#' @rdname prediction
#' @export
prediction.selection <- function(model, data = find_data(model, parent.frame()), at = NULL, type = "response", calculate_se = FALSE, ...) {
# extract predicted value at input value
data <- data
if (missing(data) || is.null(data)) {
pred <- make_data_frame(fitted = predict(model, type = type, ...),
se.fitted = NA_real_)
} 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,
...)
# cbind back together
pred <- make_data_frame(out, 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 = 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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