updated default dataverse

R/fn_get.R

@@ -44,7 +44,7 @@ get_dv_mdl_smrys <- function (mdls_lup, mdl_nms_chr = NULL)
 #' @param ttu_dv_dss_tb Ttu dataverse datasets (a tibble), Default: NULL
 #' @param mdl_predrs_in_ds_chr Model predictors in dataset (a character vector), Default: NULL
 #' @param utility_type_chr Utility type (a character vector), Default: NULL
-#' @param ttu_dv_nms_chr Ttu dataverse names (a character vector), Default: 'firstbounce'
+#' @param ttu_dv_nms_chr Ttu dataverse names (a character vector), Default: 'TTU'
 #' @param server_1L_chr Server (a character vector of length one), Default: 'dataverse.harvard.edu'
 #' @param key_1L_chr Key (a character vector of length one), Default: NULL
 #' @return Ttu dataverse datasets (a tibble)
@@ -54,8 +54,8 @@ get_dv_mdl_smrys <- function (mdls_lup, mdl_nms_chr = NULL)
 #' @importFrom purrr map_lgl
 #' @keywords internal
 get_filtered_ttu_dss <- function (ttu_dv_dss_tb = NULL, mdl_predrs_in_ds_chr = NULL, 
- utility_type_chr = NULL, ttu_dv_nms_chr = "firstbounce", 
- server_1L_chr = "dataverse.harvard.edu", key_1L_chr = NULL) 
+ utility_type_chr = NULL, ttu_dv_nms_chr = "TTU", server_1L_chr = "dataverse.harvard.edu", 
+ key_1L_chr = NULL) 
 {
  if (is.null(ttu_dv_dss_tb)) 
  ttu_dv_dss_tb <- get_ttu_dv_dss(ttu_dv_nms_chr = ttu_dv_nms_chr, 
@@ -202,7 +202,7 @@ get_mdl_smrys <- function (ingredients_ls, mdl_nms_chr = NULL)
 #' @param ttu_dv_dss_tb Ttu dataverse datasets (a tibble), Default: NULL
 #' @param mdl_predrs_in_ds_chr Model predictors in dataset (a character vector), Default: NULL
 #' @param utility_type_chr Utility type (a character vector), Default: NULL
-#' @param ttu_dv_nms_chr Ttu dataverse names (a character vector), Default: 'firstbounce'
+#' @param ttu_dv_nms_chr Ttu dataverse names (a character vector), Default: 'TTU'
 #' @param server_1L_chr Server (a character vector of length one), Default: 'dataverse.harvard.edu'
 #' @param key_1L_chr Key (a character vector of length one), Default: NULL
 #' @return Models (a lookup table)
@@ -213,8 +213,8 @@ get_mdl_smrys <- function (ingredients_ls, mdl_nms_chr = NULL)
 #' @importFrom dplyr filter mutate
 #' @keywords internal
 get_mdls_lup <- function (ttu_dv_dss_tb = NULL, mdl_predrs_in_ds_chr = NULL, 
- utility_type_chr = NULL, ttu_dv_nms_chr = "firstbounce", 
- server_1L_chr = "dataverse.harvard.edu", key_1L_chr = NULL) 
+ utility_type_chr = NULL, ttu_dv_nms_chr = "TTU", server_1L_chr = "dataverse.harvard.edu", 
+ key_1L_chr = NULL) 
 {
  if (is.null(ttu_dv_dss_tb)) 
  ttu_dv_dss_tb <- get_ttu_dv_dss(ttu_dv_nms_chr = ttu_dv_nms_chr, 
@@ -226,7 +226,7 @@ get_mdls_lup <- function (ttu_dv_dss_tb = NULL, mdl_predrs_in_ds_chr = NULL,
  mdl_predrs_in_ds_chr = mdl_predrs_in_ds_chr, utility_type_chr = utility_type_chr)
  }
  if (!is.null(ttu_dv_dss_tb)) {
- ds_smrys_ls <- get_ttu_ds_smrys("firstbounce", reference_int = ttu_dv_dss_tb$reference_int)
+ ds_smrys_ls <- get_ttu_ds_smrys("TTU", reference_int = ttu_dv_dss_tb$reference_int)
  mdls_lup <- ds_smrys_ls %>% purrr::map2_dfr(names(ds_smrys_ls), 
  ~{
  predictors_lup <- .x$predictors_lup
@@ -339,7 +339,7 @@ get_tfmn_from_lup <- function (mdl_nm_1L_chr, mdls_lup = NULL)
 }
 #' Get ttu dataset summarys
 #' @description get_ttu_ds_smrys() is a Get function that retrieves a pre-existing data object from memory, local file system or online repository. Specifically, this function implements an algorithm to get ttu dataset summarys. Function argument ttu_dv_nm_1L_chr specifies the where to look for the required object. The function returns Dataverse datasets model summarys (a list).
-#' @param ttu_dv_nm_1L_chr Ttu dataverse name (a character vector of length one), Default: 'firstbounce'
