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NPBayesImputeCat (version 0.3)

Non-Parametric Bayesian Multiple Imputation for Categorical Data

Description

These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) .

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Version

Install

install.packages('NPBayesImputeCat')

Monthly Downloads

217

Version

0.3

License

GPL (>= 3)

Maintainer

Jingchen Hu

Last Published

January 14th, 2021

Functions in NPBayesImputeCat (0.3)

Convert disjointed structrual zeros to a dataframe, using the same setting from original structrual zero data.

Class "Rcpp_Lcm"

DPMPM_nozeros_syn

Use DPMPM models to synthesize data where there are no structural zeros

RCPP implemenation of the library

Convert imputed data to a dataframe, using the same setting from original input data.

Create and initialize the Rcpp_Lcm model object

Example dataframe for structrual zeros based on the NYMockexample dataset.

DPMPM_zeros_imp

Use DPMPM models to impute missing data where there are no structural zeros

DPMPM_nozeros_imp

Use DPMPM models to impute missing data where there are no structural zeros

NPBayesImputeCat-package

Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros

Example dataframe for input categorical data with missing values based on the NYMockexample dataset.

marginal_compare_all_imp

Plot estimated marginal probabilities from observed data vs imputed datasets

Fit GLM models for imputed or synthetic datasets

pool_fitted_GLMs

Pool estimates of fitted GLM models in imputed or synthetic datasets

ss16pusa_ds_MCZ

Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.

pool_estimated_probs

Pool probability estimates from imputed or synthetic datasets

marginal_compare_all_syn

Plot estimated marginal probabilities from observed data vs synthetic datasets

ss16pusa_sample_zeros_miss

Example dataframe for input categorical data with structural zeros (with missing values).

ss16pusa_sample_zeros

Example dataframe for input categorical data with structural zeros (without missing values).

ss16pusa_sample_nozeros_miss

Example dataframe for input categorical data without structural zeros (with missing values).

Allow user to update the model with data matrix of same kind.

RCPP implemenation of the library

ss16pusa_mi_MCZ

Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.

Estimating marginal and joint probabilities in imputed or synthetic datasets

ss16pusa_sample_nozeros

Example dataframe for input categorical data without structural zeros (without missing values).