| 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 |