+#' @param ttu_dv_nm_1L_chr Ttu dataverse name (a character vector of length one), Default: 'TTU'
 #' @param server_1L_chr Server (a character vector of length one), Default: 'dataverse.harvard.edu'
 #' @param key_1L_chr Key (a character vector of length one), Default: NULL
 #' @param reference_int Reference (an integer vector), Default: NULL
@@ -350,7 +350,7 @@ get_tfmn_from_lup <- function (mdl_nm_1L_chr, mdls_lup = NULL)
 #' @importFrom purrr map_chr compact map pluck
 #' @importFrom stats setNames
 #' @keywords internal
-get_ttu_ds_smrys <- function (ttu_dv_nm_1L_chr = "firstbounce", server_1L_chr = "dataverse.harvard.edu", 
+get_ttu_ds_smrys <- function (ttu_dv_nm_1L_chr = "TTU", server_1L_chr = "dataverse.harvard.edu", 
  key_1L_chr = NULL, reference_int = NULL) 
 {
  ds_ls <- dataverse::dataverse_contents(ttu_dv_nm_1L_chr, 
@@ -367,7 +367,7 @@ get_ttu_ds_smrys <- function (ttu_dv_nm_1L_chr = "firstbounce", server_1L_chr =
 }
 #' Get ttu dataverse datasets
 #' @description get_ttu_dv_dss() is a Get function that retrieves a pre-existing data object from memory, local file system or online repository. Specifically, this function implements an algorithm to get ttu dataverse datasets. Function argument ttu_dv_nms_chr specifies the where to look for the required object. The function returns Ttu dataverse datasets (a tibble).
-#' @param ttu_dv_nms_chr Ttu dataverse names (a character vector), Default: 'firstbounce'
+#' @param ttu_dv_nms_chr Ttu dataverse names (a character vector), Default: 'TTU'
 #' @param server_1L_chr Server (a character vector of length one), Default: 'dataverse.harvard.edu'
 #' @param key_1L_chr Key (a character vector of length one), Default: NULL
 #' @return Ttu dataverse datasets (a tibble)
@@ -380,7 +380,7 @@ get_ttu_ds_smrys <- function (ttu_dv_nm_1L_chr = "firstbounce", server_1L_chr =
 #' @importFrom dplyr pull
 #' @importFrom ready4use add_labels_from_dictionary
 #' @keywords internal
-get_ttu_dv_dss <- function (ttu_dv_nms_chr = "firstbounce", server_1L_chr = "dataverse.harvard.edu", 
+get_ttu_dv_dss <- function (ttu_dv_nms_chr = "TTU", server_1L_chr = "dataverse.harvard.edu", 
  key_1L_chr = NULL) 
 {
  dv_dss_mdl_smrys_ls <- ttu_dv_nms_chr %>% purrr::map(~get_ttu_ds_smrys(.x, 
@@ -410,16 +410,16 @@ get_ttu_dv_dss <- function (ttu_dv_nms_chr = "firstbounce", server_1L_chr = "dat
 #' Get ttu dataverse predictors
 #' @description get_ttu_dv_predrs() is a Get function that retrieves a pre-existing data object from memory, local file system or online repository. Specifically, this function implements an algorithm to get ttu dataverse predictors. Function argument ttu_dv_dss_tb specifies the where to look for the required object. The function returns Predictors (a character vector).
 #' @param ttu_dv_dss_tb Ttu dataverse datasets (a tibble), Default: NULL
-#' @param ttu_dv_nms_chr Ttu dataverse names (a character vector), Default: 'firstbounce'
+#' @param ttu_dv_nms_chr Ttu dataverse names (a character vector), Default: 'TTU'
 #' @param server_1L_chr Server (a character vector of length one), Default: 'dataverse.harvard.edu'
 #' @param key_1L_chr Key (a character vector of length one), Default: NULL
 #' @return Predictors (a character vector)
 #' @rdname get_ttu_dv_predrs
 #' @export 
 #' @importFrom purrr flatten_chr
 #' @keywords internal
-get_ttu_dv_predrs <- function (ttu_dv_dss_tb = NULL, ttu_dv_nms_chr = "firstbounce", 
- server_1L_chr = "dataverse.harvard.edu", key_1L_chr = NULL) 
+get_ttu_dv_predrs <- function (ttu_dv_dss_tb = NULL, ttu_dv_nms_chr = "TTU", server_1L_chr = "dataverse.harvard.edu", 
+ key_1L_chr = NULL) 
 {
  if (is.null(ttu_dv_dss_tb)) 
  ttu_dv_dss_tb <- get_ttu_dv_dss(ttu_dv_nms_chr = ttu_dv_nms_chr, 

