#' @export
get_predictions.Gam <- 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,
...) {
se <- !is.null(ci_level) && !is.na(ci_level)
# compute ci, two-ways
if (!is.null(ci_level) && !is.na(ci_level))
ci <- (1 + ci_level) / 2
else
ci <- 0.975
# degrees of freedom
dof <- .get_df(model)
tcrit <- stats::qt(ci, df = dof)
prdat <- stats::predict(
model,
newdata = data_grid,
type = "link",
## TODO currently not supported
se.fit = FALSE
)
# copy predictions
data_grid$predicted <- link_inverse(as.vector(prdat))
# did user request standard errors? if yes, compute CI
if (se) {
se.pred <- .standard_error_predictions(
model = model,
prediction_data = data_grid,
typical = typical,
terms = terms,
condition = condition
)
if (.check_returned_se(se.pred)) {
se.fit <- se.pred$se.fit
data_grid <- se.pred$prediction_data
# calculate CI
data_grid$conf.low <- link_inverse(as.vector(prdat) - tcrit * se.fit)
data_grid$conf.high <- link_inverse(as.vector(prdat) + tcrit * se.fit)
# copy standard errors
attr(data_grid, "std.error") <- se.fit
} else {
# no CI
data_grid$conf.low <- NA
data_grid$conf.high <- NA
}
} else {
# no CI
data_grid$conf.low <- NA
data_grid$conf.high <- NA
}
data_grid
}
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