# @rdname prediction
# @export
prediction.vgam <-
function(model,
data = find_data(model, parent.frame()),
at = NULL,
type = c("response", "link"),
calculate_se = FALSE,
category,
...) {
type <- match.arg(type)
# extract predicted values
data <- data
if (missing(data) || is.null(data)) {
pred <- make_data_frame(predict(model, type = type, se.fit = FALSE, ...))
} 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 = type, se.fit = FALSE, ...)
if (!is.null(dim(tmp))) {
tmp <- as.matrix(tmp, ncol = 1)
}
# cbind back together
pred <- make_data_frame(out, fitted = make_data_frame(tmp), se.fitted = rep(NA_real_, nrow(out)))
}
# handle category argument
if (missing(category)) {
category <- names(pred)[!names(pred) %in% names(data)][1L]
pred[["fitted"]] <- pred[[category]]
} else {
w <- grep(category, names(pred))
if (!length(w)) {
stop(sprintf("category %s not found", category))
}
pred[["fitted"]] <- pred[[ w[1L] ]]
}
# 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 = type,
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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