Readme.md

In alexWhitworth/imputeMulti: Imputation Methods for Multivariate Multinomial Data

imputeMulti: Imputation methods for multivariate multinomial data

Features:

• 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.

Installation

• From CRAN

install.packages("imputeMulti")

• From Github

library("devtools");
install_github("alexwhitworth/imputeMulti",dependencies=TRUE)

Example usage:

## 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)

References:

1. Schafer, Joseph L. Analysis of incomplete multivariate data. Chapter 7. CRC press, 1997.
2. Darnieder, William Francis. Bayesian methods for data-dependent priors. Diss. The Ohio State University, 2011.

alexWhitworth/imputeMulti documentation built on July 15, 2022, 7:02 p.m.