DPMPM_zeros_imp {NPBayesImputeCat}R Documentation

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


[Package NPBayesImputeCat version 0.5 Index]