Nothing
#' @rdname prediction
#' @export
prediction.tree <-
function(model,
data = find_data(model, parent.frame()),
at = NULL,
type = NULL,
calculate_se = FALSE,
category,
...) {
if (!is.null(type)) {
warning(sprintf("'type' is ignored for models of class '%s'", class(model)))
}
# extract predicted values
data <- data
if (missing(data) || is.null(data)) {
if (is.factor(model[["y"]])) {
pred <- make_data_frame(fitted.class = predict(model, type = "class", ...))
probs <- make_data_frame(predict(model, type = "vector", ...))
names(probs) <- paste0("Pr(", names(probs), ")")
} else {
pred <- make_data_frame(fitted = predict(model, type = "vector"),
fitted.class = predict(model, type = "class", ...))
}
pred <- make_data_frame(pred, probs)
} else {
# setup data
if (is.null(at)) {
out <- data
} else {
out <- build_datalist(data, at = at, as.data.frame = TRUE)
at_specification <- attr(out, "at_specification")
}
# calculate predictions
tmp <- predict(model, newdata = out, type = "class", ...)
tmp_probs <- make_data_frame(predict(model, newdata = out, type = "probs", ...))
names(tmp_probs) <- paste0("Pr(", names(tmp_probs), ")")
# cbind back together
pred <- make_data_frame(out, fitted.class = tmp, tmp_probs)
rm(tmp, tmp_probs)
}
# handle category argument
if (missing(category)) {
w <- grep("^Pr\\(", names(pred))[1L]
category <- names(pred)[w]
pred[["fitted"]] <- pred[[w]]
} else {
w <- which(names(pred) == paste0("Pr(", category, ")"))
if (!length(w)) {
stop(sprintf("category %s not found", category))
}
pred[["fitted"]] <- pred[[ w[1L] ]]
}
pred[["se.fitted"]] <- NA_real_
# variance(s) of average predictions
vc <- NA_real_
# output
structure(pred,
class = c("prediction", "data.frame"),
at = if (is.null(at)) at else at_specification,
type = NA_character_,
call = if ("call" %in% names(model)) model[["call"]] else NULL,
model_class = class(model),
row.names = seq_len(nrow(pred)),
vcov = vc,
jacobian = NULL,
category = category,
weighted = FALSE)
}
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