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#' @include set_coef.R
#' @rdname set_coef
#' @export
set_coef.survreg <- function(model, coefs, ...) {
#Reverse engineering insight::get_get_parameters.survreg(),
#which uses summary.survreg()
nvar0 <- length(model$coefficients)
nvar <- nrow(model$var)
if (nvar > nvar0) {
model[["coefficients"]][] <- coefs[-nvar0]
model[["scale"]][] <- exp(coefs[nvar0])
} else {
model$coefficients[] <- coefs
}
model
}
#' @rdname get_predict
#' @export
get_predict.coxph <- function(
model,
newdata = insight::get_data(model),
type = "lp",
...
) {
out <- stats::predict(model, newdata = newdata, type = type, ...)
out <- data.frame(rowid = seq_len(nrow(newdata)), estimate = out)
return(out)
}
#' @rdname sanitize_model_specific
sanitize_model_specific.coxph <- function(model, vcov, ...) {
insight::check_if_installed("survival")
flag1 <- !isFALSE(vcov)
flag2 <- !isTRUE(checkmate::check_choice(vcov, choices = c("rsample", "boot", "fwb")))
flag3 <- isTRUE(getOption("marginaleffects_safe", default = TRUE))
if (flag1 && flag2 && flag3) {
msg <- 'The default delta method standard errors for `coxph` models only take into account uncertainty in the regression coefficients. Standard errors may be too small. Use the `inferences()` function or set `vcov` to "rsample", "boot" or "fwb" to compute confidence intervals by bootstrapping. Set `vcov` to `FALSE` or `options(marginaleffects_safe=FALSE)` to silence this warning.'
warning(msg, call. = FALSE)
}
return(model)
}
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