Sample from DPMM

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, in the same row order as 'patient'.

Examples

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

## fit model
posteriors <- runModel(dataset_1,
                       mcmc_iterations = 100,
                       L = 6,
                       mcmc_chains = 2,
                       standardise = TRUE,
                       seed = 1)

## 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),
                                       seed = 1)
} # }