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#' @rdname get_predict
#' @export
get_predict.fixest <- function(
model,
newdata = insight::get_data(model),
type = "response",
mfx = NULL,
...) {
insight::check_if_installed("fixest")
if (is.null(type)) {
calling_function <- if (!is.null(mfx)) mfx@calling_function else "predictions"
type <- sanitize_type(
model = model,
type = type,
calling_function = calling_function
)
}
dots <- list(...)
# some predict methods raise warnings on unused arguments
unused <- c(
"normalize_dydx",
"step_size",
"numDeriv_method",
"conf.int",
"internal_call"
)
dots <- dots[setdiff(names(dots), unused)]
# fixest is super slow when using do call because of some `deparse()` call
# issue #531: we don't want to waste time computing intervals or risk having
# them as leftover columns in contrast computations
pred <- try(
stats::predict(
object = model,
newdata = newdata,
type = type
),
silent = TRUE
)
if (inherits(pred, "try-error")) {
return(pred)
}
out <- data.table(estimate = as.numeric(pred))
out <- add_rowid(out, newdata)
return(out)
}
#' @rdname sanitize_model_specific
sanitize_model_specific.fixest <- function(model, vcov = TRUE, calling_function = "predictions", ...) {
# issue #1487 is only a problem for standard errors
if (isFALSE(vcov)) {
return(model)
}
if (!isTRUE(getOption("marginaleffects_safe", default = TRUE))) {
return(model)
}
msg <- paste(
"For this model type, `marginaleffects` cannot take into account the uncertainty in fixed-effects parameters.",
"This is especially consequential for predictions and their uncertainty intervals.",
"Set `vcov=FALSE` to compute estimates without standard errors."
)
if (!is.null(model[["fixef_vars"]])) {
# issue #1487: fixed-effects always matter for predictions
if (identical(calling_function, "predictions")) {
stop_sprintf(msg)
}
# issue #1487: fixed-effects matter for slopes and contrasts, except for linear models
if (!is.null(model$family)) {
if (identical(calling_function, "hypotheses")) {
if (isTRUE(getOption("marginaleffects_safe", default = TRUE))) {
warn_sprintf(msg)
}
} else {
stop_sprintf(msg)
}
}
}
return(model)
}
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