DPMPM_nozeros_syn function

Use DPMPM models to synthesize data where there are no structural zeros

DPMPM_nozeros_syn(X, dj, nrun, burn, thin, K, aalpha, balpha, m, vars, seed, silent)

Arguments

• X: data frame for the original data
• dj: a vector recording the number of categories of the variables
• 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 synthetic datasets
• vars: the names of variables to be synthesized
• seed: choice of random seed
• silent: Default to TRUE. Set this parameter to FALSE if more iteration info are to be printed

Returns

• syndata: m synthetic datasets

• origdata: original data

• alpha: saved 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