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 - 58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 - 58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 - 58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 - 58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 - 58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 - 59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 - 59.1 -10.0530648 NA 1.0028089765 4.9736620474 - 60 -19.2209402 NA 0.5231452932 2.8854503250 - 61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 - 61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 - 61.2 -1.4713611 NA 1.0229058138 2.5077313243 - 61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 - 61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 - 62 -2.8591600 NA -0.3979137604 0.5336771999 - 62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 - 62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 - 62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 - 63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 - 63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 - 64 -20.5963897 NA 0.3949911859 4.1191305732 - 65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 - 65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 - 65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 - 65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 - 66 -13.2318328 NA -0.5081302805 3.4943454154 - 66.1 -1.9090560 NA -0.1447837412 3.7677437009 - 66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 - 67 -25.6073277 NA 1.6716027681 4.1728388879 - 68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 - 68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 - 68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 - 68.3 -12.4977127 NA 0.7443745962 2.9406870750 - 68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 - 69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 - 70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 - 70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 - 71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 - 71.1 -7.5699888 NA -0.1103252087 1.4883817220 - 71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 - 71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 - 71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 - 72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 - 72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 - 72.2 -26.3421306 NA -0.4170988856 1.8917567329 - 72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 - 72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 - 72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 - 73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 - 74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 - 75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 - 76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 - 76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 - 76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 - 77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 - 78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 - 79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 - 79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 - 79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 - 80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 - 80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 - 80.2 -16.6381566 NA 0.5046816157 2.8025900386 - 81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 - 81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 - 81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 - 81.3 -5.2166381 NA 0.4182528210 3.3456630446 - 82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 - 82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 - 82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 - 83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 - 83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 - 83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 - 83.3 -19.2714012 NA -0.0534169155 4.7663642222 - 84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 - 84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 - 85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 - 85.1 -12.9196365 NA -0.0577615939 1.7394116637 - 85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 - 85.3 -6.3296340 NA -0.0520175929 3.1608762743 - 85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 - 85.5 -13.6714939 NA -0.3740417009 4.5092553482 - 86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 - 86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 - 86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 - 86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 - 86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 - 86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 - 87 -10.7027963 NA 1.0457427869 2.7069531474 - 87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 - 87.2 -14.9616716 NA 0.4396872616 4.5241666853 - 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- $m8j$tau_reg_norm - [1] 1e-04 - - $m8j$shape_tau_norm - [1] 0.01 - - $m8j$rate_tau_norm - [1] 0.01 - - $m8j$mu_reg_binom - [1] 0 - - $m8j$tau_reg_binom - [1] 1e-04 - - $m8j$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - 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25.1 -3.8630505 0.0982553434 1.6069735331 NA NA 1.578937e-01 - 25.2 -14.4205140 0.1928682598 1.6358226922 NA NA 3.154983e-01 - 25.3 -19.6735037 -0.0192488594 3.2646870392 NA NA -6.284150e-02 - 25.4 -9.0288933 0.4466012931 4.0782226040 NA NA 1.821339e+00 - 25.5 -9.0509738 1.1425193342 4.1560292873 NA NA 4.748344e+00 - 26 -19.7340685 0.5341531449 0.2412706357 NA NA 1.288755e-01 - 26.1 -14.1692728 1.2268695927 2.4451737676 NA NA 2.999909e+00 - 26.2 -17.2819976 0.3678294939 3.5988757887 NA NA 1.323773e+00 - 26.3 -24.6265576 0.5948516018 4.1822362854 NA NA 2.487810e+00 - 27 -7.3354999 -0.3342844147 3.6955824879 NA NA -1.235376e+00 - 27.1 -11.1488468 -0.4835141229 4.2451434687 NA NA -2.052587e+00 - 28 -11.7996597 -0.7145915499 0.5746519344 NA NA -4.106414e-01 - 28.1 -8.2030122 0.5063671955 2.7943964268 NA NA 1.414991e+00 - 28.2 -26.4317815 -0.2067413142 4.2108539480 NA NA -8.705575e-01 - 28.3 -18.5016071 0.1196789973 4.4705521734 NA NA 5.350312e-01 - 29 -5.8551395 0.1392699487 1.1898884235 NA NA 1.657157e-01 - 29.1 -2.0209442 0.7960234776 1.7624059319 NA NA 1.402916e+00 - 29.2 -5.6368080 1.0398214352 2.0210406382 NA NA 2.101521e+00 - 29.3 -3.8110961 0.0813246429 3.4078777023 NA NA 2.771444e-01 - 30 -12.7217702 -0.3296323050 2.2635366488 NA NA -7.461348e-01 - 30.1 -17.0170140 1.3635850954 3.5938334477 NA NA 4.900498e+00 - 30.2 -25.4236089 0.7354171050 3.6138710892 NA NA 2.657703e+00 - 31 -17.0783921 0.3708398217 4.3988140998 NA NA 1.631255e+00 - 32 -18.4338764 -0.0474059668 1.6745209007 NA NA -7.938228e-02 - 32.1 -19.4317212 1.2507771489 2.9128167813 NA NA 3.643285e+00 - 32.2 -19.4738978 0.1142915519 2.9676558380 NA NA 3.391780e-01 - 32.3 -21.4922645 0.6773270619 4.2099863547 NA NA 2.851538e+00 - 33 2.0838099 0.1774293842 0.0093385763 NA NA 1.656938e-03 - 33.1 -13.3172274 0.6159606291 3.4591242753 NA NA 2.130684e+00 - 34 -10.0296691 0.8590979166 1.4998774312 NA NA 1.288542e+00 - 34.1 -25.9426553 0.0546216775 3.8242761395 NA NA 2.088884e-01 - 34.2 -18.5688138 -0.0897224473 3.9072251692 NA NA -3.505658e-01 - 34.3 -15.4173859 0.4163395571 3.9582124643 NA NA 1.647960e+00 - 35 -14.3958113 -1.4693520528 1.3294299203 NA NA -1.953401e+00 - 35.1 -12.9457541 -0.3031734330 1.5276966314 NA NA -4.631570e-01 - 35.2 -16.1380691 -0.6045512101 4.5025920868 NA NA -2.722047e+00 - 36 -12.8166968 0.9823048960 0.7123168337 NA NA 6.997123e-01 - 36.1 -14.3989481 1.4466051416 1.7972493160 NA NA 2.599910e+00 - 36.2 -12.2436943 1.1606752905 1.8262697803 NA NA 2.119706e+00 - 36.3 -15.0104638 0.8373091576 4.2840119381 NA NA 3.587042e+00 - 36.4 -10.1775457 0.2640591685 4.6194464504 NA NA 1.219807e+00 - 37 -15.2223495 0.1177313455 2.0018732361 NA NA 2.356832e-01 - 37.1 -14.7526195 -0.1415483779 3.6656836793 NA NA -5.188716e-01 - 37.2 -19.8168430 0.0054610124 3.9663937816 NA NA 2.166053e-02 - 38 -2.7065118 0.8078948077 0.9826511063 NA NA 7.938787e-01 - 39 -8.7288138 0.9876451040 0.6921808305 NA NA 6.836290e-01 - 39.1 -9.2746473 -0.3431222274 0.9027792048 NA NA -3.097636e-01 - 39.2 -18.2695344 -1.7909380751 1.3055654289 NA NA -2.338187e+00 - 39.3 -13.8219083 -0.1798746191 1.5412842878 NA NA -2.772379e-01 - 39.4 -16.2254704 -0.1850961689 3.1834997435 NA NA -5.892536e-01 - 39.5 -21.7283648 0.4544226146 4.1394166439 NA NA 1.881045e+00 - 40 1.8291916 0.5350190436 1.1330395646 NA NA 6.061977e-01 - 40.1 -6.6916432 0.4189342752 2.6940994046 NA NA 1.128651e+00 - 40.2 -1.6278171 0.4211994981 3.0396614212 NA NA 1.280304e+00 - 40.3 -10.5749790 0.0916687506 4.6762977762 NA NA 4.286704e-01 - 41 -3.1556121 -0.1035047421 1.9337158254 NA NA -2.001488e-01 - 41.1 -11.5895327 -0.4684202411 3.1956304458 NA NA -1.496898e+00 - 41.2 -18.9352091 0.5972615368 3.2846923557 NA NA 1.961820e+00 - 41.3 -15.9788960 0.9885613862 3.3813529415 NA NA 3.342675e+00 - 41.4 -9.6070508 -0.3908036794 3.5482964432 NA NA -1.386687e+00 - 42 -5.2159485 -0.0338893961 0.4859252973 NA NA -1.646771e-02 - 42.1 -15.9878743 -0.4498363172 4.3293134298 NA NA -1.947482e+00 - 43 -16.6104361 0.8965546110 0.5616614548 NA NA 5.035602e-01 - 43.1 -9.5549441 0.6199122090 1.0743579536 NA NA 6.660076e-01 - 43.2 -14.2003491 0.1804894429 2.6131797966 NA NA 4.716514e-01 - 44 -8.1969033 1.3221409285 0.7662644819 NA NA 1.013110e+00 - 44.1 -19.9270197 0.3416426284 2.6490291790 NA NA 9.050213e-01 - 44.2 -22.6521171 0.5706610068 3.3371910988 NA NA 1.904405e+00 - 44.3 -21.1903736 1.2679497430 4.1154200875 NA NA 5.218146e+00 - 45 -0.5686627 0.1414983160 0.1957449992 NA NA 2.769759e-02 - 45.1 -7.5645740 0.7220892521 1.9963831536 NA NA 1.441567e+00 - 46 -19.1624789 1.5391054233 1.3477755385 NA NA 2.074369e+00 - 46.1 -18.4487574 0.3889107049 2.8565793915 NA NA 1.110954e+00 - 46.2 -15.8222682 0.1248719493 4.4160729996 NA NA 5.514436e-01 - 47 -5.4165074 0.2014101100 0.6012621359 NA NA 1.211003e-01 - 47.1 -15.0975029 0.2982973539 2.4097121472 NA NA 7.188108e-01 - 47.2 -12.9971413 1.1518107179 2.9975794035 NA NA 3.452644e+00 - 47.3 -10.6844521 0.5196802157 3.1829649757 NA NA 1.654124e+00 - 47.4 -18.2214784 0.3702301552 4.6201055450 NA NA 1.710502e+00 - 48 -8.3101471 -0.2128602862 2.8607365978 NA NA -6.089372e-01 - 48.1 -18.3854275 -0.5337239976 2.9098354396 NA NA -1.553049e+00 - 49 -13.0130319 -0.5236770035 2.7179756400 NA NA -1.423341e+00 - 50 -10.4579977 0.3897705981 1.1762060679 NA NA 4.584505e-01 - 51 -19.3157621 -0.7213343736 1.4304436720 NA NA -1.031828e+00 - 52 -4.4747188 0.3758235358 2.1266646020 NA NA 7.992506e-01 - 52.1 -4.3163827 0.7138067080 3.1000545993 NA NA 2.212840e+00 - 52.2 -6.9761408 0.8872895233 3.1268477370 NA NA 2.774419e+00 - 52.3 -20.1764756 -0.9664587437 3.5711459327 NA NA -3.451365e+00 - 52.4 -8.9036692 0.0254566848 4.7983659909 NA NA 1.221505e-01 - 52.5 -5.6949642 0.4155259424 4.9818264414 NA NA 2.070078e+00 - 53 -10.3141887 0.5675736897 0.4965799209 NA NA 2.818457e-01 - 53.1 -8.2642654 -0.3154088781 3.5505357443 NA NA -1.119870e+00 - 53.2 -9.1691554 0.2162315769 4.5790420019 NA NA 9.901335e-01 - 54 -6.2198754 -0.0880802382 1.4034724841 NA NA -1.236182e-01 - 54.1 -15.7192609 0.4129127672 1.8812377600 NA NA 7.767871e-01 - 54.2 -13.0978998 1.0119546775 2.5107589352 NA NA 2.540774e+00 - 54.3 -5.1195299 -0.1112901990 2.7848406672 NA NA -3.099255e-01 - 54.4 -16.5771751 0.8587727145 4.0143877396 NA NA 3.447447e+00 - 55 -5.7348534 -0.0116453589 0.6118522980 NA NA -7.125240e-03 - 55.1 -7.3217494 0.5835528661 0.7463747414 NA NA 4.355491e-01 - 55.2 -12.2171938 -1.0010857254 2.8201208171 NA NA -2.823183e+00 - 55.3 -12.9821266 -0.4796526070 3.1326431572 NA NA -1.502580e+00 - 55.4 -14.8599983 -0.1202746964 3.2218102901 NA NA -3.875023e-01 - 56 -14.1764282 0.5176377612 1.2231332215 NA NA 6.331399e-01 - 56.1 -12.5343602 -1.1136932588 2.3573202139 NA NA -2.625332e+00 - 56.2 -8.4573382 -0.0168103281 2.5674936292 NA NA -4.316041e-02 - 56.3 -12.4633969 0.3933023606 2.9507164378 NA NA 1.160524e+00 - 56.4 -17.3841863 0.3714625139 3.2272730360 NA NA 1.198811e+00 - 56.5 -14.8147645 0.7811448179 3.4175522043 NA NA 2.669603e+00 - 57 -3.1403293 -1.0868304872 0.2370331448 NA NA -2.576148e-01 - 57.1 -11.1509248 0.8018626997 0.2481445030 NA NA 1.989778e-01 - 57.2 -6.3940143 -0.1159517011 1.1405586067 NA NA -1.322497e-01 - 57.3 -9.3473241 0.6785562445 2.1153886721 NA NA 1.435410e+00 - 58 -12.0245677 1.6476207996 1.2210099772 NA NA 2.011761e+00 - 58.1 -9.2112246 0.3402652711 1.6334245703 NA NA 5.557977e-01 - 58.2 -1.2071742 -0.1111300753 1.6791862890 NA NA -1.866081e-01 - 58.3 -11.0141711 -0.5409234285 2.6320121693 NA NA -1.423717e+00 - 58.4 -5.3721214 -0.1271327672 2.8477731440 NA NA -3.620453e-01 - 58.5 -7.8523047 0.8713264822 3.5715569824 NA NA 3.111992e+00 - 59 -13.2946560 0.4766421367 1.9023998594 NA NA 9.067639e-01 - 59.1 -10.0530648 1.0028089765 4.9736620474 NA NA 4.987633e+00 - 60 -19.2209402 0.5231452932 2.8854503250 NA NA 1.509510e+00 - 61 -4.6699914 -0.7190130614 0.7213630795 NA NA -5.186695e-01 - 61.1 -3.5981894 0.8353702312 2.3186947661 NA NA 1.936969e+00 - 61.2 -1.4713611 1.0229058138 2.5077313243 NA NA 2.565173e+00 - 61.3 -3.8819786 1.1717723589 3.1731073430 NA NA 3.718159e+00 - 61.4 0.1041413 -0.0629201596 3.6022726283 NA NA -2.266556e-01 - 62 -2.8591600 -0.3979137604 0.5336771999 NA NA -2.123575e-01 - 62.1 -6.9461986 0.6830738372 0.6987666548 NA NA 4.773092e-01 - 62.2 -16.7910593 0.4301745954 3.4584309917 NA NA 1.487729e+00 - 62.3 -17.9844596 -0.0333139957 4.8028772371 NA NA -1.600030e-01 - 63 -24.0335535 0.3345678035 2.8097350930 NA NA 9.400469e-01 - 63.1 -11.7765300 0.3643769511 3.9653754211 NA NA 1.444891e+00 - 64 -20.5963897 0.3949911859 4.1191305732 NA NA 1.627020e+00 - 65 -2.7969169 1.2000091513 0.7076152589 NA NA 8.491448e-01 - 65.1 -11.1778694 0.0110122646 2.0252246363 NA NA 2.230231e-02 - 65.2 -5.2830399 -0.5776452043 3.1127382827 NA NA -1.798058e+00 - 65.3 -7.9353390 -0.1372183563 3.1969087943 NA NA -4.386746e-01 - 66 -13.2318328 -0.5081302805 3.4943454154 NA NA -1.775583e+00 - 66.1 -1.9090560 -0.1447837412 3.7677437009 NA NA -5.455080e-01 - 66.2 -16.6643889 0.1906241379 3.9486138616 NA NA 7.527011e-01 - 67 -25.6073277 1.6716027681 4.1728388879 NA NA 6.975329e+00 - 68 -13.4806759 0.5691848839 0.1291919907 NA NA 7.353413e-02 - 68.1 -18.4557183 0.1004860389 1.7809643946 NA NA 1.789621e-01 - 68.2 -13.3982327 -0.0061241827 2.0493205660 NA NA -1.255041e-02 - 68.3 -12.4977127 0.7443745962 2.9406870750 NA NA 2.188973e+00 - 68.4 -11.7073990 0.8726923437 4.0406670363 NA NA 3.526259e+00 - 69 -14.5290675 0.0381382683 4.1451198701 NA NA 1.580877e-01 - 70 -15.2122709 0.8126204217 0.1992557163 NA NA 1.619193e-01 - 70.1 -7.8681167 0.4691503050 0.4829774413 NA NA 2.265890e-01 - 71 -10.3352703 -0.5529062591 0.7741605386 NA NA -4.280382e-01 - 71.1 -7.5699888 -0.1103252087 1.4883817220 NA NA -1.642060e-01 - 71.2 -18.4680702 1.7178492547 4.0758526395 NA NA 7.001700e+00 - 71.3 -21.4316644 -1.0118346755 4.7048238723 NA NA -4.760504e+00 - 71.4 -8.1137650 1.8623785017 4.7242791823 NA NA 8.798396e+00 - 72 -9.1848162 -0.4521659275 0.9321196121 NA NA -4.214727e-01 - 72.1 -23.7538846 0.1375317317 1.1799991806 NA NA 1.622873e-01 - 72.2 -26.3421306 -0.4170988856 1.8917567329 NA NA -7.890496e-01 - 72.3 -27.2843801 0.7107266765 3.4853593935 NA NA 2.477138e+00 - 72.4 -20.8541617 0.1451969143 3.6884259700 NA NA 5.355481e-01 - 72.5 -12.8948965 1.6298050306 4.0854155901 NA NA 6.658431e+00 - 73 -2.6091307 -0.0307469467 4.6019889915 NA NA -1.414971e-01 - 74 -8.2790175 0.3730017941 1.4626806753 NA NA 5.455825e-01 - 75 -12.5029612 -0.4908003566 3.2524286874 NA NA -1.596293e+00 - 76 -6.0061671 -0.9888876620 1.8074807397 NA NA -1.787395e+00 - 76.1 -8.8149114 0.0003798292 4.2685073183 NA NA 1.621304e-03 - 76.2 -11.8359043 -0.8421863763 4.9688734859 NA NA -4.184718e+00 - 77 0.4772521 -0.4986802480 0.8459033852 NA NA -4.218353e-01 - 78 -9.4105229 0.0417330969 0.8231094317 NA NA 3.435091e-02 - 79 -1.0217265 -0.3767450660 0.0583819521 NA NA -2.199511e-02 - 79.1 -11.8125257 0.1516000028 2.4406372628 NA NA 3.700006e-01 - 79.2 -10.5465186 -0.1888160741 3.2962526032 NA NA -6.223855e-01 - 80 -12.7366807 -0.0041558414 0.8985060186 NA NA -3.734049e-03 - 80.1 -9.0584783 -0.0329337062 1.3434670598 NA NA -4.424535e-02 - 80.2 -16.6381566 0.5046816157 2.8025900386 NA NA 1.414416e+00 - 81 0.5547913 -0.9493950353 0.0101324962 NA NA -9.619742e-03 - 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[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m9a$group_o1 - [1] 1 2 2 1 3 2 1 2 3 1 3 1 2 1 1 1 3 1 3 3 2 2 1 1 3 3 1 1 1 1 3 1 2 1 3 3 2 - [38] 3 2 1 2 3 3 1 2 3 3 1 1 3 2 3 2 2 1 1 1 3 1 3 1 3 1 1 1 2 1 1 2 2 1 1 1 1 - [75] 2 2 2 1 2 1 2 2 2 1 1 1 3 1 2 1 1 2 2 2 2 1 1 2 1 1 1 1 1 3 2 3 3 1 1 3 1 - [112] 1 2 1 3 1 3 1 3 3 3 3 3 3 1 2 1 1 2 2 2 3 2 2 1 3 1 2 2 3 1 2 1 1 2 3 1 2 - [149] 2 2 2 2 1 3 2 2 2 3 1 3 1 2 1 1 1 3 1 2 3 1 1 2 2 2 1 3 2 1 2 2 1 2 2 3 1 - [186] 3 3 2 3 1 1 1 1 3 1 3 3 1 3 3 2 3 2 1 1 2 1 1 3 2 1 2 3 1 2 1 2 2 2 2 3 3 - [223] 2 1 1 1 1 1 2 3 3 3 3 1 1 2 2 1 1 2 2 2 2 3 3 3 2 1 1 2 1 2 1 1 3 3 1 3 3 - [260] 3 1 1 3 3 3 1 3 3 3 1 1 1 2 3 1 3 2 2 2 3 2 2 2 3 2 2 2 2 1 2 3 2 2 1 2 1 - [297] 2 3 2 3 2 1 3 2 3 1 3 3 2 1 2 3 2 2 1 3 3 3 3 2 2 1 1 2 2 1 1 2 2 - - $m9a$shape_diag_RinvD - [1] "0.01" - - $m9a$rate_diag_RinvD - [1] "0.001" - - - $m9b - $m9b$M_id - C2 (Intercept) C1 B11 - 1 -1.381594459 1 0.7175865 1 - 2 0.344426024 1 0.7507170 1 - 3 NA 1 0.7255954 1 - 4 -0.228910007 1 0.7469352 0 - 5 NA 1 0.7139120 1 - 6 -2.143955482 1 0.7332505 1 - 7 -1.156567023 1 0.7345929 1 - 8 -0.598827660 1 0.7652589 0 - 9 NA 1 0.7200622 1 - 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[1] "0.001" - - $m9b$RinvD_y_id - [,1] [,2] - [1,] NA 0 - [2,] 0 NA - - $m9b$KinvD_y_id - id - 3 - - - $m9c - $m9c$M_id - C2 (Intercept) C1 B11 - 1 -1.381594459 1 0.7175865 1 - 2 0.344426024 1 0.7507170 1 - 3 NA 1 0.7255954 1 - 4 -0.228910007 1 0.7469352 0 - 5 NA 1 0.7139120 1 - 6 -2.143955482 1 0.7332505 1 - 7 -1.156567023 1 0.7345929 1 - 8 -0.598827660 1 0.7652589 0 - 9 NA 1 0.7200622 1 - 10 -1.006719032 1 0.7423879 1 - 11 0.239801450 1 0.7437448 0 - 12 -1.064969789 1 0.7446470 1 - 13 -0.538082688 1 0.7530186 1 - 14 NA 1 0.7093137 1 - 15 -1.781049276 1 0.7331192 1 - 16 NA 1 0.7011390 1 - 17 NA 1 0.7432395 1 - 18 -0.014579883 1 0.7545191 1 - 19 -2.121550136 1 0.7528487 1 - 20 NA 1 0.7612865 0 - 21 -0.363239698 1 0.7251719 1 - 22 -0.121568514 1 0.7300630 1 - 23 -0.951271111 1 0.7087249 1 - 24 NA 1 0.7391938 0 - 25 -0.974288621 1 0.7820641 1 - 26 -1.130632418 1 0.7118298 1 - 27 0.114339868 1 0.7230857 0 - 28 0.238334648 1 0.7489353 1 - 29 0.840744958 1 0.7510888 1 - 30 NA 1 0.7300717 1 - 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68 NA 1 0.7482736 1 - 69 -0.405642769 1 0.7338267 1 - 70 NA 1 0.7607742 1 - 71 -0.843748427 1 0.7777600 1 - 72 0.116003683 1 0.7408143 1 - 73 -0.778634325 1 0.7248271 1 - 74 NA 1 0.7364916 0 - 75 NA 1 0.7464926 1 - 76 NA 1 0.7355430 1 - 77 -0.632974758 1 0.7208449 1 - 78 NA 1 0.7373573 1 - 79 -0.778064615 1 0.7598079 1 - 80 NA 1 0.7360415 1 - 81 NA 1 0.7293932 1 - 82 -0.246123253 1 0.7279309 1 - 83 -1.239659782 1 0.7344643 0 - 84 -0.467772280 1 0.7384350 0 - 85 NA 1 0.7323716 1 - 86 -2.160485036 1 0.7576597 1 - 87 -0.657675572 1 0.7496139 1 - 88 NA 1 0.7275239 1 - 89 -0.696710744 1 0.7250648 1 - 90 NA 1 0.7335262 0 - 91 -0.179395847 1 0.7343980 1 - 92 -0.441545568 1 0.7380425 1 - 93 -0.685799334 1 0.7389460 0 - 94 NA 1 0.7259951 1 - 95 0.191929445 1 0.7282840 0 - 96 NA 1 0.7281676 0 - 97 -0.069760671 1 0.7245642 1 - 98 NA 1 0.7526938 1 - 99 NA 1 0.7272309 1 - 100 NA 1 0.7383460 1 - - $m9c$M_lvlone - y - 1 -13.0493856 - 1.1 -9.3335901 - 1.2 -22.3469852 - 1.3 -15.0417337 - 2 -12.0655434 - 2.1 -15.8674476 - 2.2 -7.8800006 - 3 -11.4820604 - 3.1 -10.5983220 - 3.2 -22.4519157 - 4 -1.2697775 - 4.1 -11.1215184 - 4.2 -3.6134138 - 4.3 -14.5982385 - 5 -6.8457515 - 5.1 -7.0551214 - 5.2 -12.3418980 - 5.3 -9.2366906 - 6 -5.1648211 - 7 -10.0599502 - 7.1 -18.3267285 - 7.2 -12.5138426 - 8 -1.6305331 - 8.1 -9.6520453 - 8.2 -1.5278462 - 8.3 -7.4172211 - 8.4 -7.1238609 - 8.5 -8.8706950 - 9 -0.1634429 - 9.1 -2.6034300 - 9.2 -6.7272369 - 10 -6.4172202 - 10.1 -11.4834569 - 11 -8.7911356 - 11.1 -19.6645080 - 11.2 -20.2030932 - 11.3 -21.3082176 - 11.4 -14.5802901 - 12 -15.2006287 - 13 0.8058816 - 13.1 -13.6379208 - 14 -15.3422873 - 14.1 -10.0965208 - 14.2 -16.6452027 - 14.3 -15.8389733 - 15 -8.9424594 - 15.1 -22.0101983 - 15.2 -7.3975599 - 15.3 -10.3567334 - 16 -1.9691302 - 16.1 -9.9308357 - 16.2 -6.9626923 - 16.3 -3.2862557 - 16.4 -3.3972355 - 16.5 -11.5767835 - 17 -10.5474144 - 17.1 -7.6215009 - 17.2 -16.5386939 - 17.3 -20.0004774 - 17.4 -18.8505475 - 18 -19.7302351 - 19 -14.6177568 - 19.1 -17.8043866 - 19.2 -15.1641705 - 19.3 -16.6898418 - 20 -12.9059229 - 20.1 -16.8191201 - 20.2 -6.1010131 - 20.3 -7.9415371 - 20.4 -9.3904458 - 20.5 -13.3504189 - 21 -7.6974718 - 21.1 -11.9335526 - 21.2 -12.7064929 - 22 -21.5022909 - 22.1 -12.7745451 - 23 -3.5146508 - 23.1 -4.6724048 - 24 -2.5619821 - 25 -6.2944970 - 25.1 -3.8630505 - 25.2 -14.4205140 - 25.3 -19.6735037 - 25.4 -9.0288933 - 25.5 -9.0509738 - 26 -19.7340685 - 26.1 -14.1692728 - 26.2 -17.2819976 - 26.3 -24.6265576 - 27 -7.3354999 - 27.1 -11.1488468 - 28 -11.7996597 - 28.1 -8.2030122 - 28.2 -26.4317815 - 28.3 -18.5016071 - 29 -5.8551395 - 29.1 -2.0209442 - 29.2 -5.6368080 - 29.3 -3.8110961 - 30 -12.7217702 - 30.1 -17.0170140 - 30.2 -25.4236089 - 31 -17.0783921 - 32 -18.4338764 - 32.1 -19.4317212 - 32.2 -19.4738978 - 32.3 -21.4922645 - 33 2.0838099 - 33.1 -13.3172274 - 34 -10.0296691 - 34.1 -25.9426553 - 34.2 -18.5688138 - 34.3 -15.4173859 - 35 -14.3958113 - 35.1 -12.9457541 - 35.2 -16.1380691 - 36 -12.8166968 - 36.1 -14.3989481 - 36.2 -12.2436943 - 36.3 -15.0104638 - 36.4 -10.1775457 - 37 -15.2223495 - 37.1 -14.7526195 - 37.2 -19.8168430 - 38 -2.7065118 - 39 -8.7288138 - 39.1 -9.2746473 - 39.2 -18.2695344 - 39.3 -13.8219083 - 39.4 -16.2254704 - 39.5 -21.7283648 - 40 1.8291916 - 40.1 -6.6916432 - 40.2 -1.6278171 - 40.3 -10.5749790 - 41 -3.1556121 - 41.1 -11.5895327 - 41.2 -18.9352091 - 41.3 -15.9788960 - 41.4 -9.6070508 - 42 -5.2159485 - 42.1 -15.9878743 - 43 -16.6104361 - 43.1 -9.5549441 - 43.2 -14.2003491 - 44 -8.1969033 - 44.1 -19.9270197 - 44.2 -22.6521171 - 44.3 -21.1903736 - 45 -0.5686627 - 45.1 -7.5645740 - 46 -19.1624789 - 46.1 -18.4487574 - 46.2 -15.8222682 - 47 -5.4165074 - 47.1 -15.0975029 - 47.2 -12.9971413 - 47.3 -10.6844521 - 47.4 -18.2214784 - 48 -8.3101471 - 48.1 -18.3854275 - 49 -13.0130319 - 50 -10.4579977 - 51 -19.3157621 - 52 -4.4747188 - 52.1 -4.3163827 - 52.2 -6.9761408 - 52.3 -20.1764756 - 52.4 -8.9036692 - 52.5 -5.6949642 - 53 -10.3141887 - 53.1 -8.2642654 - 53.2 -9.1691554 - 54 -6.2198754 - 54.1 -15.7192609 - 54.2 -13.0978998 - 54.3 -5.1195299 - 54.4 -16.5771751 - 55 -5.7348534 - 55.1 -7.3217494 - 55.2 -12.2171938 - 55.3 -12.9821266 - 55.4 -14.8599983 - 56 -14.1764282 - 56.1 -12.5343602 - 56.2 -8.4573382 - 56.3 -12.4633969 - 56.4 -17.3841863 - 56.5 -14.8147645 - 57 -3.1403293 - 57.1 -11.1509248 - 57.2 -6.3940143 - 57.3 -9.3473241 - 58 -12.0245677 - 58.1 -9.2112246 - 58.2 -1.2071742 - 58.3 -11.0141711 - 58.4 -5.3721214 - 58.5 -7.8523047 - 59 -13.2946560 - 59.1 -10.0530648 - 60 -19.2209402 - 61 -4.6699914 - 61.1 -3.5981894 - 61.2 -1.4713611 - 61.3 -3.8819786 - 61.4 0.1041413 - 62 -2.8591600 - 62.1 -6.9461986 - 62.2 -16.7910593 - 62.3 -17.9844596 - 63 -24.0335535 - 63.1 -11.7765300 - 64 -20.5963897 - 65 -2.7969169 - 65.1 -11.1778694 - 65.2 -5.2830399 - 65.3 -7.9353390 - 66 -13.2318328 - 66.1 -1.9090560 - 66.2 -16.6643889 - 67 -25.6073277 - 68 -13.4806759 - 68.1 -18.4557183 - 68.2 -13.3982327 - 68.3 -12.4977127 - 68.4 -11.7073990 - 69 -14.5290675 - 70 -15.2122709 - 70.1 -7.8681167 - 71 -10.3352703 - 71.1 -7.5699888 - 71.2 -18.4680702 - 71.3 -21.4316644 - 71.4 -8.1137650 - 72 -9.1848162 - 72.1 -23.7538846 - 72.2 -26.3421306 - 72.3 -27.2843801 - 72.4 -20.8541617 - 72.5 -12.8948965 - 73 -2.6091307 - 74 -8.2790175 - 75 -12.5029612 - 76 -6.0061671 - 76.1 -8.8149114 - 76.2 -11.8359043 - 77 0.4772521 - 78 -9.4105229 - 79 -1.0217265 - 79.1 -11.8125257 - 79.2 -10.5465186 - 80 -12.7366807 - 80.1 -9.0584783 - 80.2 -16.6381566 - 81 0.5547913 - 81.1 -4.0892715 - 81.2 1.8283303 - 81.3 -5.2166381 - 82 -3.0749381 - 82.1 -10.5506696 - 82.2 -18.2226347 - 83 -12.5872635 - 83.1 -11.9756502 - 83.2 -10.6744217 - 83.3 -19.2714012 - 84 -2.6320312 - 84.1 -9.8140094 - 85 -12.3886736 - 85.1 -12.9196365 - 85.2 -9.6433248 - 85.3 -6.3296340 - 85.4 -7.0405525 - 85.5 -13.6714939 - 86 -10.8756412 - 86.1 -12.0055331 - 86.2 -13.3724699 - 86.3 -13.3252145 - 86.4 -14.9191290 - 86.5 -17.7515546 - 87 -10.7027963 - 87.1 -22.4941954 - 87.2 -14.9616716 - 88 -2.2264493 - 88.1 -8.9626474 - 88.2 -2.5095281 - 88.3 -16.3345673 - 89 -11.0459647 - 90 -4.5610239 - 90.1 -11.7036651 - 90.2 -5.3838521 - 90.3 -4.1636999 - 91 -7.1462503 - 91.1 -12.8374475 - 91.2 -18.2576707 - 92 -6.4119222 - 93 5.2122168 - 93.1 3.1211725 - 93.2 -3.6841177 - 93.3 2.6223542 - 93.4 -11.1877696 - 94 -6.9602492 - 94.1 -7.4318416 - 94.2 -4.3498045 - 94.3 -11.6340088 - 94.4 -12.9357964 - 94.5 -14.7648530 - 95 -12.8849309 - 95.1 -9.7451502 - 95.2 -0.8535063 - 96 -4.9139832 - 96.1 -3.9582653 - 96.2 -9.6555492 - 96.3 -11.8690793 - 96.4 -11.0224373 - 96.5 -10.9530403 - 97 -9.8540471 - 97.1 -19.2262840 - 98 -11.9651231 - 98.1 -2.6515128 - 98.2 -12.2606382 - 99 -11.4720500 - 99.1 -14.0596866 - 99.2 -17.3939469 - 100 1.1005874 - 100.1 -3.8226248 - 100.2 -0.9123182 - 100.3 -15.8389474 - 100.4 -12.8093826 - - $m9c$spM_id - center scale - C2 -0.6240921 0.68571078 - (Intercept) NA NA - C1 0.7372814 0.01472882 - B11 NA NA - - $m9c$mu_reg_norm - [1] 0 - - $m9c$tau_reg_norm - [1] 1e-04 - - $m9c$shape_tau_norm - [1] 0.01 - - $m9c$rate_tau_norm - [1] 0.01 - - $m9c$group_id - [1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5 - [19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11 - [37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16 - [55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21 - [73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27 - [91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33 - [109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39 - [127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43 - [145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49 - [163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55 - [181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58 - [199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65 - [217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71 - [235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79 - [253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85 - [271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89 - [289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94 - [307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99 - [325] 100 100 100 100 100 - - $m9c$shape_diag_RinvD - [1] "0.01" - - $m9c$rate_diag_RinvD - [1] "0.001" - - - -# jagsmodel remains the same - - Code - lapply(models, "[[", "jagsmodel") - Output - $m0a1 - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - } - $m0a2 - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - } - $m0a3 - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - log(mu_y[i]) <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - } - $m0a4 - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- 1/max(1e-10, inv_mu_y[i]) - inv_mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - } - $m0b1 - model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - } - $m0b2 - model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - probit(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - } - $m0b3 - model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - log(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - } - $m0b4 - model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - log(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - } - $m0c1 - model { - - # Gamma mixed effects model for L1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) - inv_mu_L1[i] <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - } - $m0c2 - model { - - # Gamma mixed effects model for L1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - log(mu_L1[i]) <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - } - $m0d1 - model { - - # Poisson mixed effects model for p1 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) - log(mu_p1[i]) <- b_p1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) - mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for p1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) - } - $m0d2 - model { - - # Poisson mixed effects model for p1 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) - mu_p1[i] <- b_p1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) - mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for p1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) - } - $m0e1 - model { - - # Log-normal mixed effects model for L1 ----------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) - mu_L1[i] <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - } - $m0f1 - model { - - # Beta mixed effects model for Be1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) - - shape1_Be1[i] <- mu_Be1[i] * tau_Be1 - shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 - - logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ]) - mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for Be1 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1]) - } - $m1a - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for y - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - } - $m1b - model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for b1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - } - $m1c - model { - - # Gamma mixed effects model for L1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i]) - - shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2) - rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2) - - mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i]) - inv_mu_L1[i] <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for L1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - } - $m1d - model { - - # Poisson mixed effects model for p1 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i])) - log(mu_p1[i]) <- b_p1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ]) - mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for p1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1]) - } - $m1e - model { - - # Log-normal mixed effects model for L1 ----------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1) - mu_L1[i] <- b_L1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ]) - mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for L1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1 <- sqrt(1/tau_L1) - - invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1]) - } - $m1f - model { - - # Beta mixed effects model for Be1 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15) - - shape1_Be1[i] <- mu_Be1[i] * tau_Be1 - shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1 - - logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ]) - mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - } - - # Priors for the model for Be1 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1]) - } - $m2a - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for y - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } - $m2b - model { - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for b2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } - $m2c - model { - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1mis - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } - $m2d - model { - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i])) - log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for p2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } - $m2e - model { - - # Log-normal mixed effects model for L1mis -------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) - mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for L1mis - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } - $m2f - model { - - # Beta mixed effects model for Be2 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for Be2 - for (k in 1:2) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } - $m3a - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for y - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m3b - model { - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for b2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m3c - model { - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for L1mis - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m3d - model { - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i])) - log(mu_p2[i]) <- b_p2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for p2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m3e - model { - - # Log-normal mixed effects model for L1mis -------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis) - mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for L1mis - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m3f - model { - - # Beta mixed effects model for Be2 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1] - } - - # Priors for the model for Be2 - for (k in 1:1) { - beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] - } - - # Priors for the model for C2 - for (k in 1:1) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m4a - model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] - } - - # Priors for the model for c1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) - log(mu_p2[i]) <- b_p2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] - } - - # Priors for the model for p2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + M_id[ii, 3] * alpha[7] - } - - # Priors for the model for c2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + - alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[10] + M_id[ii, 3] * alpha[11] - } - - # Priors for the model for L1mis - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Beta mixed effects model for Be2 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[13] + M_id[ii, 3] * alpha[14] - } - - # Priors for the model for Be2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[15] - - M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 15:15) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - } - $m4b - model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[3] * M_lvlone[i, 6] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) - mu_p2[i] <- b_p2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[3] * M_lvlone[i, 6] + - alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for p2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - probit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5] - } - - # Priors for the model for b2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- 1/max(1e-10, inv_mu_c2[i]) - inv_mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] - } - - # Priors for the model for c2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Log-normal mixed effects model for L1mis -------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dlnorm(mu_L1mis[i], tau_L1mis) - mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10] - } - - # Priors for the model for L1mis - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - } - $m4c - model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[3] * M_lvlone[i, 6] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) - mu_p2[i] <- b_p2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[3] * M_lvlone[i, 6] + - alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for p2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - log(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - - - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5] - } - - # Priors for the model for b2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) - log(mu_c2[i]) <- b_c2_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8] - } - - # Priors for the model for c2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10] - } - - # Priors for the model for L1mis - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - } - $m4d - model { - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[3] * M_lvlone[i, 7] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - # Priors for the model for c1 - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_beta[k]) - tau_reg_norm_ridge_beta[k] ~ dgamma(0.01, 0.01) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Poisson mixed effects model for p2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i])) - mu_p2[i] <- b_p2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[3] * M_lvlone[i, 7] + - alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - alpha[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ]) - mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for