Nothing
#
# a modified version of `predict.rpart.s' from the rpart package
# see COPYRIGHTS for details.
#
predict.irpart <-
function(object, newdata = list(),
type = c("vector", "prob", "class", "matrix"), ...) {
if(!inherits(object, "rpart"))
stop("Not legitimate tree")
mtype <- missing(type)
type <- match.arg(type)
if(missing(newdata))
where <- object$where
else {
if(is.null(attr(newdata, "terms")) & !inherits(newdata, "rpart.matrix")) {
Terms <- delete.response(object$terms)
act <- (object$call)$na.action
if (is.null(act)) act<- na.rpart
newdata <- model.frame(Terms, newdata, na.action = act,
xlev=attr(object, "xlevels"))
newdata <- getFromNamespace("rpart.matrix", ns = "rpart")(newdata)
}
where <- getFromNamespace("pred.rpart", ns = "rpart")(object, newdata)
}
frame <- object$frame
method <- object$method
ylevels <- attr(object, "ylevels")
nclass <- length(ylevels)
if(mtype && nclass > 0) type <- "prob"
if(type == "vector" || (type=="matrix" && is.null(frame$yval2))) {
pred <- frame$yval[where]
names(pred) <- names(where)
}
else if (type == "matrix") {
pred <- frame$yval2[where,]
dimnames(pred) <- list(names(where), NULL)
}
else if(type == "class" && nclass > 0) {
pred <- factor(ylevels[frame$yval[where]], levels=ylevels)
names(pred) <- names(where)
}
else if (type == "prob" && nclass > 0) {
pred <- frame$yval2[where, 1 + nclass + 1:nclass]
dimnames(pred) <- list(names(where), ylevels)
}
else stop("Invalid prediction for rpart object")
# Expand out the missing values in the result
# But only if operating on the original dataset
if (missing(newdata) && !is.null(object$na.action))
pred <- naresid(object$na.action, pred)
pred
}
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