Sample from DPMM — posterior

Sample missing values from the fitted DPMM

Usage

posterior_dpmm(
  patient,
  samples,
  seed = NULL,
  cont_vars = NULL,
  cat_vars = NULL,
  mcmc_chain = NULL
)

Arguments

patient: dataset with missing values.
samples: vector of iterations to be used in the posterior.
seed: specify seed to be used. (default = NULL)
cont_vars: names of continuous variables
cat_vars: names of categorical variables
mcmc_chain: MCMC posterior samples of DPMM parameters

Value

A list with n entries of n rows with missingness

Examples

if (FALSE) {
## load dataset
data(dataset_1)

## fit model
posteriors <- runModel(dataset_1, 
                       mcmc_iterations = 100,
                       L = 6, 
                       mcmc_chains = 2, 
                       standardise = TRUE)
                       
## introduce missing data
rows <- 501:550
dataset_missing <- dataset_1
dataset_missing_predict <- dataset_missing[rows,]
dataset_missing_predict[,1] <- as.numeric(NA)

# predict missing values
posteriors.dpmmfit <- predict_dpmm_fit(posteriors, 
                                       dataset_missing_predict, 
                                       samples = c(1:100))
}