#' Proportional hazards regression
#'
#' @description
#' `proportional_hazards()` defines a model for the hazard function
#' as a multiplicative function of covariates times a baseline hazard. This
#' function can fit censored regression models.
#'
#' \Sexpr[stage=render,results=rd]{parsnip:::make_engine_list("proportional_hazards")}
#'
#' More information on how \pkg{parsnip} is used for modeling is at
#' \url{https://www.tidymodels.org/}.
#'
#' @inheritParams nearest_neighbor
#' @inheritParams linear_reg
#' @param mode A single character string for the prediction outcome mode.
#' The only possible value for this model is "censored regression".
#'
#' @templateVar modeltype proportional_hazards
#' @template spec-details
#'
#' @template spec-survival
#'
#' @details
#' Proportional hazards models include the Cox model.
#'
#' @template spec-references
#'
#' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("proportional_hazards")}
#'
#' @examplesIf !parsnip:::is_cran_check()
#' show_engines("proportional_hazards")
#'
#' proportional_hazards(mode = "censored regression")
#' @keywords internal
#' @export
proportional_hazards <- function(
mode = "censored regression",
engine = "survival",
penalty = NULL,
mixture = NULL) {
args <- list(
penalty = enquo(penalty),
mixture = enquo(mixture)
)
new_model_spec(
"proportional_hazards",
args = args,
eng_args = NULL,
mode = mode,
user_specified_mode = !missing(mode),
method = NULL,
engine = engine,
user_specified_engine = !missing(engine)
)
}
# ------------------------------------------------------------------------------
#' @method update proportional_hazards
#' @rdname parsnip_update
#' @export
update.proportional_hazards <- function(object,
parameters = NULL,
penalty = NULL,
mixture = NULL,
fresh = FALSE, ...) {
args <- list(
penalty = enquo(penalty),
mixture = enquo(mixture)
)
update_spec(
object = object,
parameters = parameters,
args_enquo_list = args,
fresh = fresh,
cls = "proportional_hazards",
...
)
}
#' @export
translate.proportional_hazards <- function(x, engine = x$engine, ...) {
x <- translate.default(x, engine, ...)
if (engine == "glmnet") {
# See https://parsnip.tidymodels.org/reference/glmnet-details.html
.check_glmnet_penalty_fit(x)
x <- set_glmnet_penalty_path(x)
# Since the `fit` information is gone for the penalty, we need to have an
# evaluated value for the parameter.
x$args$penalty <- rlang::eval_tidy(x$args$penalty)
}
x
}
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