p2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson_ridge_alpha[k]) - tau_reg_poisson_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - - invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - log(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[8] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - alpha[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - - - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[6] - } - - # Priors for the model for b2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_alpha[k]) - tau_reg_binom_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2) - log(mu_c2[i]) <- b_c2_id[group_id[i], 1] + - alpha[11] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - alpha[12] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[10] - } - - # Priors for the model for c2 - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] + - alpha[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[13] - } - - # Priors for the model for L1mis - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k]) - tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Normal mixed effects model for Be2 -------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 6] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1) - mu_Be2[i] <- b_Be2_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * alpha[15] - } - - # Priors for the model for Be2 - for (k in 15:15) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_Be2 <- sqrt(1/tau_Be2) - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - } - $m5a - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[6] * M_lvlone[i, 5] + beta[7] * M_lvlone[i, 6] + - beta[8] * M_lvlone[i, 7] + - beta[9] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + - beta[11] * (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] + - beta[12] * (M_lvlone[i, 10] - spM_lvlone[10, 1])/spM_lvlone[10, 2] + - beta[13] * (M_lvlone[i, 11] - spM_lvlone[11, 1])/spM_lvlone[11, 2] + - beta[14] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] + - M_id[ii, 4] * beta[3] + M_id[ii, 5] * beta[4] + - (M_id[ii, 6] - spM_id[6, 1])/spM_id[6, 2] * beta[5] - mu_b_y_id[ii, 2] <- beta[10] - } - - # Priors for the model for y - for (k in 1:14) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[6] * M_lvlone[i, 5] + - alpha[7] * M_lvlone[i, 6] + alpha[8] * M_lvlone[i, 7] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - - M_lvlone[i, 8] <- abs(M_id[group_id[i], 7] - M_lvlone[i, 2]) - - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] + - M_id[ii, 4] * alpha[3] + M_id[ii, 5] * alpha[4] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[5] - } - - # Priors for the model for c2 - for (k in 1:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Cumulative logit mixed effects model for o2 ----------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dcat(p_o2[i, 1:4]) - eta_o2[i] <- b_o2_id[group_id[i], 1] + - alpha[14] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4]))) - p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2])) - p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3])) - p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3])) - - logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i] - logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i] - logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i] - - M_lvlone[i, 5] <- ifelse(M_lvlone[i, 3] == 2, 1, 0) - M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 3, 1, 0) - M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 4, 1, 0) - - } - - for (ii in 1:100) { - b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ]) - mu_b_o2_id[ii, 1] <- M_id[ii, 3] * alpha[10] + M_id[ii, 4] * alpha[11] + - M_id[ii, 5] * alpha[12] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[13] - } - - - - # Priors for the model for o2 - for (k in 10:14) { - alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal) - } delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - - gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal) - gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1]) - gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2]) - - invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[15] + M_id[ii, 3] * alpha[16] + - M_id[ii, 4] * alpha[17] + M_id[ii, 5] * alpha[18] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[19] - } - - # Priors for the model for time - for (k in 15:19) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Multinomial logit model for M2 ------------------------------------------------ - for (ii in 1:100) { - M_id[ii, 1] ~ dcat(p_M2[ii, 1:4]) - - p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ]))) - p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ]))) - p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ]))) - p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ]))) - - log(phi_M2[ii, 1]) <- 0 - log(phi_M2[ii, 2]) <- M_id[ii, 2] * alpha[20] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[21] - log(phi_M2[ii, 3]) <- M_id[ii, 2] * alpha[22] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[23] - log(phi_M2[ii, 4]) <- M_id[ii, 2] * alpha[24] + - (M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[25] - - M_id[ii, 3] <- ifelse(M_id[ii, 1] == 2, 1, 0) - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 3, 1, 0) - M_id[ii, 5] <- ifelse(M_id[ii, 1] == 4, 1, 0) - - } - - # Priors for the model for M2 - for (k in 20:25) { - alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 10] <- M_lvlone[i, 5] * M_lvlone[i, 8] - M_lvlone[i, 11] <- M_lvlone[i, 6] * M_lvlone[i, 8] - M_lvlone[i, 12] <- M_lvlone[i, 7] * M_lvlone[i, 8] - } - - } - $m5b - model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] + - b_b1_id[group_id[i], 2] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - b_b1_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:3] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 2] * beta[1] - mu_b_b1_id[ii, 2] <- beta[5] - mu_b_b1_id[ii, 3] <- 0 - } - - # Priors for the model for b1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k]) - tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01) - } - - for (k in 1:3) { - RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_b1_id[1:3, 1:3] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id) - D_b1_id[1:3, 1:3] <- inverse(invD_b1_id[ , ]) - - - # Gamma mixed effects model for L1mis ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i]) - - shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2) - rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2) - - mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i]) - inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ]) - mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] - } - - # Priors for the model for L1mis - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k]) - tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma) - sigma_L1mis <- sqrt(1/tau_L1mis) - - invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1]) - - - # Beta mixed effects model for Be2 ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15) - - shape1_Be2[i] <- mu_Be2[i] * tau_Be2 - shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2 - - logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] + - alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - - - M_lvlone[i, 7] <- log(M_lvlone[i, 3]) - - } - - for (ii in 1:100) { - b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ]) - mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] - } - - # Priors for the model for Be2 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta_ridge_alpha[k]) - tau_reg_beta_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta) - - - invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - - - M_lvlone[i, 6] <- abs(M_lvlone[i, 4] - M_id[group_id[i], 1]) - - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[11] - } - - # Priors for the model for c1 - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 5] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[14] - } - - # Priors for the model for time - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - log(mu_C2[ii]) <- M_id[ii, 2] * alpha[15] - - - - } - - # Priors for the model for C2 - for (k in 15:15) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m6a - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - beta[3] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[4] * M_lvlone[i, 3] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- beta[5] - } - - # Priors for the model for y - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Binomial mixed effects model for b2 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i]))) - logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - - - M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0) - } - - for (ii in 1:100) { - b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ]) - mu_b_b2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] - } - - # Priors for the model for b2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] - } - - # Priors for the model for C2 - for (k in 5:6) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m6b - model { - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_b1_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[1] * M_id[group_id[i], 2] + - beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] + - beta[3] * M_id[group_id[i], 3] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:2] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- beta[5] - mu_b_b1_id[ii, 2] <- 0 - } - - # Priors for the model for b1 - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k]) - tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01) - } - - for (k in 1:2) { - RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_b1_id[1:2, 1:2] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id) - D_b1_id[1:2, 1:2] <- inverse(invD_b1_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] + - M_id[ii, 3] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[6] + - M_id[ii, 3] * alpha[7] - } - - # Priors for the model for time - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9] - } - - # Priors for the model for C2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k]) - tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m7a - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[2] - mu_b_y_id[ii, 3] <- beta[3] - } - - # Priors for the model for y - for (k in 1:3) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - } - $m7b - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[2] - mu_b_y_id[ii, 3] <- beta[3] - mu_b_y_id[ii, 4] <- beta[4] - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:4) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) - } - $m7c - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] + - (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2] - mu_b_y_id[ii, 2] <- beta[4] - mu_b_y_id[ii, 3] <- beta[5] - mu_b_y_id[ii, 4] <- beta[6] - } - - # Priors for the model for y - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:4) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) - } - $m7d - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] - mu_b_y_id[ii, 2] <- 0 - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] - } - - # Priors for the model for time - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - } - - # Priors for the model for C2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m7e - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] - mu_b_y_id[ii, 2] <- beta[5] - mu_b_y_id[ii, 3] <- beta[6] - mu_b_y_id[ii, 4] <- beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:4) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] - } - - # Priors for the model for C2 - for (k in 5:6) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m7f - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] + - beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] - mu_b_y_id[ii, 2] <- 0 - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7] - } - - # Priors for the model for time - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - } - - # Priors for the model for C2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m8a - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[4] - mu_b_y_id[ii, 3] <- beta[3] - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } - $m8b - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[4] - mu_b_y_id[ii, 3] <- beta[3] - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1] - } - - # Priors for the model for c2 - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - } - $m8c - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] - mu_b_y_id[ii, 2] <- beta[5] - mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6] - } - - # Priors for the model for y - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] - } - - # Priors for the model for c2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6] - } - - # Priors for the model for c1 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] - - M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 8:8) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3] - } - - } - $m8d - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] - mu_b_y_id[ii, 2] <- beta[5] - mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6] - } - - # Priors for the model for y - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] - } - - # Priors for the model for c2 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6] - } - - # Priors for the model for c1 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9] - } - - # Priors for the model for time - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] - - M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 10:10) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3] - } - - } - $m8e - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for time - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] - } - - } - $m8f - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 10:11) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] - } - - } - $m8g - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 6:7) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] - } - - } - $m8h - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for time - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - } - - } - $m8i - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[5] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 10:11) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - } - - } - $m8j - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for time - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - } - - } - $m8k - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[6] - mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c2 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2) - mu_c2[i] <- b_c2_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ]) - mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c2 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c2 <- sqrt(1/tau_c2) - - invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for c1 - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for time - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - } - - } - $m8l - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] + - beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[5] + M_id[ii, 4] * beta[7] - mu_b_y_id[ii, 3] <- 0 - } - - # Priors for the model for y - for (k in 1:9) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:3) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - M_id[ii, 4] * alpha[7] - } - - # Priors for the model for time - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 8:9) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - - # Re-calculate interaction terms - for (i in 1:329) { - M_lvlone[i, 4] <- M_id[group_id[i], 4] * M_lvlone[i, 2] - M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3] - M_lvlone[i, 7] <- M_id[group_id[i], 4] * M_lvlone[i, 2] * M_lvlone[i, 3] - } - - } - $m8m - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + b_y_id[group_id[i], 2] * M_lvlone[i, 3] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[4] * M_lvlone[i, 4] + beta[5] * M_lvlone[i, 5] + - beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - mu_b_y_id[ii, 2] <- beta[3] - } - - # Priors for the model for y - for (k in 1:6) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - } - $m8n - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] + - b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - b_y_id[group_id[i], 4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] + - beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[6] * M_lvlone[i, 5] - } - - for (ii in 1:100) { - b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 4] * beta[3] - mu_b_y_id[ii, 2] <- beta[2] - mu_b_y_id[ii, 3] <- beta[5] - mu_b_y_id[ii, 4] <- beta[7] - } - - # Priors for the model for y - for (k in 1:7) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:4) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for c1 --------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1) - mu_c1[i] <- b_c1_id[group_id[i], 1] + - alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] + - alpha[5] * M_lvlone[i, 5] - } - - for (ii in 1:100) { - b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ]) - mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for c1 - for (k in 1:5) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_c1 <- sqrt(1/tau_c1) - - invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[6] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] + - M_id[ii, 4] * alpha[8] - } - - # Priors for the model for time - for (k in 6:9) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Binomial mixed effects model for b1 ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 4] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i]))) - logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ]) - mu_b_b1_id[ii, 1] <- M_id[ii, 2] * alpha[10] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] + - M_id[ii, 4] * alpha[12] - } - - # Priors for the model for b1 - for (k in 10:12) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1]) - - - # Binomial model for B2 --------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii]))) - logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14] - - M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0) - - } - - # Priors for the model for B2 - for (k in 13:14) { - alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom) - } - - } - $m9a - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + