Use DPMPM models to impute missing data where there are no structural zeros
DPMPM_zeros_imp(X, MCZ, Nmax, nrun, burn, thin, K, aalpha, balpha, m, seed, silent)
m imputed datasets
original data containing missing values
save posterior draws of alpha, which can be used to check MCMC convergence
saved number of occupied mixture components, which can be used to track whether K is large enough
saved posterior draws of the augmented sample size, which can be used to check MCMC convergence
data frame for the data containing missing values
data frame containing the structural zeros definition
an upper truncation limit for the augmented sample size
number of mcmc iterations
number of burn-in iterations
thining parameter for outputing iterations
number of latent classes
the hyperparameters in stick-breaking prior distribution for alpha
the hyperparameters in stick-breaking prior distribution for alpha
number of imputations
choice of random seed
Default to TRUE. Set this parameter to FALSE if more iteration info are to be printed