#' @export
get_predictions.glmgee <- 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,
...) {
if (is.null(vcov)) {
vcov <- "robust"
}
vcov <- insight::validate_argument(
vcov,
c("robust", "df-adjusted", "model", "bias-corrected")
)
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)
# get predictions
prdat <- as.data.frame(stats::predict(
model,
newdata = data_grid,
se.fit = TRUE,
type = "link",
varest = vcov,
...
))
data_grid$predicted <- prdat$fit
if (isTRUE(se)) {
# 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$se.fit
attr(data_grid, "prediction.interval") <- FALSE
} else {
# CI
data_grid$conf.low <- NA
data_grid$conf.high <- NA
}
data_grid$predicted <- link_inverse(data_grid$predicted)
data_grid
}
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