DPMPM_zeros_imp function

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)

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

Returns

• 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