data-raw/fns/get.R

@@ -27,7 +27,7 @@ get_dv_mdl_smrys <- function(mdls_lup,
 get_filtered_ttu_dss <- function(ttu_dv_dss_tb = NULL,
  mdl_predrs_in_ds_chr = NULL,
  utility_type_chr = NULL,
- ttu_dv_nms_chr = "firstbounce",
+ ttu_dv_nms_chr = "TTU",
  server_1L_chr = "dataverse.harvard.edu",
  key_1L_chr = NULL){
  if(is.null(ttu_dv_dss_tb))
@@ -99,7 +99,7 @@ get_mdl_from_dv <- function(mdl_nm_1L_chr,
 get_mdls_lup <- function(ttu_dv_dss_tb = NULL,
  mdl_predrs_in_ds_chr = NULL,
  utility_type_chr = NULL,
- ttu_dv_nms_chr = "firstbounce",
+ ttu_dv_nms_chr = "TTU",
  server_1L_chr = "dataverse.harvard.edu",
  key_1L_chr = NULL){
  if(is.null(ttu_dv_dss_tb))
@@ -114,7 +114,7 @@ get_mdls_lup <- function(ttu_dv_dss_tb = NULL,
  utility_type_chr = utility_type_chr)
  }
  if(!is.null(ttu_dv_dss_tb)){
- ds_smrys_ls <- get_ttu_ds_smrys("firstbounce", reference_int = ttu_dv_dss_tb$reference_int)
+ ds_smrys_ls <- get_ttu_ds_smrys("TTU", reference_int = ttu_dv_dss_tb$reference_int)
  mdls_lup <- ds_smrys_ls %>%
  purrr::map2_dfr(names(ds_smrys_ls),
  ~{
@@ -231,7 +231,7 @@ get_predictors_lup <- function(mdl_meta_data_ls = NULL,
  return(predictors_tb)
 }
 get_ttu_dv_predrs <- function(ttu_dv_dss_tb = NULL,
- ttu_dv_nms_chr = "firstbounce",
+ ttu_dv_nms_chr = "TTU",
  server_1L_chr = "dataverse.harvard.edu",
  key_1L_chr = NULL){
  if(is.null(ttu_dv_dss_tb))
@@ -254,7 +254,7 @@ get_tfmn_from_lup <- function(mdl_nm_1L_chr, mdls_lup = NULL){
 
