Predict from DPMM — predict_dpmm

Predict missing values from the fitted DPMM.

Usage

predict_dpmm_fit(object, newdata, samples = seq(1000, 2500, 100), ...)

Arguments

object: object class 'dpmm_fit'
newdata: dataframe with missingness
samples: vector of iterations to be used in the posterior
...: other parameters used in 'posterior.dpmm'

Value

A list with n entries for n rows with missingness, each entry is a dataframe with the sampled missing values.

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))
}