#' @rdname prediction
#' @export
prediction.Gam <-
function(model,
data = find_data(model, parent.frame()),
at = NULL,
type = c("response", "link", "terms"),
calculate_se = TRUE,
...) {
type <- match.arg(type)
# extract predicted value
data <- data
if (missing(data) || is.null(data)) {
if (isTRUE(calculate_se)) {
pred <- predict(model, type = type, se.fit = TRUE, ...)
pred <- make_data_frame(fitted = pred[["fit"]], se.fitted = pred[["se.fit"]][,1L])
} else {
pred <- predict(model, type = type, se.fit = FALSE, ...)
pred <- make_data_frame(fitted = pred, se.fitted = rep(NA_real_, length(pred)))
}
} 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
if (isTRUE(calculate_se)) {
pred <- predict(model, newdata = out, type = type, se.fit = FALSE, ...)
pred <- make_data_frame(out, fitted = pred, se.fitted = rep(NA_real_, length(pred)))
} else {
pred <- predict(model, newdata = out, type = type, se.fit = FALSE, ...)
pred <- make_data_frame(out, fitted = pred, se.fitted = rep(NA_real_, length(pred)))
}
}
# 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,
weighted = FALSE)
}
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