marginal_compare_all_syn

Plot estimated marginal probabilities from observed data vs 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

marginal_compare_all_syn function

<dl> <dt>obsdata</dt>
<dd>the observed data</dd> <dt>syndata</dt>
<dd>the list of m imputed datasets</dd> <dt>vars</dt>
<dd>the variable of interest</dd></dl>

Arguments

Plot estimated marginal probabilities from observed data vs synthetic datasets — marginal_compare_all_syn

<dl>

 <dt>obsdata</dt>
<dd>the observed data</dd>

 <dt>syndata</dt>
<dd>the list of m imputed datasets</dd>

 <dt>vars</dt>
<dd>the variable of interest</dd>

</dl>

marginal_compare_all_syn: Plot estimated marginal probabilities from observed data vs synthetic datasets

Description

Usage

Value

Arguments