pool_fitted_GLMs

GLMResults

<p>choose between "imputation", "synthesis_full", "synthesis_partial"</p>

method

<p>Pool estimates of fitted GLM models in imputed or synthetic datasets</p>

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>.

pool_fitted_GLMs: Pool estimates of fitted GLM models in imputed or synthetic datasets

Description

Usage

Arguments

Value