b_y_o1[group_o1[i], 1] + - beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] + - beta[3] * M_lvlone[i, 3] + - beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] - } - - for (iii in 1:3) { - b_y_o1[iii, 1:1] ~ dnorm(mu_b_y_o1[iii, ], invD_y_o1[ , ]) - mu_b_y_o1[iii, 1] <- 0 - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - invD_y_o1[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_o1[1, 1] <- 1 / (invD_y_o1[1, 1]) - } - $m9b - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] + - b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] - } - - for (ii in 1:100) { - b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + - M_id[ii, 4] * beta[4] - mu_b_y_id[ii, 2] <- beta[5] - } - - # Priors for the model for y - for (k in 1:5) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - for (k in 1:2) { - RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD) - } - invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id) - D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ]) - - - # Normal mixed effects model for time ------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time) - mu_time[i] <- b_time_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ]) - mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] + - M_id[ii, 4] * alpha[4] - } - - # Priors for the model for time - for (k in 1:4) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_time ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_time <- sqrt(1/tau_time) - - invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_time_id[1, 1] <- 1 / (invD_time_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[5] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] + - M_id[ii, 4] * alpha[7] - } - - # Priors for the model for C2 - for (k in 5:7) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - $m9c - model { - - # Normal mixed effects model for y ---------------------------------------------- - for (i in 1:329) { - M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y) - mu_y[i] <- b_y_id[group_id[i], 1] - } - - for (ii in 1:100) { - b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ]) - mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] + - (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] + - M_id[ii, 4] * beta[4] - } - - # Priors for the model for y - for (k in 1:4) { - beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_y ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_y <- sqrt(1/tau_y) - - invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16) - D_y_id[1, 1] <- 1 / (invD_y_id[1, 1]) - - - # Normal model for C2 ----------------------------------------------------------- - for (ii in 1:100) { - M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2) - mu_C2[ii] <- M_id[ii, 2] * alpha[1] + - (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] + - M_id[ii, 4] * alpha[3] - } - - # Priors for the model for C2 - for (k in 1:3) { - alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm) - } - tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm) - sigma_C2 <- sqrt(1/tau_C2) - - } - -# GRcrit and MCerror give same result - - Code - lapply(models0, GR_crit, multivariate = FALSE) - Output - $m0a1 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - $m0a2 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - $m0a3 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - $m0a4 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - $m0b1 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - D_b1_id[1,1] NaN NaN - - - $m0b2 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - D_b1_id[1,1] NaN NaN - - - $m0b3 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - D_b1_id[1,1] NaN NaN - - - $m0b4 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - D_b1_id[1,1] NaN NaN - - - $m0c1 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - sigma_L1 NaN NaN - D_L1_id[1,1] NaN NaN - - - $m0c2 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - sigma_L1 NaN NaN - D_L1_id[1,1] NaN NaN - - - $m0d1 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - D_p1_id[1,1] NaN NaN - - - $m0d2 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - D_p1_id[1,1] NaN NaN - - - $m0e1 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - sigma_L1 NaN NaN - D_L1_id[1,1] NaN NaN - - - $m0f1 - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - tau_Be1 NaN NaN - D_Be1_id[1,1] NaN NaN - - - $m1a - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - $m1b - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - D_b1_id[1,1] NaN NaN - - - $m1c - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - sigma_L1 NaN NaN - D_L1_id[1,1] NaN NaN - - - $m1d - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - D_p1_id[1,1] NaN NaN - - - $m1e - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - sigma_L1 NaN NaN - D_L1_id[1,1] NaN NaN - - - $m1f - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - tau_Be1 NaN NaN - D_Be1_id[1,1] NaN NaN - - - $m2a - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - $m2b - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - D_b2_id[1,1] NaN NaN - - - $m2c - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - sigma_L1mis NaN NaN - D_L1mis_id[1,1] NaN NaN - - - $m2d - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - D_p2_id[1,1] NaN NaN - - - $m2e - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - sigma_L1mis NaN NaN - D_L1mis_id[1,1] NaN NaN - - - $m2f - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - tau_Be2 NaN NaN - D_Be2_id[1,1] NaN NaN - - - $m3a - Potential scale reduction factors: - - Point est. Upper C.I. - C2 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - $m3b - Potential scale reduction factors: - - Point est. Upper C.I. - C2 NaN NaN - D_b2_id[1,1] NaN NaN - - - $m3c - Potential scale reduction factors: - - Point est. Upper C.I. - C2 NaN NaN - sigma_L1mis NaN NaN - D_L1mis_id[1,1] NaN NaN - - - $m3d - Potential scale reduction factors: - - Point est. Upper C.I. - C2 NaN NaN - D_p2_id[1,1] NaN NaN - - - $m3e - Potential scale reduction factors: - - Point est. Upper C.I. - C2 NaN NaN - sigma_L1mis NaN NaN - D_L1mis_id[1,1] NaN NaN - - - $m3f - Potential scale reduction factors: - - Point est. Upper C.I. - C2 NaN NaN - tau_Be2 NaN NaN - D_Be2_id[1,1] NaN NaN - - - $m4a - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - B21 NaN NaN - c2 NaN NaN - p2 NaN NaN - L1mis NaN NaN - Be2 NaN NaN - sigma_c1 NaN NaN - D_c1_id[1,1] NaN NaN - - - $m4b - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - b21 NaN NaN - p2 NaN NaN - L1mis NaN NaN - sigma_c1 NaN NaN - D_c1_id[1,1] NaN NaN - - - $m4c - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - b21 NaN NaN - p2 NaN NaN - L1mis NaN NaN - sigma_c1 NaN NaN - D_c1_id[1,1] NaN NaN - - - $m4d - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c2 NaN NaN - b21 NaN NaN - p2 NaN NaN - L1mis NaN NaN - Be2 NaN NaN - sigma_c1 NaN NaN - D_c1_id[1,1] NaN NaN - - - $m5a - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - M22 NaN NaN - M23 NaN NaN - M24 NaN NaN - log(C1) NaN NaN - o22 NaN NaN - o23 NaN NaN - o24 NaN NaN - abs(C1 - c2) NaN NaN - time NaN NaN - I(time^2) NaN NaN - o22:abs(C1 - c2) NaN NaN - o23:abs(C1 - c2) NaN NaN - o24:abs(C1 - c2) NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - - - $m5b - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - L1mis NaN NaN - abs(c1 - C2) NaN NaN - log(Be2) NaN NaN - time NaN NaN - D_b1_id[1,1] NaN NaN - D_b1_id[1,2] NaN NaN - D_b1_id[2,2] NaN NaN - D_b1_id[1,3] NaN NaN - D_b1_id[2,3] NaN NaN - D_b1_id[3,3] NaN NaN - - - $m6a - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - C2 NaN NaN - b21 NaN NaN - time NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - $m6b - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C2 NaN NaN - B11 NaN NaN - c1 NaN NaN - time NaN NaN - D_b1_id[1,1] NaN NaN - D_b1_id[1,2] NaN NaN - D_b1_id[2,2] NaN NaN - - - $m7a - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - ns(time, df = 2)1 NaN NaN - ns(time, df = 2)2 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m7b - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - bs(time, df = 3)1 NaN NaN - bs(time, df = 3)2 NaN NaN - bs(time, df = 3)3 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - D_y_id[1,4] NaN NaN - D_y_id[2,4] NaN NaN - D_y_id[3,4] NaN NaN - D_y_id[4,4] NaN NaN - - - $m7c - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - c1 NaN NaN - ns(time, df = 3)1 NaN NaN - ns(time, df = 3)2 NaN NaN - ns(time, df = 3)3 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - D_y_id[1,4] NaN NaN - D_y_id[2,4] NaN NaN - D_y_id[3,4] NaN NaN - D_y_id[4,4] NaN NaN - - - $m7d - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - C2 NaN NaN - c1 NaN NaN - ns(time, df = 3)1 NaN NaN - ns(time, df = 3)2 NaN NaN - ns(time, df = 3)3 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - - - $m7e - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - C2 NaN NaN - c1 NaN NaN - ns(time, df = 3)1 NaN NaN - ns(time, df = 3)2 NaN NaN - ns(time, df = 3)3 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - D_y_id[1,4] NaN NaN - D_y_id[2,4] NaN NaN - D_y_id[3,4] NaN NaN - D_y_id[4,4] NaN NaN - - - $m7f - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - C2 NaN NaN - c1 NaN NaN - ns(time, df = 3)1 NaN NaN - ns(time, df = 3)2 NaN NaN - ns(time, df = 3)3 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - - - $m8a - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c1 NaN NaN - c2 NaN NaN - time NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8b - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c1 NaN NaN - c2 NaN NaN - time NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8c - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - B21 NaN NaN - c1 NaN NaN - c2 NaN NaN - time NaN NaN - B21:c1 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8d - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - B21 NaN NaN - c1 NaN NaN - c2 NaN NaN - time NaN NaN - B21:c1 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8e - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c1 NaN NaN - c2 NaN NaN - time NaN NaN - B21:c1 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8f - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c1 NaN NaN - c2 NaN NaN - time NaN NaN - B21:c1 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8g - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c1 NaN NaN - c2 NaN NaN - time NaN NaN - B21:c1 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8h - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c2 NaN NaN - c1 NaN NaN - time NaN NaN - B21:c2 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8i - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c2 NaN NaN - c1 NaN NaN - time NaN NaN - B21:c2 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8j - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c2 NaN NaN - c1 NaN NaN - time NaN NaN - B21:c2 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8k - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c2 NaN NaN - c1 NaN NaN - time NaN NaN - B21:c2 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8l - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c1 NaN NaN - time NaN NaN - B21:c1 NaN NaN - B21:time NaN NaN - c1:time NaN NaN - B21:c1:time NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - - - $m8m - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c1 NaN NaN - b11 NaN NaN - o1.L NaN NaN - o1.Q NaN NaN - c1:b11 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - - - $m8n - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - B21 NaN NaN - c1 NaN NaN - time NaN NaN - b11 NaN NaN - C1:time NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - D_y_id[1,3] NaN NaN - D_y_id[2,3] NaN NaN - D_y_id[3,3] NaN NaN - D_y_id[1,4] NaN NaN - D_y_id[2,4] NaN NaN - D_y_id[3,4] NaN NaN - D_y_id[4,4] NaN NaN - - - $m9a - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - c1 NaN NaN - b11 NaN NaN - time NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_o1[1,1] NaN NaN - - - $m9b - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - C2 NaN NaN - B11 NaN NaN - time NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - D_y_id[1,2] NaN NaN - D_y_id[2,2] NaN NaN - - - $m9c - Potential scale reduction factors: - - Point est. Upper C.I. - (Intercept) NaN NaN - C1 NaN NaN - C2 NaN NaN - B11 NaN NaN - sigma_y NaN NaN - D_y_id[1,1] NaN NaN - - - ---- - - Code - lapply(models0, MC_error) - Output - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - $m0a1 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - $m0a2 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - $m0a3 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - $m0a4 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - $m0b1 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - D_b1_id[1,1] 0 0 0 NaN - - $m0b2 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - D_b1_id[1,1] 0 0 0 NaN - - $m0b3 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - D_b1_id[1,1] 0 0 0 NaN - - $m0b4 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - D_b1_id[1,1] 0 0 0 NaN - - $m0c1 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - sigma_L1 0 0 0 NaN - D_L1_id[1,1] 0 0 0 NaN - - $m0c2 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - sigma_L1 0 0 0 NaN - D_L1_id[1,1] 0 0 0 NaN - - $m0d1 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - D_p1_id[1,1] 0 0 0 NaN - - $m0d2 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - D_p1_id[1,1] 0 0 0 NaN - - $m0e1 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - sigma_L1 0 0 0 NaN - D_L1_id[1,1] 0 0 0 NaN - - $m0f1 - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - tau_Be1 0 0 0 NaN - D_Be1_id[1,1] 0 0 0 NaN - - $m1a - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - $m1b - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - D_b1_id[1,1] 0 0 0 NaN - - $m1c - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - sigma_L1 0 0 0 NaN - D_L1_id[1,1] 0 0 0 NaN - - $m1d - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - D_p1_id[1,1] 0 0 0 NaN - - $m1e - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - sigma_L1 0 0 0 NaN - D_L1_id[1,1] 0 0 0 NaN - - $m1f - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - tau_Be1 0 0 0 NaN - D_Be1_id[1,1] 0 0 0 NaN - - $m2a - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - $m2b - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - D_b2_id[1,1] 0 0 0 NaN - - $m2c - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - sigma_L1mis 0 0 0 NaN - D_L1mis_id[1,1] 0 0 0 NaN - - $m2d - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - D_p2_id[1,1] 0 0 0 NaN - - $m2e - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - sigma_L1mis 0 0 0 NaN - D_L1mis_id[1,1] 0 0 0 NaN - - $m2f - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - tau_Be2 0 0 0 NaN - D_Be2_id[1,1] 0 0 0 NaN - - $m3a - est MCSE SD MCSE/SD - C2 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - $m3b - est MCSE SD MCSE/SD - C2 0 0 0 NaN - D_b2_id[1,1] 0 0 0 NaN - - $m3c - est MCSE SD MCSE/SD - C2 0 0 0 NaN - sigma_L1mis 0 0 0 NaN - D_L1mis_id[1,1] 0 0 0 NaN - - $m3d - est MCSE SD MCSE/SD - C2 0 0 0 NaN - D_p2_id[1,1] 0 0 0 NaN - - $m3e - est MCSE SD MCSE/SD - C2 0 0 0 NaN - sigma_L1mis 0 0 0 NaN - D_L1mis_id[1,1] 0 0 0 NaN - - $m3f - est MCSE SD MCSE/SD - C2 0 0 0 NaN - tau_Be2 0 0 0 NaN - D_Be2_id[1,1] 0 0 0 NaN - - $m4a - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - B21 0 0 0 NaN - c2 0 0 0 NaN - p2 0 0 0 NaN - L1mis 0 0 0 NaN - Be2 0 0 0 NaN - sigma_c1 0 0 0 NaN - D_c1_id[1,1] 0 0 0 NaN - - $m4b - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - b21 0 0 0 NaN - p2 0 0 0 NaN - L1mis 0 0 0 NaN - sigma_c1 0 0 0 NaN - D_c1_id[1,1] 0 0 0 NaN - - $m4c - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - b21 0 0 0 NaN - p2 0 0 0 NaN - L1mis 0 0 0 NaN - sigma_c1 0 0 0 NaN - D_c1_id[1,1] 0 0 0 NaN - - $m4d - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c2 0 0 0 NaN - b21 0 0 0 NaN - p2 0 0 0 NaN - L1mis 0 0 0 NaN - Be2 0 0 0 NaN - sigma_c1 0 0 0 NaN - D_c1_id[1,1] 0 0 0 NaN - - $m5a - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - M22 0 0 0 NaN - M23 0 0 0 NaN - M24 0 0 0 NaN - log(C1) 0 0 0 NaN - o22 0 0 0 NaN - o23 0 0 0 NaN - o24 0 0 0 NaN - abs(C1 - c2) 0 0 0 NaN - time 0 0 0 NaN - I(time^2) 0 0 0 NaN - o22:abs(C1 - c2) 0 0 0 NaN - o23:abs(C1 - c2) 0 0 0 NaN - o24:abs(C1 - c2) 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - - $m5b - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - L1mis 0 0 0 NaN - abs(c1 - C2) 0 0 0 NaN - log(Be2) 0 0 0 NaN - time 0 0 0 NaN - D_b1_id[1,1] 0 0 0 NaN - D_b1_id[1,2] 0 0 0 NaN - D_b1_id[2,2] 0 0 0 NaN - D_b1_id[1,3] 0 0 0 NaN - D_b1_id[2,3] 0 0 0 NaN - D_b1_id[3,3] 0 0 0 NaN - - $m6a - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - C2 0 0 0 NaN - b21 0 0 0 NaN - time 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - $m6b - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C2 0 0 0 NaN - B11 0 0 0 NaN - c1 0 0 0 NaN - time 0 0 0 NaN - D_b1_id[1,1] 0 0 0 NaN - D_b1_id[1,2] 0 0 0 NaN - D_b1_id[2,2] 0 0 0 NaN - - $m7a - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - ns(time, df = 2)1 0 0 0 NaN - ns(time, df = 2)2 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m7b - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - bs(time, df = 3)1 0 0 0 NaN - bs(time, df = 3)2 0 0 0 NaN - bs(time, df = 3)3 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - D_y_id[1,4] 0 0 0 NaN - D_y_id[2,4] 0 0 0 NaN - D_y_id[3,4] 0 0 0 NaN - D_y_id[4,4] 0 0 0 NaN - - $m7c - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - c1 0 0 0 NaN - ns(time, df = 3)1 0 0 0 NaN - ns(time, df = 3)2 0 0 0 NaN - ns(time, df = 3)3 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - D_y_id[1,4] 0 0 0 NaN - D_y_id[2,4] 0 0 0 NaN - D_y_id[3,4] 0 0 0 NaN - D_y_id[4,4] 0 0 0 NaN - - $m7d - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - C2 0 0 0 NaN - c1 0 0 0 NaN - ns(time, df = 3)1 0 0 0 NaN - ns(time, df = 3)2 0 0 0 NaN - ns(time, df = 3)3 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - - $m7e - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - C2 0 0 0 NaN - c1 0 0 0 NaN - ns(time, df = 3)1 0 0 0 NaN - ns(time, df = 3)2 0 0 0 NaN - ns(time, df = 3)3 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - D_y_id[1,4] 0 0 0 NaN - D_y_id[2,4] 0 0 0 NaN - D_y_id[3,4] 0 0 0 NaN - D_y_id[4,4] 0 0 0 NaN - - $m7f - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - C2 0 0 0 NaN - c1 0 0 0 NaN - ns(time, df = 3)1 0 0 0 NaN - ns(time, df = 3)2 0 0 0 NaN - ns(time, df = 3)3 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - - $m8a - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c1 0 0 0 NaN - c2 0 0 0 NaN - time 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8b - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c1 0 0 0 NaN - c2 0 0 0 NaN - time 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8c - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - B21 0 0 0 NaN - c1 0 0 0 NaN - c2 0 0 0 NaN - time 0 0 0 NaN - B21:c1 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8d - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - B21 0 0 0 NaN - c1 0 0 0 NaN - c2 0 0 0 NaN - time 0 0 0 NaN - B21:c1 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8e - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c1 0 0 0 NaN - c2 0 0 0 NaN - time 0 0 0 NaN - B21:c1 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8f - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c1 0 0 0 NaN - c2 0 0 0 NaN - time 0 0 0 NaN - B21:c1 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8g - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c1 0 0 0 NaN - c2 0 0 0 NaN - time 0 0 0 NaN - B21:c1 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8h - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c2 0 0 0 NaN - c1 0 0 0 NaN - time 0 0 0 NaN - B21:c2 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8i - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c2 0 0 0 NaN - c1 0 0 0 NaN - time 0 0 0 NaN - B21:c2 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8j - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c2 0 0 0 NaN - c1 0 0 0 NaN - time 0 0 0 NaN - B21:c2 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8k - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c2 0 0 0 NaN - c1 0 0 0 NaN - time 0 0 0 NaN - B21:c2 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8l - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c1 0 0 0 NaN - time 0 0 0 NaN - B21:c1 0 0 0 NaN - B21:time 0 0 0 NaN - c1:time 0 0 0 NaN - B21:c1:time 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - - $m8m - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c1 0 0 0 NaN - b11 0 0 0 NaN - o1.L 0 0 0 NaN - o1.Q 0 0 0 NaN - c1:b11 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - - $m8n - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - B21 0 0 0 NaN - c1 0 0 0 NaN - time 0 0 0 NaN - b11 0 0 0 NaN - C1:time 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - D_y_id[1,3] 0 0 0 NaN - D_y_id[2,3] 0 0 0 NaN - D_y_id[3,3] 0 0 0 NaN - D_y_id[1,4] 0 0 0 NaN - D_y_id[2,4] 0 0 0 NaN - D_y_id[3,4] 0 0 0 NaN - D_y_id[4,4] 0 0 0 NaN - - $m9a - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - c1 0 0 0 NaN - b11 0 0 0 NaN - time 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_o1[1,1] 0 0 0 NaN - - $m9b - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - C2 0 0 0 NaN - B11 0 0 0 NaN - time 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - D_y_id[1,2] 0 0 0 NaN - D_y_id[2,2] 0 0 0 NaN - - $m9c - est MCSE SD MCSE/SD - (Intercept) 0 0 0 NaN - C1 0 0 0 NaN - C2 0 0 0 NaN - B11 0 0 0 NaN - sigma_y 0 0 0 NaN - D_y_id[1,1] 0 0 0 NaN - - -# summary output remained the same - - Code - lapply(models0, print) - Output - - Call: - lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), - n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), - n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - Call: - glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - Call: - glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - Call: - glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - p1 - (Intercept) - p1 (Intercept) 0 - - - Call: - glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - p1 - (Intercept) - p1 (Intercept) 0 - - - Call: - lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian log-normal mixed model for "L1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - Call: - betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian beta mixed model for "Be1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - Be1 - (Intercept) - Be1 (Intercept) 0 - - - Call: - lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - Call: - glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - Call: - glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - p1 - (Intercept) - p1 (Intercept) 0 - - - Call: - lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian log-normal mixed model for "L1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - Call: - betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian beta mixed model for "Be1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - Be1 - (Intercept) - Be1 (Intercept) 0 - - - Call: - lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b2" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - b2 - (Intercept) - b2 (Intercept) 0 - - - Call: - glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1mis" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - L1mis - (Intercept) - L1mis (Intercept) 0 - - - - Residual standard deviation: - sigma_L1mis - 0 - - Call: - glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p2" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - p2 - (Intercept) - p2 (Intercept) 0 - - - Call: - lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian log-normal mixed model for "L1mis" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - L1mis - (Intercept) - L1mis (Intercept) 0 - - - - Residual standard deviation: - sigma_L1mis - 0 - - Call: - betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian beta mixed model for "Be2" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - Be2 - (Intercept) - Be2 (Intercept) 0 - - - Call: - lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b2" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - b2 - (Intercept) - b2 (Intercept) 0 - - - Call: - glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1mis" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - L1mis - (Intercept) - L1mis (Intercept) 0 - - - - Residual standard deviation: - sigma_L1mis - 0 - - Call: - glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p2" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - p2 - (Intercept) - p2 (Intercept) 0 - - - Call: - lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian log-normal mixed model for "L1mis" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - L1mis - (Intercept) - L1mis (Intercept) 0 - - - - Residual standard deviation: - sigma_L1mis - 0 - - Call: - betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian beta mixed model for "Be2" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - Be2 - (Intercept) - Be2 (Intercept) 0 - - - Call: - lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", - L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - - Fixed effects: - (Intercept) B21 c2 p2 L1mis Be2 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - c1 - (Intercept) - c1 (Intercept) 0 - - - - Residual standard deviation: - sigma_c1 - 0 - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", - p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", - L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - - Fixed effects: - (Intercept) c2 b21 p2 L1mis - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - c1 - (Intercept) - c1 (Intercept) 0 - - - - Residual standard deviation: - sigma_c1 - 0 - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", - p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", - b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - - Fixed effects: - (Intercept) c2 b21 p2 L1mis - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - c1 - (Intercept) - c1 (Intercept) 0 - - - - Residual standard deviation: - sigma_c1 - 0 - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", - p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", - b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, - warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1))) - - Bayesian linear mixed model for "c1" - - Fixed effects: - (Intercept) c2 b21 p2 L1mis Be2 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - c1 - (Intercept) - c1 (Intercept) 0 - - - - Residual standard deviation: - sigma_c1 - 0 - - Call: - lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + - I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) M22 M23 M24 - 0 0 0 0 - log(C1) o22 o23 o24 - 0 0 0 0 - abs(C1 - c2) time I(time^2) o22:abs(C1 - c2) - 0 0 0 0 - o23:abs(C1 - c2) o24:abs(C1 - c2) - 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) time - y (Intercept) 0 0 - y time 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + - (time + I(time^2) | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", - L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) L1mis abs(c1 - C2) log(Be2) time - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - b1 b1 b1 - (Intercept) time I(time^2) - b1 (Intercept) 0 0 0 - b1 time 0 0 0 - b1 I(time^2) 0 0 0 - - - Call: - lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, - n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 b21 time - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y - time - y time 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | - id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, - shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) C2 B11 c1 time - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - b1 b1 - time I(time^2) - b1 time 0 0 - b1 I(time^2) 0 0 - - - Call: - lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, - df = 2) | id, n.iter = 10, seed = 2020, adapt = 5) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 - 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 - y (Intercept) 0 0 0 - y ns(time, df = 2)1 0 0 0 - y ns(time, df = 2)2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, - df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 - 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y y - (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 - y (Intercept) 0 0 0 0 - y bs(time, df = 3)1 0 0 0 0 - y bs(time, df = 3)2 0 0 0 0 - y bs(time, df = 3)3 0 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, - random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, - nadapt = 5) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 c1 ns(time, df = 3)1 - 0 0 0 0 - ns(time, df = 3)2 ns(time, df = 3)3 - 0 0 - - - Random effects covariance matrix: - $id - y y y y - (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - y (Intercept) 0 0 0 0 - y ns(time, df = 3)1 0 0 0 0 - y ns(time, df = 3)2 0 0 0 0 - y ns(time, df = 3)3 0 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - 0 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) time - y (Intercept) 0 0 - y time 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, - no_model = "time", seed = 2020) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - 0 0 0 - - - Random effects covariance matrix: - $id - y y y y - (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - y (Intercept) 0 0 0 0 - y ns(time, df = 3)1 0 0 0 0 - y ns(time, df = 3)2 0 0 0 0 - y ns(time, df = 3)3 0 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - 0 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) time - y (Intercept) 0 0 - y time 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + - c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c1 c2 time - 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + - c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c1 c2 time - 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + - c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) B21 c1 c2 time B21:c1 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c1 - y (Intercept) 0 0 0 - y time 0 0 0 - y c1 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + - c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) B21 c1 c2 time B21:c1 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c1 - y (Intercept) 0 0 0 - y time 0 0 0 - y c1 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", - "c1"), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c1 - y (Intercept) 0 0 0 - y time 0 0 0 - y c1 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c1 - y (Intercept) 0 0 0 - y time 0 0 0 - y c1 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + - I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 time B21:c1 - 0 0 0 0 0 0 - B21:time c1:time B21:c1:time - 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time I(time^2) - y (Intercept) 0 0 0 - y time 0 0 0 - y I(time^2) 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | - id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c1 b11 o1.L o1.Q c1:b11 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) b11 - y (Intercept) 0 0 - y b11 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, - random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 time b11 - 0 0 0 0 0 0 - C1:time - 0 - - - Random effects covariance matrix: - $id - y y y y - (Intercept) C1 time C1:time - y (Intercept) 0 0 0 0 - y C1 0 0 0 0 - y time 0 0 0 0 - y C1:time 0 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c1 b11 time - 0 0 0 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - $o1 - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 B11 time - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) time - y (Intercept) 0 0 - y time 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - Call: - lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, monitor_params = c(analysis_random = TRUE), - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 B11 - 0 0 0 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - $m0a1 - - Call: - lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m0a2 - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m0a3 - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m0a4 - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m0b1 - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - $m0b2 - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - $m0b3 - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), - n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - $m0b4 - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), - n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - $m0c1 - - Call: - glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - $m0c2 - - Call: - glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - $m0d1 - - Call: - glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - p1 - (Intercept) - p1 (Intercept) 0 - - - $m0d2 - - Call: - glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - p1 - (Intercept) - p1 (Intercept) 0 - - - $m0e1 - - Call: - lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian log-normal mixed model for "L1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - $m0f1 - - Call: - betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian beta mixed model for "Be1" - - Fixed effects: - (Intercept) - 0 - - - Random effects covariance matrix: - $id - Be1 - (Intercept) - Be1 (Intercept) 0 - - - $m1a - - Call: - lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m1b - - Call: - glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - b1 - (Intercept) - b1 (Intercept) 0 - - - $m1c - - Call: - glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - $m1d - - Call: - glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - p1 - (Intercept) - p1 (Intercept) 0 - - - $m1e - - Call: - lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian log-normal mixed model for "L1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - L1 - (Intercept) - L1 (Intercept) 0 - - - - Residual standard deviation: - sigma_L1 - 0 - - $m1f - - Call: - betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian beta mixed model for "Be1" - - Fixed effects: - (Intercept) C1 - 0 0 - - - Random effects covariance matrix: - $id - Be1 - (Intercept) - Be1 (Intercept) 0 - - - $m2a - - Call: - lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m2b - - Call: - glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b2" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - b2 - (Intercept) - b2 (Intercept) 0 - - - $m2c - - Call: - glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1mis" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - L1mis - (Intercept) - L1mis (Intercept) 0 - - - - Residual standard deviation: - sigma_L1mis - 0 - - $m2d - - Call: - glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p2" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - p2 - (Intercept) - p2 (Intercept) 0 - - - $m2e - - Call: - lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian log-normal mixed model for "L1mis" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - L1mis - (Intercept) - L1mis (Intercept) 0 - - - - Residual standard deviation: - sigma_L1mis - 0 - - $m2f - - Call: - betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian beta mixed model for "Be2" - - Fixed effects: - (Intercept) c2 - 0 0 - - - Random effects covariance matrix: - $id - Be2 - (Intercept) - Be2 (Intercept) 0 - - - $m3a - - Call: - lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m3b - - Call: - glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b2" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - b2 - (Intercept) - b2 (Intercept) 0 - - - $m3c - - Call: - glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian Gamma mixed model for "L1mis" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - L1mis - (Intercept) - L1mis (Intercept) 0 - - - - Residual standard deviation: - sigma_L1mis - 0 - - $m3d - - Call: - glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian poisson mixed model for "p2" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - p2 - (Intercept) - p2 (Intercept) 0 - - - $m3e - - Call: - lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian log-normal mixed model for "L1mis" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - L1mis - (Intercept) - L1mis (Intercept) 0 - - - - Residual standard deviation: - sigma_L1mis - 0 - - $m3f - - Call: - betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian beta mixed model for "Be2" - - Fixed effects: - C2 - 0 - - - Random effects covariance matrix: - $id - Be2 - (Intercept) - Be2 (Intercept) 0 - - - $m4a - - Call: - lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", - L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - - Fixed effects: - (Intercept) B21 c2 p2 L1mis Be2 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - c1 - (Intercept) - c1 (Intercept) 0 - - - - Residual standard deviation: - sigma_c1 - 0 - - $m4b - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", - p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", - L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - - Fixed