Nothing
predict.rpart <- function(object, newdata,
type = c("vector", "prob", "class", "matrix"),
na.action = na.pass, ...)
{
if (!inherits(object, "rpart")) stop("Not a legitimate \"rpart\" object")
mtype <- missing(type)
type <- match.arg(type)
where <- if (missing(newdata)) object$where
else {
if (is.null(attr(newdata, "terms"))) {
Terms <- delete.response(object$terms)
newdata <- model.frame(Terms, newdata, na.action = na.action,
xlev = attr(object, "xlevels"))
if (!is.null(cl <- attr(Terms, "dataClasses")))
.checkMFClasses(cl, newdata, TRUE)
}
pred.rpart(object, rpart.matrix(newdata))
}
frame <- object$frame
ylevels <- attr(object, "ylevels")
nclass <- length(ylevels)
if (mtype && nclass > 0L) type <- "prob"
if (type == "vector" || (type == "matrix" && is.null(frame$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 > 0L) {
if (length(ylevels) == 0L)
stop("type 'class' is only appropriate for classification")
pred <- factor(ylevels[frame$yval[where]], levels = ylevels)
names(pred) <- names(where)
} else if (type == "prob" && nclass > 0L) {
pred <- frame$yval2[where, 1L + nclass + 1L:nclass, drop = FALSE]
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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