compute_probs

Estimating marginal and joint probabilities in 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

compute_probs function

<dl> <dt>InputData</dt>
<dd>a list of imputed or synthetic datasets</dd> <dt>varlist</dt>
<dd>a list of variable names (or combination of names) to evaluate (marginal or joint) probabilities for</dd></dl>

Arguments

Estimating marginal and joint probabilities in imputed or synthetic datasets — compute_probs

<dl>

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

 <dt>varlist</dt>
<dd>a list of variable names (or combination of names) to evaluate (marginal or joint) probabilities for</dd>

</dl>

compute_probs: Estimating marginal and joint probabilities in imputed or synthetic datasets

Description

Usage

Value

Arguments