fit_GLMs

Fit GLM models for imputed or synthetic datasets

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) <doi:10.1080/10618600.2013.844700>.

Jingchen Hu

NPBayesImputeCat

Non-Parametric Bayesian Multiple Imputation for Categorical Data

fit_GLMs function

<dl> <dt>InputData</dt>
<dd>a list of imputed or synthetic datasets</dd> <dt>exp</dt>
<dd>GLM expression (for polr and nnet, those libraries should be loaded first)</dd></dl>

Arguments

Fit GLM models for imputed or synthetic datasets — fit_GLMs

<dl>

 <dt>InputData</dt>
<dd>a list of imputed or synthetic datasets</dd>

 <dt>exp</dt>
<dd>GLM expression (for polr and nnet, those libraries should be loaded first)</dd>

</dl>

fit_GLMs: Fit GLM models for imputed or synthetic datasets

Description

Usage

Value

Arguments