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#' @title Print a RLT object
#' @description Print a RLT object
#' @param x A fitted RLT object
#' @param ... ...
#' @return No return value
#' @examples
#' x = matrix(rnorm(100), ncol = 10)
#' y = rowMeans(x)
#' fit = RLT(x, y, ntrees = 5)
#' fit
#' @export
print.RLT<- function(x, ...)
{
cat("Reinforcement Learning Trees for", x$model, "model:\n")
cat(" number of trees:", x$ntrees, "\n")
cat(" sample size:", x$n, "\n")
cat(" number of variables:", x$p, "\n")
cat(" nmin:", x$nmin, "\n")
if (x$reinforcement == 0)
cat(" mtry:", x$mtry, "\n")
cat(" resampling:", paste(round(x$resample.prob*100,2), "%", sep=""), c("without", "with")[x$replacement+1], "replacement \n")
cat(" split generating method:", x$split.gen, "\n")
cat(" use subject weights:", c("No", "Yes")[x$use.sub.weight+1], "\n")
# if (x$oobMSE)
# cat(" variance explained:", paste(round(x$PMSE*100,2), "%", sep=""), "(based on OOB cross-validation) \n")
cat(" reinforcement learning:", c("No", "Yes")[x$reinforcement+1], "\n")
if (x$reinforcement)
{
if (x$muting > 1)
cat(" muting by count:", x$muting, "at each split \n")
if (x$muting == -1)
cat(" muting:", paste(round(x$muting.percent*100,2), "%", sep=""), "at each split \n")
cat(" protected variables:", x$protect, "\n")
if (x$combsplit > 1)
cat(" Linear combination:", x$combsplit, "with VI threshold", x$combsplit.th, "\n")
}
}
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