effects: - (Intercept) c2 b21 p2 L1mis - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - c1 - (Intercept) - c1 (Intercept) 0 - - - - Residual standard deviation: - sigma_c1 - 0 - - $m4c - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", - p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", - b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "c1" - - Fixed effects: - (Intercept) c2 b21 p2 L1mis - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - c1 - (Intercept) - c1 (Intercept) 0 - - - - Residual standard deviation: - sigma_c1 - 0 - - $m4d - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", - p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", - b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, - warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1))) - - Bayesian linear mixed model for "c1" - - Fixed effects: - (Intercept) c2 b21 p2 L1mis Be2 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - c1 - (Intercept) - c1 (Intercept) 0 - - - - Residual standard deviation: - sigma_c1 - 0 - - $m5a - - Call: - lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + - I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) M22 M23 M24 - 0 0 0 0 - log(C1) o22 o23 o24 - 0 0 0 0 - abs(C1 - c2) time I(time^2) o22:abs(C1 - c2) - 0 0 0 0 - o23:abs(C1 - c2) o24:abs(C1 - c2) - 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) time - y (Intercept) 0 0 - y time 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m5b - - Call: - glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + - (time + I(time^2) | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", - L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) L1mis abs(c1 - C2) log(Be2) time - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - b1 b1 b1 - (Intercept) time I(time^2) - b1 (Intercept) 0 0 0 - b1 time 0 0 0 - b1 I(time^2) 0 0 0 - - - $m6a - - Call: - lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, - n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 b21 time - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y - time - y time 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m6b - - Call: - glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | - id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, - shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian binomial mixed model for "b1" - - Fixed effects: - (Intercept) C2 B11 c1 time - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - b1 b1 - time I(time^2) - b1 time 0 0 - b1 I(time^2) 0 0 - - - $m7a - - Call: - lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, - df = 2) | id, n.iter = 10, seed = 2020, adapt = 5) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 - 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 - y (Intercept) 0 0 0 - y ns(time, df = 2)1 0 0 0 - y ns(time, df = 2)2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m7b - - Call: - lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, - df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 - 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y y - (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 - y (Intercept) 0 0 0 0 - y bs(time, df = 3)1 0 0 0 0 - y bs(time, df = 3)2 0 0 0 0 - y bs(time, df = 3)3 0 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m7c - - Call: - lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, - random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, - nadapt = 5) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 c1 ns(time, df = 3)1 - 0 0 0 0 - ns(time, df = 3)2 ns(time, df = 3)3 - 0 0 - - - Random effects covariance matrix: - $id - y y y y - (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - y (Intercept) 0 0 0 0 - y ns(time, df = 3)1 0 0 0 0 - y ns(time, df = 3)2 0 0 0 0 - y ns(time, df = 3)3 0 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m7d - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - 0 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) time - y (Intercept) 0 0 - y time 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m7e - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, - no_model = "time", seed = 2020) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - 0 0 0 - - - Random effects covariance matrix: - $id - y y y y - (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - y (Intercept) 0 0 0 0 - y ns(time, df = 3)1 0 0 0 0 - y ns(time, df = 3)2 0 0 0 0 - y ns(time, df = 3)3 0 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m7f - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 - 0 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) time - y (Intercept) 0 0 - y time 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8a - - Call: - lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + - c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c1 c2 time - 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8b - - Call: - lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + - c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c1 c2 time - 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8c - - Call: - lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + - c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) B21 c1 c2 time B21:c1 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c1 - y (Intercept) 0 0 0 - y time 0 0 0 - y c1 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8d - - Call: - lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + - c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) B21 c1 c2 time B21:c1 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c1 - y (Intercept) 0 0 0 - y time 0 0 0 - y c1 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8e - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8f - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8g - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", - "c1"), seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8h - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c1 - y (Intercept) 0 0 0 - y time 0 0 0 - y c1 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8i - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c1 - y (Intercept) 0 0 0 - y time 0 0 0 - y c1 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8j - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8k - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 - 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time c2 - y (Intercept) 0 0 0 - y time 0 0 0 - y c2 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8l - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + - I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 time B21:c1 - 0 0 0 0 0 0 - B21:time c1:time B21:c1:time - 0 0 0 - - - Random effects covariance matrix: - $id - y y y - (Intercept) time I(time^2) - y (Intercept) 0 0 0 - y time 0 0 0 - y I(time^2) 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8m - - Call: - lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | - id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c1 b11 o1.L o1.Q c1:b11 - 0 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) b11 - y (Intercept) 0 0 - y b11 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m8n - - Call: - lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, - random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 B21 c1 time b11 - 0 0 0 0 0 0 - C1:time - 0 - - - Random effects covariance matrix: - $id - y y y y - (Intercept) C1 time C1:time - y (Intercept) 0 0 0 0 - y C1 0 0 0 0 - y time 0 0 0 0 - y C1:time 0 0 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m9a - - Call: - lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) c1 b11 time - 0 0 0 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - $o1 - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m9b - - Call: - lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 B11 time - 0 0 0 0 0 - - - Random effects covariance matrix: - $id - y y - (Intercept) time - y (Intercept) 0 0 - y time 0 0 - - - - Residual standard deviation: - sigma_y - 0 - - $m9c - - Call: - lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, monitor_params = c(analysis_random = TRUE), - seed = 2020, warn = FALSE, mess = FALSE) - - Bayesian linear mixed model for "y" - - Fixed effects: - (Intercept) C1 C2 B11 - 0 0 0 0 - - - Random effects covariance matrix: - $id - y - (Intercept) - y (Intercept) 0 - - - - Residual standard deviation: - sigma_y - 0 - - ---- - - Code - lapply(models0, coef) - Output - $m0a1 - $m0a1$y - (Intercept) sigma_y D_y_id[1,1] - 0 0 0 - - - $m0a2 - $m0a2$y - (Intercept) sigma_y D_y_id[1,1] - 0 0 0 - - - $m0a3 - $m0a3$y - (Intercept) sigma_y D_y_id[1,1] - 0 0 0 - - - $m0a4 - $m0a4$y - (Intercept) sigma_y D_y_id[1,1] - 0 0 0 - - - $m0b1 - $m0b1$b1 - (Intercept) D_b1_id[1,1] - 0 0 - - - $m0b2 - $m0b2$b1 - (Intercept) D_b1_id[1,1] - 0 0 - - - $m0b3 - $m0b3$b1 - (Intercept) D_b1_id[1,1] - 0 0 - - - $m0b4 - $m0b4$b1 - (Intercept) D_b1_id[1,1] - 0 0 - - - $m0c1 - $m0c1$L1 - (Intercept) sigma_L1 D_L1_id[1,1] - 0 0 0 - - - $m0c2 - $m0c2$L1 - (Intercept) sigma_L1 D_L1_id[1,1] - 0 0 0 - - - $m0d1 - $m0d1$p1 - (Intercept) D_p1_id[1,1] - 0 0 - - - $m0d2 - $m0d2$p1 - (Intercept) D_p1_id[1,1] - 0 0 - - - $m0e1 - $m0e1$L1 - (Intercept) sigma_L1 D_L1_id[1,1] - 0 0 0 - - - $m0f1 - $m0f1$Be1 - (Intercept) tau_Be1 D_Be1_id[1,1] - 0 0 0 - - - $m1a - $m1a$y - (Intercept) C1 sigma_y D_y_id[1,1] - 0 0 0 0 - - - $m1b - $m1b$b1 - (Intercept) C1 D_b1_id[1,1] - 0 0 0 - - - $m1c - $m1c$L1 - (Intercept) C1 sigma_L1 D_L1_id[1,1] - 0 0 0 0 - - - $m1d - $m1d$p1 - (Intercept) C1 D_p1_id[1,1] - 0 0 0 - - - $m1e - $m1e$L1 - (Intercept) C1 sigma_L1 D_L1_id[1,1] - 0 0 0 0 - - - $m1f - $m1f$Be1 - (Intercept) C1 tau_Be1 D_Be1_id[1,1] - 0 0 0 0 - - - $m2a - $m2a$y - (Intercept) c2 sigma_y D_y_id[1,1] - 0 0 0 0 - - - $m2b - $m2b$b2 - (Intercept) c2 D_b2_id[1,1] - 0 0 0 - - - $m2c - $m2c$L1mis - (Intercept) c2 sigma_L1mis D_L1mis_id[1,1] - 0 0 0 0 - - - $m2d - $m2d$p2 - (Intercept) c2 D_p2_id[1,1] - 0 0 0 - - - $m2e - $m2e$L1mis - (Intercept) c2 sigma_L1mis D_L1mis_id[1,1] - 0 0 0 0 - - - $m2f - $m2f$Be2 - (Intercept) c2 tau_Be2 D_Be2_id[1,1] - 0 0 0 0 - - - $m3a - $m3a$y - C2 sigma_y D_y_id[1,1] - 0 0 0 - - - $m3b - $m3b$b2 - C2 D_b2_id[1,1] - 0 0 - - - $m3c - $m3c$L1mis - C2 sigma_L1mis D_L1mis_id[1,1] - 0 0 0 - - - $m3d - $m3d$p2 - C2 D_p2_id[1,1] - 0 0 - - - $m3e - $m3e$L1mis - C2 sigma_L1mis D_L1mis_id[1,1] - 0 0 0 - - - $m3f - $m3f$Be2 - C2 tau_Be2 D_Be2_id[1,1] - 0 0 0 - - - $m4a - $m4a$c1 - (Intercept) B21 c2 p2 L1mis Be2 - 0 0 0 0 0 0 - sigma_c1 D_c1_id[1,1] - 0 0 - - - $m4b - $m4b$c1 - (Intercept) c2 b21 p2 L1mis sigma_c1 - 0 0 0 0 0 0 - D_c1_id[1,1] - 0 - - - $m4c - $m4c$c1 - (Intercept) c2 b21 p2 L1mis sigma_c1 - 0 0 0 0 0 0 - D_c1_id[1,1] - 0 - - - $m4d - $m4d$c1 - (Intercept) c2 b21 p2 L1mis Be2 - 0 0 0 0 0 0 - sigma_c1 D_c1_id[1,1] - 0 0 - - - $m5a - $m5a$y - (Intercept) M22 M23 M24 - 0 0 0 0 - log(C1) o22 o23 o24 - 0 0 0 0 - abs(C1 - c2) time I(time^2) o22:abs(C1 - c2) - 0 0 0 0 - o23:abs(C1 - c2) o24:abs(C1 - c2) sigma_y D_y_id[1,1] - 0 0 0 0 - D_y_id[1,2] D_y_id[2,2] - 0 0 - - - $m5b - $m5b$b1 - (Intercept) L1mis abs(c1 - C2) log(Be2) time D_b1_id[1,1] - 0 0 0 0 0 0 - D_b1_id[1,2] D_b1_id[2,2] D_b1_id[1,3] D_b1_id[2,3] D_b1_id[3,3] - 0 0 0 0 0 - - - $m6a - $m6a$y - (Intercept) C1 C2 b21 time sigma_y - 0 0 0 0 0 0 - D_y_id[1,1] - 0 - - - $m6b - $m6b$b1 - (Intercept) C2 B11 c1 time D_b1_id[1,1] - 0 0 0 0 0 0 - D_b1_id[1,2] D_b1_id[2,2] - 0 0 - - - $m7a - $m7a$y - (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 sigma_y - 0 0 0 0 - D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] - 0 0 - - - $m7b - $m7b$y - (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 - 0 0 0 0 - sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] - 0 0 0 0 - D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] - 0 0 0 0 - D_y_id[2,4] D_y_id[3,4] D_y_id[4,4] - 0 0 0 - - - $m7c - $m7c$y - (Intercept) C1 c1 ns(time, df = 3)1 - 0 0 0 0 - ns(time, df = 3)2 ns(time, df = 3)3 sigma_y D_y_id[1,1] - 0 0 0 0 - D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] - 0 0 0 0 - D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] D_y_id[3,4] - 0 0 0 0 - D_y_id[4,4] - 0 - - - $m7d - $m7d$y - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y - 0 0 0 0 - D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] - 0 0 0 - - - $m7e - $m7e$y - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y - 0 0 0 0 - D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] - 0 0 0 0 - D_y_id[3,4] D_y_id[4,4] - 0 0 - - - $m7f - $m7f$y - (Intercept) C1 C2 c1 - 0 0 0 0 - ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y - 0 0 0 0 - D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] - 0 0 0 - - - $m8a - $m8a$y - (Intercept) c1 c2 time sigma_y D_y_id[1,1] - 0 0 0 0 0 0 - D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] - 0 0 0 0 0 - - - $m8b - $m8b$y - (Intercept) c1 c2 time sigma_y D_y_id[1,1] - 0 0 0 0 0 0 - D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] - 0 0 0 0 0 - - - $m8c - $m8c$y - (Intercept) B21 c1 c2 time B21:c1 - 0 0 0 0 0 0 - sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] - 0 0 0 0 0 0 - D_y_id[3,3] - 0 - - - $m8d - $m8d$y - (Intercept) B21 c1 c2 time B21:c1 - 0 0 0 0 0 0 - sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] - 0 0 0 0 0 0 - D_y_id[3,3] - 0 - - - $m8e - $m8e$y - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] - 0 0 - - - $m8f - $m8f$y - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] - 0 0 - - - $m8g - $m8g$y - (Intercept) C1 B21 c1 c2 time - 0 0 0 0 0 0 - B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] - 0 0 - - - $m8h - $m8h$y - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] - 0 0 - - - $m8i - $m8i$y - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] - 0 0 - - - $m8j - $m8j$y - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] - 0 0 - - - $m8k - $m8k$y - (Intercept) C1 B21 c2 c1 time - 0 0 0 0 0 0 - B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] - 0 0 - - - $m8l - $m8l$y - (Intercept) C1 B21 c1 time B21:c1 - 0 0 0 0 0 0 - B21:time c1:time B21:c1:time sigma_y D_y_id[1,1] D_y_id[1,2] - 0 0 0 0 0 0 - D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] - 0 0 0 0 - - - $m8m - $m8m$y - (Intercept) c1 b11 o1.L o1.Q c1:b11 - 0 0 0 0 0 0 - sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] - 0 0 0 0 - - - $m8n - $m8n$y - (Intercept) C1 B21 c1 time b11 - 0 0 0 0 0 0 - C1:time sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] - 0 0 0 0 0 0 - D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] D_y_id[3,4] D_y_id[4,4] - 0 0 0 0 0 0 - - - $m9a - $m9a$y - (Intercept) c1 b11 time sigma_y D_y_id[1,1] - 0 0 0 0 0 0 - D_y_o1[1,1] - 0 - - - $m9b - $m9b$y - (Intercept) C1 C2 B11 time sigma_y - 0 0 0 0 0 0 - D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] - 0 0 0 - - - $m9c - $m9c$y - (Intercept) C1 C2 B11 sigma_y D_y_id[1,1] - 0 0 0 0 0 0 - - - ---- - - Code - lapply(models0, confint) - Output - $m0a1 - $m0a1$y - 2.5% 97.5% - (Intercept) 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - $m0a2 - $m0a2$y - 2.5% 97.5% - (Intercept) 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - $m0a3 - $m0a3$y - 2.5% 97.5% - (Intercept) 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - $m0a4 - $m0a4$y - 2.5% 97.5% - (Intercept) 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - $m0b1 - $m0b1$b1 - 2.5% 97.5% - (Intercept) 0 0 - D_b1_id[1,1] 0 0 - - - $m0b2 - $m0b2$b1 - 2.5% 97.5% - (Intercept) 0 0 - D_b1_id[1,1] 0 0 - - - $m0b3 - $m0b3$b1 - 2.5% 97.5% - (Intercept) 0 0 - D_b1_id[1,1] 0 0 - - - $m0b4 - $m0b4$b1 - 2.5% 97.5% - (Intercept) 0 0 - D_b1_id[1,1] 0 0 - - - $m0c1 - $m0c1$L1 - 2.5% 97.5% - (Intercept) 0 0 - sigma_L1 0 0 - D_L1_id[1,1] 0 0 - - - $m0c2 - $m0c2$L1 - 2.5% 97.5% - (Intercept) 0 0 - sigma_L1 0 0 - D_L1_id[1,1] 0 0 - - - $m0d1 - $m0d1$p1 - 2.5% 97.5% - (Intercept) 0 0 - D_p1_id[1,1] 0 0 - - - $m0d2 - $m0d2$p1 - 2.5% 97.5% - (Intercept) 0 0 - D_p1_id[1,1] 0 0 - - - $m0e1 - $m0e1$L1 - 2.5% 97.5% - (Intercept) 0 0 - sigma_L1 0 0 - D_L1_id[1,1] 0 0 - - - $m0f1 - $m0f1$Be1 - 2.5% 97.5% - (Intercept) 0 0 - tau_Be1 0 0 - D_Be1_id[1,1] 0 0 - - - $m1a - $m1a$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - $m1b - $m1b$b1 - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - D_b1_id[1,1] 0 0 - - - $m1c - $m1c$L1 - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - sigma_L1 0 0 - D_L1_id[1,1] 0 0 - - - $m1d - $m1d$p1 - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - D_p1_id[1,1] 0 0 - - - $m1e - $m1e$L1 - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - sigma_L1 0 0 - D_L1_id[1,1] 0 0 - - - $m1f - $m1f$Be1 - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - tau_Be1 0 0 - D_Be1_id[1,1] 0 0 - - - $m2a - $m2a$y - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - $m2b - $m2b$b2 - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - D_b2_id[1,1] 0 0 - - - $m2c - $m2c$L1mis - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - sigma_L1mis 0 0 - D_L1mis_id[1,1] 0 0 - - - $m2d - $m2d$p2 - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - D_p2_id[1,1] 0 0 - - - $m2e - $m2e$L1mis - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - sigma_L1mis 0 0 - D_L1mis_id[1,1] 0 0 - - - $m2f - $m2f$Be2 - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - tau_Be2 0 0 - D_Be2_id[1,1] 0 0 - - - $m3a - $m3a$y - 2.5% 97.5% - C2 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - $m3b - $m3b$b2 - 2.5% 97.5% - C2 0 0 - D_b2_id[1,1] 0 0 - - - $m3c - $m3c$L1mis - 2.5% 97.5% - C2 0 0 - sigma_L1mis 0 0 - D_L1mis_id[1,1] 0 0 - - - $m3d - $m3d$p2 - 2.5% 97.5% - C2 0 0 - D_p2_id[1,1] 0 0 - - - $m3e - $m3e$L1mis - 2.5% 97.5% - C2 0 0 - sigma_L1mis 0 0 - D_L1mis_id[1,1] 0 0 - - - $m3f - $m3f$Be2 - 2.5% 97.5% - C2 0 0 - tau_Be2 0 0 - D_Be2_id[1,1] 0 0 - - - $m4a - $m4a$c1 - 2.5% 97.5% - (Intercept) 0 0 - B21 0 0 - c2 0 0 - p2 0 0 - L1mis 0 0 - Be2 0 0 - sigma_c1 0 0 - D_c1_id[1,1] 0 0 - - - $m4b - $m4b$c1 - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - b21 0 0 - p2 0 0 - L1mis 0 0 - sigma_c1 0 0 - D_c1_id[1,1] 0 0 - - - $m4c - $m4c$c1 - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - b21 0 0 - p2 0 0 - L1mis 0 0 - sigma_c1 0 0 - D_c1_id[1,1] 0 0 - - - $m4d - $m4d$c1 - 2.5% 97.5% - (Intercept) 0 0 - c2 0 0 - b21 0 0 - p2 0 0 - L1mis 0 0 - Be2 0 0 - sigma_c1 0 0 - D_c1_id[1,1] 0 0 - - - $m5a - $m5a$y - 2.5% 97.5% - (Intercept) 0 0 - M22 0 0 - M23 0 0 - M24 0 0 - log(C1) 0 0 - o22 0 0 - o23 0 0 - o24 0 0 - abs(C1 - c2) 0 0 - time 0 0 - I(time^2) 0 0 - o22:abs(C1 - c2) 0 0 - o23:abs(C1 - c2) 0 0 - o24:abs(C1 - c2) 