#' @export
get_predictions.logistf <- function(model,
data_grid = NULL,
terms = NULL,
ci_level = 0.95,
type = NULL,
typical = NULL,
vcov = NULL,
vcov_args = NULL,
condition = NULL,
interval = "confidence",
bias_correction = FALSE,
link_inverse = insight::link_inverse(model),
model_info = NULL,
verbose = TRUE,
...) {
# does user want standard errors?
se <- !is.null(ci_level) && !is.na(ci_level)
prdat <- stats::predict(
model,
newdata = data_grid,
type = "link",
se.fit = se,
...
)
# compute ci, two-ways
if (!is.null(ci_level) && !is.na(ci_level)) {
ci <- (1 + ci_level) / 2
} else {
ci <- 0.975
}
# get predicted values, on link-scale
data_grid$predicted <- prdat$fit
# did user request standard errors? if yes, compute CI
if (se && !is.null(prdat$se.fit)) {
tcrit <- stats::qnorm(ci)
data_grid$conf.low <- link_inverse(data_grid$predicted - tcrit * prdat$se.fit)
data_grid$conf.high <- link_inverse(data_grid$predicted + tcrit * prdat$se.fit)
# copy standard errors
attr(data_grid, "std.error") <- prdat$se.fit
} else {
# No CI
data_grid$conf.low <- NA
data_grid$conf.high <- NA
}
# transform predicted values
data_grid$predicted <- link_inverse(data_grid$predicted)
data_grid
}
#' @export
get_predictions.flic <- get_predictions.logistf
#' @export
get_predictions.flac <- get_predictions.logistf
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