kstar_MCMCdiag

A helper function to perform MCMC diagnostics for kstar

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

kstar_MCMCdiag function

<dl> <dt>kstar</dt>
<dd>the vector output of kstar from running the DPMPM model</dd> <dt>nrun</dt>
<dd>number of MCMC iterations used in running the DPMPM model</dd> <dt>burn</dt>
<dd>number of burn-in iterations used in running the DPMPM model</dd> <dt>thin</dt>
<dd>number of thinning used in running the DPMPM model</dd></dl>

Arguments

Perform MCMC diagnostics for kstar — kstar_MCMCdiag

<dl>

 <dt>kstar</dt>
<dd>the vector output of kstar from running the DPMPM model</dd>

 <dt>nrun</dt>
<dd>number of MCMC iterations used in running the DPMPM model</dd>

 <dt>burn</dt>
<dd>number of burn-in iterations used in running the DPMPM model</dd>

 <dt>thin</dt>
<dd>number of thinning used in running the DPMPM model</dd>

</dl>

kstar_MCMCdiag: Perform MCMC diagnostics for kstar

Description

Usage

Value

Arguments