Non-Parametric Bayesian Multiple Imputation for Categorical Data

---

Documentation for package ‘NPBayesImputeCat’ version 0.5

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Code demos. Use demo() to run them.

Help Pages

NPBayesImputeCat-package	Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros
compute_probs	Estimating marginal and joint probabilities in imputed or synthetic datasets
CreateModel	Create and initialize the Lcm model object
DPMPM_nozeros_imp	Use DPMPM models to impute missing data where there are no structural zeros
DPMPM_nozeros_syn	Use DPMPM models to synthesize data where there are no structural zeros
DPMPM_zeros_imp	Use DPMPM models to impute missing data where there are no structural zeros
fit_GLMs	Fit GLM models for imputed or synthetic datasets
GetDataFrame	Convert imputed data to a dataframe, using the same setting from original input data.
GetMCZ	Convert disjointed structrual zeros to a dataframe, using the same setting from original structrual zero data.
kstar_MCMCdiag	Perform MCMC diagnostics for kstar
Lcm	Class '"Rcpp_Lcm"'
marginal_compare_all_imp	Plot estimated marginal probabilities from observed data vs imputed datasets
marginal_compare_all_syn	Plot estimated marginal probabilities from observed data vs synthetic datasets
MCZ	Example dataframe for structrual zeros based on the NYMockexample dataset.
NPBayesImputeCat	Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros
pool_estimated_probs	Pool probability estimates from imputed or synthetic datasets
pool_fitted_GLMs	Pool estimates of fitted GLM models in imputed or synthetic datasets
Rcpp_Lcm-class	Rcpp implemenation of the Lcm functions
ss16pusa_ds_MCZ	Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.
ss16pusa_mi_MCZ	Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.
ss16pusa_sample_nozeros	Example dataframe for input categorical data without structural zeros (without missing values).
ss16pusa_sample_nozeros_miss	Example dataframe for input categorical data without structural zeros (with missing values).
ss16pusa_sample_zeros	Example dataframe for input categorical data with structural zeros (without missing values).
ss16pusa_sample_zeros_miss	Example dataframe for input categorical data with structural zeros (with missing values).
UpdateX	Allow user to update the model with data matrix of same kind.
X	Example dataframe for input categorical data with missing values based on the NYMockexample dataset.