#' @keywords internal
#' @rdname other_predict
#' @param level A single numeric value between zero and one for the
#' interval estimates.
#' @param std_error A single logical for whether the standard error should be
#' returned (assuming that the model can compute it).
#' @inheritParams predict.model_fit
#' @method predict_confint model_fit
#' @export predict_confint.model_fit
#' @export
predict_confint.model_fit <- function(object, new_data, level = 0.95, std_error = FALSE, ...) {
check_spec_pred_type(object, "conf_int")
if (inherits(object$fit, "try-error")) {
cli::cli_warn("Model fit failed; cannot make predictions.")
return(NULL)
}
new_data <- prepare_data(object, new_data)
# preprocess data
if (!is.null(object$spec$method$pred$conf_int$pre))
new_data <- object$spec$method$pred$conf_int$pre(new_data, object)
# Pass some extra arguments to be used in post-processor
object$spec$method$pred$conf_int$extras <-
list(level = level, std_error = std_error)
pred_call <- make_pred_call(object$spec$method$pred$conf_int)
res <- eval_tidy(pred_call)
# post-process the predictions
if (!is.null(object$spec$method$pred$conf_int$post)) {
res <- object$spec$method$pred$conf_int$post(res, object)
}
attr(res, "level") <- level
res
}
#' @export
#' @keywords internal
#' @rdname other_predict
#' @inheritParams predict.model_fit
predict_confint <- function(object, ...)
UseMethod("predict_confint")
# ------------------------------------------------------------------------------
#' @export
#' @keywords internal
#' @rdname other_predict
#' @inheritParams predict.model_fit
predict_predint <- function(object, ...)
UseMethod("predict_predint")
#' @keywords internal
#' @rdname other_predict
#' @inheritParams predict.model_fit
#' @method predict_predint model_fit
#' @export predict_predint.model_fit
#' @export
predict_predint.model_fit <- function(object, new_data, level = 0.95, std_error = FALSE, ...) {
check_spec_pred_type(object, "pred_int")
if (inherits(object$fit, "try-error")) {
cli::cli_warn("Model fit failed; cannot make predictions.")
return(NULL)
}
new_data <- prepare_data(object, new_data)
# preprocess data
if (!is.null(object$spec$method$pred$pred_int$pre))
new_data <- object$spec$method$pred$pred_int$pre(new_data, object)
# create prediction call
# Pass some extra arguments to be used in post-processor
object$spec$method$pred$pred_int$extras <-
list(level = level, std_error = std_error)
pred_call <- make_pred_call(object$spec$method$pred$pred_int)
res <- eval_tidy(pred_call)
# post-process the predictions
if (!is.null(object$spec$method$pred$pred_int$post)) {
res <- object$spec$method$pred$pred_int$post(res, object)
}
attr(res, "level") <- level
res
}
#' @export
#' @keywords internal
#' @rdname other_predict
#' @inheritParams predict.model_fit
predict_predint <- function(object, ...)
UseMethod("predict_predint")
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