- Uses S4 classes.
- imputation methods for multivariate multinomial data.
- Imputation of the summary statistics is done using EM and data augmentation
- Imputation of the observation level data is done via MLE
- allows for the following priors:
c("none", "data.dep", "flat.prior", "non.informative")
- If data dependent priors are chosen, calculated from approximately 20% of the observations data (assuming 20% of data are complete cases). If less than 20% of data are complete cases, all complete cases are used.
- From CRAN
install.packages("imputeMulti")
- From Github
library("devtools");
install_github("alexwhitworth/imputeMulti",dependencies=TRUE)
## load library and example data
library(imputeMulti)
data(tract2221)
# usage for non-informative priors for both EM and DA
# other priors may also be specified (not shown)
test_em <- multinomial_impute(tract2221[,1:4], method= "EM",
conj_prior = "non.informative", verbose= TRUE)
test_da <- multinomial_impute(tract2221[,1:4], method= "DA",
conj_prior = "non.informative", verbose= TRUE)
# extract imputed values and parameter estimates
get_imputations(test_em)
get_parameters(test_da)
- Schafer, Joseph L. Analysis of incomplete multivariate data. Chapter 7. CRC press, 1997.
- Darnieder, William Francis. Bayesian methods for data-dependent priors. Diss. The Ohio State University, 2011.