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - - - $m5b - $m5b$b1 - 2.5% 97.5% - (Intercept) 0 0 - L1mis 0 0 - abs(c1 - C2) 0 0 - log(Be2) 0 0 - time 0 0 - D_b1_id[1,1] 0 0 - D_b1_id[1,2] 0 0 - D_b1_id[2,2] 0 0 - D_b1_id[1,3] 0 0 - D_b1_id[2,3] 0 0 - D_b1_id[3,3] 0 0 - - - $m6a - $m6a$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - C2 0 0 - b21 0 0 - time 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - $m6b - $m6b$b1 - 2.5% 97.5% - (Intercept) 0 0 - C2 0 0 - B11 0 0 - c1 0 0 - time 0 0 - D_b1_id[1,1] 0 0 - D_b1_id[1,2] 0 0 - D_b1_id[2,2] 0 0 - - - $m7a - $m7a$y - 2.5% 97.5% - (Intercept) 0 0 - ns(time, df = 2)1 0 0 - ns(time, df = 2)2 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m7b - $m7b$y - 2.5% 97.5% - (Intercept) 0 0 - bs(time, df = 3)1 0 0 - bs(time, df = 3)2 0 0 - bs(time, df = 3)3 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - D_y_id[1,4] 0 0 - D_y_id[2,4] 0 0 - D_y_id[3,4] 0 0 - D_y_id[4,4] 0 0 - - - $m7c - $m7c$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - c1 0 0 - ns(time, df = 3)1 0 0 - ns(time, df = 3)2 0 0 - ns(time, df = 3)3 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - D_y_id[1,4] 0 0 - D_y_id[2,4] 0 0 - D_y_id[3,4] 0 0 - D_y_id[4,4] 0 0 - - - $m7d - $m7d$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - C2 0 0 - c1 0 0 - ns(time, df = 3)1 0 0 - ns(time, df = 3)2 0 0 - ns(time, df = 3)3 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - - - $m7e - $m7e$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - C2 0 0 - c1 0 0 - ns(time, df = 3)1 0 0 - ns(time, df = 3)2 0 0 - ns(time, df = 3)3 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - D_y_id[1,4] 0 0 - D_y_id[2,4] 0 0 - D_y_id[3,4] 0 0 - D_y_id[4,4] 0 0 - - - $m7f - $m7f$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - C2 0 0 - c1 0 0 - ns(time, df = 3)1 0 0 - ns(time, df = 3)2 0 0 - ns(time, df = 3)3 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - - - $m8a - $m8a$y - 2.5% 97.5% - (Intercept) 0 0 - c1 0 0 - c2 0 0 - time 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8b - $m8b$y - 2.5% 97.5% - (Intercept) 0 0 - c1 0 0 - c2 0 0 - time 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8c - $m8c$y - 2.5% 97.5% - (Intercept) 0 0 - B21 0 0 - c1 0 0 - c2 0 0 - time 0 0 - B21:c1 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8d - $m8d$y - 2.5% 97.5% - (Intercept) 0 0 - B21 0 0 - c1 0 0 - c2 0 0 - time 0 0 - B21:c1 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8e - $m8e$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c1 0 0 - c2 0 0 - time 0 0 - B21:c1 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8f - $m8f$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c1 0 0 - c2 0 0 - time 0 0 - B21:c1 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8g - $m8g$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c1 0 0 - c2 0 0 - time 0 0 - B21:c1 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8h - $m8h$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c2 0 0 - c1 0 0 - time 0 0 - B21:c2 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8i - $m8i$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c2 0 0 - c1 0 0 - time 0 0 - B21:c2 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8j - $m8j$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c2 0 0 - c1 0 0 - time 0 0 - B21:c2 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8k - $m8k$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c2 0 0 - c1 0 0 - time 0 0 - B21:c2 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8l - $m8l$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c1 0 0 - time 0 0 - B21:c1 0 0 - B21:time 0 0 - c1:time 0 0 - B21:c1:time 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - - - $m8m - $m8m$y - 2.5% 97.5% - (Intercept) 0 0 - c1 0 0 - b11 0 0 - o1.L 0 0 - o1.Q 0 0 - c1:b11 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - - - $m8n - $m8n$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - B21 0 0 - c1 0 0 - time 0 0 - b11 0 0 - C1:time 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - D_y_id[1,3] 0 0 - D_y_id[2,3] 0 0 - D_y_id[3,3] 0 0 - D_y_id[1,4] 0 0 - D_y_id[2,4] 0 0 - D_y_id[3,4] 0 0 - D_y_id[4,4] 0 0 - - - $m9a - $m9a$y - 2.5% 97.5% - (Intercept) 0 0 - c1 0 0 - b11 0 0 - time 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_o1[1,1] 0 0 - - - $m9b - $m9b$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - C2 0 0 - B11 0 0 - time 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - D_y_id[1,2] 0 0 - D_y_id[2,2] 0 0 - - - $m9c - $m9c$y - 2.5% 97.5% - (Intercept) 0 0 - C1 0 0 - C2 0 0 - B11 0 0 - sigma_y 0 0 - D_y_id[1,1] 0 0 - - - ---- - - Code - lapply(models0, summary, missinfo = TRUE) - Output - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - $m0a1 - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 1:10 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0a2 - - Bayesian linear mixed model fitted with JointAI - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 1:10 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0a3 - - Bayesian linear mixed model fitted with JointAI - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0a4 - - Bayesian linear mixed model fitted with JointAI - - Call: - glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0b1 - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b1_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - b1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0b2 - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b1_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - b1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0b3 - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), - n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b1_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 51:60 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - b1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0b4 - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), - n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b1_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 51:60 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - b1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0c1 - - Bayesian Gamma mixed model fitted with JointAI - - Call: - glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - L1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0c2 - - Bayesian Gamma mixed model fitted with JointAI - - Call: - glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - L1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0d1 - - Bayesian poisson mixed model fitted with JointAI - - Call: - glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_p1_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - p1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0d2 - - Bayesian poisson mixed model fitted with JointAI - - Call: - glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_p1_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - p1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0e1 - - Bayesian log-normal mixed model fitted with JointAI - - Call: - lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - L1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m0f1 - - Bayesian beta mixed model fitted with JointAI - - Call: - betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_Be1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - tau_Be1 0 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - Be1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m1a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 1:10 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - - - $m1b - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b1_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - b1 lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - - - $m1c - - Bayesian Gamma mixed model fitted with JointAI - - Call: - glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - L1 lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - - - $m1d - - Bayesian poisson mixed model fitted with JointAI - - Call: - glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_p1_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - p1 lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - - - $m1e - - Bayesian log-normal mixed model fitted with JointAI - - Call: - lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - L1 lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - - - $m1f - - Bayesian beta mixed model fitted with JointAI - - Call: - betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_Be1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - tau_Be1 0 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - Be1 lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - - - $m2a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 1:10 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - id id 0 0 - - - $m2b - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b2_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 189 57.4 - - Number and proportion of missing values: - level # NA % NA - c2 lvlone 66 20.1 - b2 lvlone 99 30.1 - - level # NA % NA - id id 0 0 - - - $m2c - - Bayesian Gamma mixed model fitted with JointAI - - Call: - glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1mis_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1mis 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 246 74.8 - - Number and proportion of missing values: - level # NA % NA - L1mis lvlone 20 6.08 - c2 lvlone 66 20.06 - - level # NA % NA - id id 0 0 - - - $m2d - - Bayesian poisson mixed model fitted with JointAI - - Call: - glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_p2_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 142 43.2 - - Number and proportion of missing values: - level # NA % NA - c2 lvlone 66 20.1 - p2 lvlone 162 49.2 - - level # NA % NA - id id 0 0 - - - $m2e - - Bayesian log-normal mixed model fitted with JointAI - - Call: - lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1mis_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1mis 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 246 74.8 - - Number and proportion of missing values: - level # NA % NA - L1mis lvlone 20 6.08 - c2 lvlone 66 20.06 - - level # NA % NA - id id 0 0 - - - $m2f - - Bayesian beta mixed model fitted with JointAI - - Call: - betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_Be2_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - tau_Be2 0 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 246 74.8 - - Number and proportion of missing values: - level # NA % NA - Be2 lvlone 20 6.08 - c2 lvlone 66 20.06 - - level # NA % NA - id id 0 0 - - - $m3a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 1:10 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - - level # NA % NA - id id 0 0 - C2 id 42 42 - - - $m3b - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b2_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58.0 - lvlone lvlone 230 69.9 - - Number and proportion of missing values: - level # NA % NA - b2 lvlone 99 30.1 - - level # NA % NA - id id 0 0 - C2 id 42 42 - - - $m3c - - Bayesian Gamma mixed model fitted with JointAI - - Call: - glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1mis_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1mis 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58.0 - lvlone lvlone 309 93.9 - - Number and proportion of missing values: - level # NA % NA - L1mis lvlone 20 6.08 - - level # NA % NA - id id 0 0 - C2 id 42 42 - - - $m3d - - Bayesian poisson mixed model fitted with JointAI - - Call: - glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_p2_id[1,1] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58.0 - lvlone lvlone 167 50.8 - - Number and proportion of missing values: - level # NA % NA - p2 lvlone 162 49.2 - - level # NA % NA - id id 0 0 - C2 id 42 42 - - - $m3e - - Bayesian log-normal mixed model fitted with JointAI - - Call: - lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_L1mis_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_L1mis 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58.0 - lvlone lvlone 309 93.9 - - Number and proportion of missing values: - level # NA % NA - L1mis lvlone 20 6.08 - - level # NA % NA - id id 0 0 - C2 id 42 42 - - - $m3f - - Bayesian beta mixed model fitted with JointAI - - Call: - betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_Be2_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of other parameters: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - tau_Be2 0 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58.0 - lvlone lvlone 309 93.9 - - Number and proportion of missing values: - level # NA % NA - Be2 lvlone 20 6.08 - - level # NA % NA - id id 0 0 - C2 id 42 42 - - - $m4a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", - L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - p2 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - Be2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_c1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_c1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90 - lvlone lvlone 125 38 - - Number and proportion of missing values: - level # NA % NA - c1 lvlone 0 0.00 - L1mis lvlone 20 6.08 - Be2 lvlone 20 6.08 - c2 lvlone 66 20.06 - p2 lvlone 162 49.24 - - level # NA % NA - id id 0 0 - B2 id 10 10 - - - $m4b - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", - p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", - L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - b21 0 0 0 0 0 NaN NaN - p2 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_c1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_c1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 98 29.8 - - Number and proportion of missing values: - level # NA % NA - c1 lvlone 0 0.00 - L1mis lvlone 20 6.08 - c2 lvlone 66 20.06 - b2 lvlone 99 30.09 - p2 lvlone 162 49.24 - - level # NA % NA - id id 0 0 - - - $m4c - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", - p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", - b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - b21 0 0 0 0 0 NaN NaN - p2 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_c1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_c1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 98 29.8 - - Number and proportion of missing values: - level # NA % NA - c1 lvlone 0 0.00 - L1mis lvlone 20 6.08 - c2 lvlone 66 20.06 - b2 lvlone 99 30.09 - p2 lvlone 162 49.24 - - level # NA % NA - id id 0 0 - - - $m4d - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, - n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", - p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", - b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, - warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1))) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - b21 0 0 0 0 0 NaN NaN - p2 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - Be2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_c1_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_c1 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 92 28 - - Number and proportion of missing values: - level # NA % NA - c1 lvlone 0 0.00 - L1mis lvlone 20 6.08 - Be2 lvlone 20 6.08 - c2 lvlone 66 20.06 - b2 lvlone 99 30.09 - p2 lvlone 162 49.24 - - level # NA % NA - id id 0 0 - - - $m5a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + - I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, - seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - M22 0 0 0 0 0 NaN NaN - M23 0 0 0 0 0 NaN NaN - M24 0 0 0 0 0 NaN NaN - log(C1) 0 0 0 0 0 NaN NaN - o22 0 0 0 0 0 NaN NaN - o23 0 0 0 0 0 NaN NaN - o24 0 0 0 0 0 NaN NaN - abs(C1 - c2) 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - I(time^2) 0 0 0 0 0 NaN NaN - o22:abs(C1 - c2) 0 0 0 0 0 NaN NaN - o23:abs(C1 - c2) 0 0 0 0 0 NaN NaN - o24:abs(C1 - c2) 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 56 56 - lvlone lvlone 217 66 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - time lvlone 0 0.0 - o2 lvlone 59 17.9 - c2 lvlone 66 20.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - M2 id 44 44 - - - $m5b - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + - (time + I(time^2) | id), data = longDF, family = binomial(), - n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", - L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", - seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - abs(c1 - C2) 0 0 0 0 0 NaN NaN - log(Be2) 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b1_id[1,1] 0 0 0 0 NaN NaN - D_b1_id[1,2] 0 0 0 0 0 NaN NaN - D_b1_id[2,2] 0 0 0 0 NaN NaN - D_b1_id[1,3] 0 0 0 0 0 NaN NaN - D_b1_id[2,3] 0 0 0 0 0 NaN NaN - D_b1_id[3,3] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58.0 - lvlone lvlone 291 88.4 - - Number and proportion of missing values: - level # NA % NA - b1 lvlone 0 0.00 - c1 lvlone 0 0.00 - time lvlone 0 0.00 - L1mis lvlone 20 6.08 - Be2 lvlone 20 6.08 - - level # NA % NA - id id 0 0 - C2 id 42 42 - - - $m6a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, - n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - b21 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58.0 - lvlone lvlone 230 69.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - time lvlone 0 0.0 - b2 lvlone 99 30.