DPMPM_zeros_imp: Use DPMPM models to impute missing data where there are no...

In NPBayesImputeCat: Non-Parametric Bayesian Multiple Imputation for Categorical Data

Use DPMPM models to impute missing data where there are no structural zeros

Description

Use DPMPM models to impute missing data where there are no structural zeros

Usage

DPMPM_zeros_imp(X, MCZ, Nmax, nrun, burn, thin, K, aalpha, balpha, m, seed, silent)

Arguments

X	data frame for the data containing missing values
MCZ	data frame containing the structural zeros definition
Nmax	an upper truncation limit for the augmented sample size
nrun	number of mcmc iterations
burn	number of burn-in iterations
thin	thining parameter for outputing iterations
K	number of latent classes
aalpha	the hyperparameters in stick-breaking prior distribution for alpha
balpha	the hyperparameters in stick-breaking prior distribution for alpha
m	number of imputations
seed	choice of random seed
silent	Default to TRUE. Set this parameter to FALSE if more iteration info are to be printed

Value

impdata	m imputed datasets
origdata	original data containing missing values
alpha	save posterior draws of alpha, which can be used to check MCMC convergence
kstar	saved number of occupied mixture components, which can be used to track whether K is large enough
Nmax	saved posterior draws of the augmented sample size, which can be used to check MCMC convergence

NPBayesImputeCat documentation built on Oct. 3, 2022, 5:05 p.m.