Nothing
#
# use update to fit multiple trees to bootstrap samples
#
irpart <- function(formula, data=NULL, weights, subset,
na.action=na.rpart, method, model=FALSE, x=FALSE, y=TRUE,
parms, control, cost, bcontrol, ...)
{
mc <- match.call()
mc$bcontrol <- NULL
mc[[1]] <- as.name("rpart")
m <- match.call(expand.dots=FALSE)
m$model <- m$method <- m$control <- m$bcontrol <- NULL
m$x <- m$y <- m$parms <- m$... <- NULL
m$cost <- NULL
m$na.action <- na.action
m[[1]] <- as.name("model.frame.default")
m <- eval(m, parent.frame())
init_tree <- eval(mc, parent.frame())
nobs <- length(init_tree$where)
if (missing(weights)) {
weights <- rep(1.0, nobs)
} else {
warning("weights argument ignored in irpart")
}
yclasses <- c(class = "sclass", exp = "ssurv", anova = "sreg", poisson = "sreg")
#
# Bagging: repeat this several times!
#
if (is.null(bcontrol)) stop("bcontrol not given")
mod <- vector(mode="list", length=bcontrol$nbagg)
for (b in 1:bcontrol$nbagg) {
if (bcontrol$nbagg > 1)
bindx <- sample(1:nobs, bcontrol$ns, replace=bcontrol$replace)
else
bindx <- 1:nobs
tab <- tabulate(bindx, nbins = nobs)
mc$data <- m[bindx,,drop = FALSE] ### tab * weights
ans <- eval(mc, parent.frame())
# return the appropriate class
this <- list(bindx = bindx, btree = ans)
class(this) <- yclasses[init_tree$method]
mod[[b]] <- this
}
mod
}
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