#' @export
get_predictions.clm <- 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)
# compute ci, two-ways
if (!is.null(ci_level) && !is.na(ci_level)) {
ci <- ci_level
} else {
ci <- 0.95
}
# prediction, with CI
prdat <- stats::predict(
model,
newdata = data_grid,
type = "prob",
interval = se,
level = ci,
...
)
# convert to data frame.
prdat <- as.data.frame(prdat)
# bind predictions to model frame
data_grid <- cbind(prdat, data_grid)
# get levels of response
lv <- levels(insight::get_response(model, verbose = FALSE))
# for proportional ordinal logistic regression (see ordinal::clm),
# we have predicted values for each response category. Hence,
# gather columns. Since we also have conf. int. for each response
# category, we need to gather multiple columns at once
if (isTRUE(se)) {
# length of each variable block
l <- seq_len(ncol(prdat) / 3)
colnames(data_grid)[l] <- lv
data_grid <- .multiple_gather(
data_grid,
names_to = "response.level",
values_to = c("predicted", "conf.low", "conf.high"),
columns = list(l, l + length(l), l + 2 * length(l))
)
} else {
data_grid <- .gather(
data_grid,
names_to = "response.level",
values_to = "predicted",
colnames(prdat)
)
# No CI
data_grid$conf.low <- NA
data_grid$conf.high <- NA
}
data_grid
}
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