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#' @rdname prediction
#' @export
prediction.ar <- function(model, data, at = NULL, calculate_se = TRUE,...) {
# extract predicted values
if (missing(data) || is.null(data)) {
if (isTRUE(calculate_se)) {
tmp <- predict(object = model, se.fit = TRUE, ...)
pred <- make_data_frame(fitted = tmp[[1L]], se.fitted = tmp[[2L]])
} else {
tmp <- predict(object = model, se.fit = FALSE, ...)
pred <- make_data_frame(fitted = tmp, se.fitted = rep(NA_real_, length(tmp)))
}
} else {
# setup data
if (is.null(at)) {
data <- data
} else {
data <- build_datalist(data, at = at, as.data.frame = TRUE)
at_specification <- attr(data, "at_specification")
}
if (isTRUE(calculate_se)) {
tmp <- predict(model, newdata = data, se.fit = TRUE, ...)
pred <- make_data_frame(fitted = tmp[[1L]], se.fitted = tmp[[2L]])
} else {
tmp <- predict(model, newdata = data, se.fit = FALSE, ...)
pred <- make_data_frame(fitted = tmp, se.fitted = rep(NA_real_, length(tmp)))
}
}
# 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,
weighted = FALSE)
}
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