  return(tfmn_1L_chr)
 }
-get_ttu_ds_smrys <- function(ttu_dv_nm_1L_chr = "firstbounce",
+get_ttu_ds_smrys <- function(ttu_dv_nm_1L_chr = "TTU",
  server_1L_chr = "dataverse.harvard.edu",
  key_1L_chr = NULL,
  reference_int = NULL){
@@ -276,7 +276,7 @@ get_ttu_ds_smrys <- function(ttu_dv_nm_1L_chr = "firstbounce",
  stats::setNames(names(dv_dss_mdl_smrys_ls)[reference_int])
  return(dv_dss_mdl_smrys_ls)
 }
-get_ttu_dv_dss <- function(ttu_dv_nms_chr = "firstbounce",
+get_ttu_dv_dss <- function(ttu_dv_nms_chr = "TTU",
  server_1L_chr = "dataverse.harvard.edu",
  key_1L_chr = NULL){
  dv_dss_mdl_smrys_ls <- ttu_dv_nms_chr %>%

vignettes/Economic_Analysis.Rmd

@@ -95,7 +95,7 @@ predn_ds_ls <- make_predn_metadata_ls(ds_tb,
  mdls_lup = get_mdls_lup(utility_type_chr = "AQoL-6D",
  mdl_predrs_in_ds_chr = c("PHQ9 total score",
  "SOFAS total score"),
- ttu_dv_nms_chr = "firstbounce"),
+ ttu_dv_nms_chr = "TTU"),
  mdl_nm_1L_chr = "PHQ9_SOFAS_1_OLS_CLL")
 ```
 
@@ -134,6 +134,7 @@ he_smry_ls <- ds_tb %>% make_hlth_ec_smry(predn_ds_ls = predn_ds_ls,
 As part of the output of the `make_hlth_ec_smry`{.R} function is a BCEA object, we can use the BCEA package to produce a number of graphical summaries of economic results. One of the most important is the production of a cost-effectiveness plane. This plot highlights that, with an ICER of \$`r he_smry_ls$ce_res_ls$ICER %>% format(big.mark =",")`, `r ifelse(he_smry_ls$ce_res_ls$ceac[he_smry_ls$ce_res_ls$k==50000]==0.5,"half",ifelse(he_smry_ls$ce_res_ls$ceac[he_smry_ls$ce_res_ls$k==50000]<0.5,"less than half","most"))` of the bootstrapped iteration incremental cost and QALY pairs fall within the zone of cost-effectiveness (green). In fact, at the cost-effectiveness threshold we supplied, the results suggest there is a `r he_smry_ls$ce_res_ls$ceac[he_smry_ls$ce_res_ls$k==50000] * 100`% probability that the intervention is cost-effective.
 
 ```{r fig.width=6}
+library(ggplot2)
 BCEA::ceplane.plot(he_smry_ls$ce_res_ls, wtp =50000, 
  area_color = "green",
  graph = "ggplot2",

vignettes/Prediction_With_Mdls.Rmd

@@ -13,13 +13,13 @@ params:
 library(magrittr)
 library(youthu)
 ```
-Note, all models and input data used in this example are fake data for illustrative purposes only - they should not be used to inform decision making.
+Note, this example uses fake data - it should should not be used to inform decision making.
 
 ## Identifying suitable transfer to utility algorithms
 To identify datasets that contain transfer to utility models compatible with youthu (ie those developped with the [TTU package](https://ready4-dev.github.io/TTU/index.html)), you can use the following command. 
 
 ```{r}
-ttu_dv_dss_tb <- get_ttu_dv_dss("firstbounce")
+ttu_dv_dss_tb <- get_ttu_dv_dss("TTU")
 ```
 
 ```{r ttudss, echo = F, tab.cap='Transfer to Utility Datasets', tab.id = 'ttudss', results="asis"}