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - C2 id 42 42 - - - $m6b - - Bayesian binomial mixed model fitted with JointAI - - Call: - glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | - id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, - shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - B11 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_b1_id[1,1] 0 0 0 0 NaN NaN - D_b1_id[1,2] 0 0 0 0 0 NaN NaN - D_b1_id[2,2] 0 0 0 0 NaN NaN - - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - b1 lvlone 0 0 - c1 lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - B1 id 0 0 - id id 0 0 - C2 id 42 42 - - - $m7a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, - df = 2) | id, n.iter = 10, seed = 2020, adapt = 5) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - ns(time, df = 2)1 0 0 0 0 0 NaN NaN - ns(time, df = 2)2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 101:110 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m7b - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, - df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - bs(time, df = 3)1 0 0 0 0 0 NaN NaN - bs(time, df = 3)2 0 0 0 0 0 NaN NaN - bs(time, df = 3)3 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - D_y_id[1,4] 0 0 0 0 0 NaN NaN - D_y_id[2,4] 0 0 0 0 0 NaN NaN - D_y_id[3,4] 0 0 0 0 0 NaN NaN - D_y_id[4,4] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m7c - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, - random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, - nadapt = 5) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - ns(time, df = 3)1 0 0 0 0 0 NaN NaN - ns(time, df = 3)2 0 0 0 0 0 NaN NaN - ns(time, df = 3)3 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - D_y_id[1,4] 0 0 0 0 0 NaN NaN - D_y_id[2,4] 0 0 0 0 0 NaN NaN - D_y_id[3,4] 0 0 0 0 0 NaN NaN - D_y_id[4,4] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 101:110 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - c1 lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - - - $m7d - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - ns(time, df = 3)1 0 0 0 0 0 NaN NaN - ns(time, df = 3)2 0 0 0 0 0 NaN NaN - ns(time, df = 3)3 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - c1 lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - C2 id 42 42 - - - $m7e - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, - no_model = "time", seed = 2020) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - ns(time, df = 3)1 0 0 0 0 0 NaN NaN - ns(time, df = 3)2 0 0 0 0 0 NaN NaN - ns(time, df = 3)3 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - D_y_id[1,4] 0 0 0 0 0 NaN NaN - D_y_id[2,4] 0 0 0 0 0 NaN NaN - D_y_id[3,4] 0 0 0 0 0 NaN NaN - D_y_id[4,4] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - c1 lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - C2 id 42 42 - - - $m7f - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, - random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - ns(time, df = 3)1 0 0 0 0 0 NaN NaN - ns(time, df = 3)2 0 0 0 0 0 NaN NaN - ns(time, df = 3)3 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - c1 lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - C2 id 42 42 - - - $m8a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + - c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - Warning - - There are missing values in a variable for which a random effect is - specified ("c2"). It will not be possible to re-scale the random - effects "b_y_id" and their variance covariance matrix "D_y_id" back to - the original scale of the data. If you are not interested in the - estimated random effects or their (co)variances this is not a problem. - The fixed effects estimates are not affected by this. If you are - interested in the random effects or the (co)variances you need to - specify that "time" and "c2" are not scaled (using the argument - "scale_params"). - Output - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - id id 0 0 - - - $m8b - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + - c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - Warning - - There are missing values in a variable for which a random effect is - specified ("c2"). It will not be possible to re-scale the random - effects "b_y_id" and their variance covariance matrix "D_y_id" back to - the original scale of the data. If you are not interested in the - estimated random effects or their (co)variances this is not a problem. - The fixed effects estimates are not affected by this. If you are - interested in the random effects or the (co)variances you need to - specify that "time" and "c2" are not scaled (using the argument - "scale_params"). - Output - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - id id 0 0 - - - $m8c - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + - c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - id id 0 0 - B2 id 10 10 - - - $m8d - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + - c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - id id 0 0 - B2 id 10 10 - - - $m8e - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - Warning - - There are missing values in a variable for which a random effect is - specified ("c2"). It will not be possible to re-scale the random - effects "b_y_id" and their variance covariance matrix "D_y_id" back to - the original scale of the data. If you are not interested in the - estimated random effects or their (co)variances this is not a problem. - The fixed effects estimates are not affected by this. If you are - interested in the random effects or the (co)variances you need to - specify that "time" and "c2" are not scaled (using the argument - "scale_params"). - Output - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m8f - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", - seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - Warning - - There are missing values in a variable for which a random effect is - specified ("c2"). It will not be possible to re-scale the random - effects "b_y_id" and their variance covariance matrix "D_y_id" back to - the original scale of the data. If you are not interested in the - estimated random effects or their (co)variances this is not a problem. - The fixed effects estimates are not affected by this. If you are - interested in the random effects or the (co)variances you need to - specify that "time" and "c2" are not scaled (using the argument - "scale_params"). - Output - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m8g - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", - "c1"), seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - Warning - - There are missing values in a variable for which a random effect is - specified ("c2"). It will not be possible to re-scale the random - effects "b_y_id" and their variance covariance matrix "D_y_id" back to - the original scale of the data. If you are not interested in the - estimated random effects or their (co)variances this is not a problem. - The fixed effects estimates are not affected by this. If you are - interested in the random effects or the (co)variances you need to - specify that "time" and "c2" are not scaled (using the argument - "scale_params"). - Output - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m8h - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m8i - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", - seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m8j - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - Warning - - There are missing values in a variable for which a random effect is - specified ("c2"). It will not be possible to re-scale the random - effects "b_y_id" and their variance covariance matrix "D_y_id" back to - the original scale of the data. If you are not interested in the - estimated random effects or their (co)variances this is not a problem. - The fixed effects estimates are not affected by this. If you are - interested in the random effects or the (co)variances you need to - specify that "time" and "c2" are not scaled (using the argument - "scale_params"). - Output - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m8k - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, - random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c2 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - Warning - - There are missing values in a variable for which a random effect is - specified ("c2"). It will not be possible to re-scale the random - effects "b_y_id" and their variance covariance matrix "D_y_id" back to - the original scale of the data. If you are not interested in the - estimated random effects or their (co)variances this is not a problem. - The fixed effects estimates are not affected by this. If you are - interested in the random effects or the (co)variances you need to - specify that "time" and "c2" are not scaled (using the argument - "scale_params"). - Output - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90.0 - lvlone lvlone 263 79.9 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0.0 - c1 lvlone 0 0.0 - time lvlone 0 0.0 - c2 lvlone 66 20.1 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m8l - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + - I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - B21:time 0 0 0 0 0 NaN NaN - c1:time 0 0 0 0 0 NaN NaN - B21:c1:time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - c1 lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m8m - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | - id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, - mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - b11 0 0 0 0 0 NaN NaN - o1.L 0 0 0 0 0 NaN NaN - o1.Q 0 0 0 0 0 NaN NaN - c1:b11 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 100 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - c1 lvlone 0 0 - b1 lvlone 0 0 - o1 lvlone 0 0 - - level # NA % NA - id id 0 0 - - - $m8n - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, - random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, - warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - b11 0 0 0 0 0 NaN NaN - C1:time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - D_y_id[1,3] 0 0 0 0 0 NaN NaN - D_y_id[2,3] 0 0 0 0 0 NaN NaN - D_y_id[3,3] 0 0 0 0 NaN NaN - D_y_id[1,4] 0 0 0 0 0 NaN NaN - D_y_id[2,4] 0 0 0 0 0 NaN NaN - D_y_id[3,4] 0 0 0 0 0 NaN NaN - D_y_id[4,4] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 90 90 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - c1 lvlone 0 0 - time lvlone 0 0 - b1 lvlone 0 0 - - level # NA % NA - C1 id 0 0 - id id 0 0 - B2 id 10 10 - - - $m9a - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, - n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - b11 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - - * For level "id": - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - * For level "o1": - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_o1[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 1:10 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - o1: 3 - - - Number and proportion of complete cases: - level # % - id id 100 100 - o1 o1 3 100 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - c1 lvlone 0 0 - b1 lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - id id 0 0 - - level # NA % NA - o1 o1 0 0 - - - $m9b - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, - n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), - seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - B11 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - D_y_id[1,2] 0 0 0 0 0 NaN NaN - D_y_id[2,2] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 6:15 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - time lvlone 0 0 - - level # NA % NA - C1 id 0 0 - B1 id 0 0 - id id 0 0 - C2 id 42 42 - - - $m9c - - Bayesian linear mixed model fitted with JointAI - - Call: - lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, - n.iter = 10, monitor_params = c(analysis_random = TRUE), - seed = 2020, warn = FALSE, mess = FALSE) - - - Posterior summary: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - B11 0 0 0 0 0 NaN NaN - - - Posterior summary of random effects covariance matrix: - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - D_y_id[1,1] 0 0 0 0 NaN NaN - - - Posterior summary of residual std. deviation: - Mean SD 2.5% 97.5% GR-crit MCE/SD - sigma_y 0 0 0 0 NaN NaN - - - MCMC settings: - Iterations = 1:10 - Sample size per chain = 10 - Thinning interval = 1 - Number of chains = 3 - - Number of observations: 329 - Number of groups: - - id: 100 - - - Number and proportion of complete cases: - level # % - id id 58 58 - lvlone lvlone 329 100 - - Number and proportion of missing values: - level # NA % NA - y lvlone 0 0 - - level # NA % NA - C1 id 0 0 - B1 id 0 0 - id id 0 0 - C2 id 42 42 - - - ---- - - Code - lapply(models0, function(x) coef(summary(x))) - Output - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - [1] "No variability observed in a component. Setting batch size to 1" - $m0a1 - $m0a1$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0a2 - $m0a2$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0a3 - $m0a3$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0a4 - $m0a4$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0b1 - $m0b1$b1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0b2 - $m0b2$b1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0b3 - $m0b3$b1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0b4 - $m0b4$b1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0c1 - $m0c1$L1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0c2 - $m0c2$L1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0d1 - $m0d1$p1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0d2 - $m0d2$p1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0e1 - $m0e1$L1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m0f1 - $m0f1$Be1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - - - $m1a - $m1a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - $m1b - $m1b$b1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - $m1c - $m1c$L1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - $m1d - $m1d$p1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - $m1e - $m1e$L1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - $m1f - $m1f$Be1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - - - $m2a - $m2a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - $m2b - $m2b$b2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - $m2c - $m2c$L1mis - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - $m2d - $m2d$p2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - $m2e - $m2e$L1mis - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - $m2f - $m2f$Be2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - - - $m3a - $m3a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - $m3b - $m3b$b2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - $m3c - $m3c$L1mis - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - $m3d - $m3d$p2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - $m3e - $m3e$L1mis - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - $m3f - $m3f$Be2 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - C2 0 0 0 0 0 NaN NaN - - - $m4a - $m4a$c1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - p2 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - Be2 0 0 0 0 0 NaN NaN - - - $m4b - $m4b$c1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - b21 0 0 0 0 0 NaN NaN - p2 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - - - $m4c - $m4c$c1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - b21 0 0 0 0 0 NaN NaN - p2 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - - - $m4d - $m4d$c1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - b21 0 0 0 0 0 NaN NaN - p2 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - Be2 0 0 0 0 0 NaN NaN - - - $m5a - $m5a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - M22 0 0 0 0 0 NaN NaN - M23 0 0 0 0 0 NaN NaN - M24 0 0 0 0 0 NaN NaN - log(C1) 0 0 0 0 0 NaN NaN - o22 0 0 0 0 0 NaN NaN - o23 0 0 0 0 0 NaN NaN - o24 0 0 0 0 0 NaN NaN - abs(C1 - c2) 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - I(time^2) 0 0 0 0 0 NaN NaN - o22:abs(C1 - c2) 0 0 0 0 0 NaN NaN - o23:abs(C1 - c2) 0 0 0 0 0 NaN NaN - o24:abs(C1 - c2) 0 0 0 0 0 NaN NaN - - - $m5b - $m5b$b1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - L1mis 0 0 0 0 0 NaN NaN - abs(c1 - C2) 0 0 0 0 0 NaN NaN - log(Be2) 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - $m6a - $m6a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - b21 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - $m6b - $m6b$b1 - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - B11 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - $m7a - $m7a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - ns(time, df = 2)1 0 0 0 0 0 NaN NaN - ns(time, df = 2)2 0 0 0 0 0 NaN NaN - - - $m7b - $m7b$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - bs(time, df = 3)1 0 0 0 0 0 NaN NaN - bs(time, df = 3)2 0 0 0 0 0 NaN NaN - bs(time, df = 3)3 0 0 0 0 0 NaN NaN - - - $m7c - $m7c$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - ns(time, df = 3)1 0 0 0 0 0 NaN NaN - ns(time, df = 3)2 0 0 0 0 0 NaN NaN - ns(time, df = 3)3 0 0 0 0 0 NaN NaN - - - $m7d - $m7d$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - ns(time, df = 3)1 0 0 0 0 0 NaN NaN - ns(time, df = 3)2 0 0 0 0 0 NaN NaN - ns(time, df = 3)3 0 0 0 0 0 NaN NaN - - - $m7e - $m7e$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - ns(time, df = 3)1 0 0 0 0 0 NaN NaN - ns(time, df = 3)2 0 0 0 0 0 NaN NaN - ns(time, df = 3)3 0 0 0 0 0 NaN NaN - - - $m7f - $m7f$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - ns(time, df = 3)1 0 0 0 0 0 NaN NaN - ns(time, df = 3)2 0 0 0 0 0 NaN NaN - ns(time, df = 3)3 0 0 0 0 0 NaN NaN - - - $m8a - $m8a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - $m8b - $m8b$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - $m8c - $m8c$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - $m8d - $m8d$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - $m8e - $m8e$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - $m8f - $m8f$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - $m8g - $m8g$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - - - $m8h - $m8h$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c2 0 0 0 0 0 NaN NaN - - - $m8i - $m8i$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c2 0 0 0 0 0 NaN NaN - - - $m8j - $m8j$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c2 0 0 0 0 0 NaN NaN - - - $m8k - $m8k$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c2 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c2 0 0 0 0 0 NaN NaN - - - $m8l - $m8l$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - B21:c1 0 0 0 0 0 NaN NaN - B21:time 0 0 0 0 0 NaN NaN - c1:time 0 0 0 0 0 NaN NaN - B21:c1:time 0 0 0 0 0 NaN NaN - - - $m8m - $m8m$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - b11 0 0 0 0 0 NaN NaN - o1.L 0 0 0 0 0 NaN NaN - o1.Q 0 0 0 0 0 NaN NaN - c1:b11 0 0 0 0 0 NaN NaN - - - $m8n - $m8n$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - B21 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - b11 0 0 0 0 0 NaN NaN - C1:time 0 0 0 0 0 NaN NaN - - - $m9a - $m9a$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - c1 0 0 0 0 0 NaN NaN - b11 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - $m9b - $m9b$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - B11 0 0 0 0 0 NaN NaN - time 0 0 0 0 0 NaN NaN - - - $m9c - $m9c$y - Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD - (Intercept) 0 0 0 0 0 NaN NaN - C1 0 0 0 0 0 NaN NaN - C2 0 0 0 0 0 NaN NaN - B11 0 0 0 0 0 NaN NaN - - -

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