| DPMPM_nozeros_syn {NPBayesImputeCat} | R Documentation |
Use DPMPM models to synthesize data where there are no structural zeros
Description
Use DPMPM models to synthesize data where there are no structural zeros
Usage
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 |
Value
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 |