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#' GAMLSS bootstrap method
#'
#' @description Creates a random generation function for the missing
#' values with bootstrap sample from the fitted GAMLSS model for the
#' completely observed data.
#'
#' @param incomplete.data Data frame with missings on one variable.
#' @param R Boolean matrix with the response indicator.
#' @param fit Random sample generator method.
#' @param ... extra arguments for the control of the gamlss fitting
#' function
#'
#' @return Returns a imputation sample generator.
ImpGamlssBootstrap <- function(incomplete.data, fit, R, ...) {
## Imputation using the bootstrap predictive distribution
available.cases <- subset(incomplete.data, R)
## Fit initial gamlss model with completely observed data.
master.predict <- fit(available.cases, available.cases, ...)
tryCatch(
{
function(...) {
bootstrap.sample <- available.cases
repeat {
## Replace observed part of the available cases with a
## random sample of the same distribution
bootstrap.sample[,1] <- master.predict(...)
## Create random sample generator with
## bootstrap.sample as completely observed set and the
## observed part of the variables with missings as
## predictors.
bootstrap.predict <- fit(bootstrap.sample,
subset(incomplete.data, !R), ...)
break
}
bootstrap.predict(...)
}
}, error = function(e) {
function(model) {
do.call(rep,
args=list(NA, nrow(subset(incomplete.data, !R))))}
}
)
}
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