# These functions define the proportional hazards models.
# They are executed when this package is loaded via `.onLoad()` and modify the
# parsnip package's model environment.
# These functions are tested indirectly when the models are used. Since this
# function is executed on package startup, you can't execute them to test since
# they are already in the parsnip model database. We'll exclude them from
# coverage stats for this reason.
# nocov start
make_proportional_hazards_survival <- function() {
parsnip::set_model_engine(
"proportional_hazards",
mode = "censored regression",
eng = "survival"
)
parsnip::set_dependency(
"proportional_hazards",
eng = "survival",
pkg = "survival",
mode = "censored regression"
)
parsnip::set_dependency(
"proportional_hazards",
eng = "survival",
pkg = "censored",
mode = "censored regression"
)
parsnip::set_fit(
model = "proportional_hazards",
eng = "survival",
mode = "censored regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "survival", fun = "coxph"),
defaults = list(x = TRUE, model = TRUE)
)
)
parsnip::set_encoding(
model = "proportional_hazards",
eng = "survival",
mode = "censored regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "proportional_hazards",
eng = "survival",
mode = "censored regression",
type = "time",
value = list(
pre = cph_survival_pre,
post = NULL,
func = c(pkg = "censored", fun = "survival_time_coxph"),
args =
list(
object = quote(object),
new_data = quote(new_data)
)
)
)
parsnip::set_pred(
model = "proportional_hazards",
eng = "survival",
mode = "censored regression",
type = "survival",
value = list(
pre = cph_survival_pre,
post = NULL,
func = c(pkg = "censored", fun = "survival_prob_coxph"),
args =
list(
object = quote(object),
new_data = quote(new_data),
eval_time = rlang::expr(eval_time),
output = "surv",
interval = expr(interval),
conf.int = expr(level)
)
)
)
parsnip::set_pred(
model = "proportional_hazards",
eng = "survival",
mode = "censored regression",
type = "linear_pred",
value = list(
pre = NULL,
post = function(x, object) {
unname(x)
},
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
na.action = quote(stats::na.exclude),
reference = "zero"
)
)
)
}
make_proportional_hazards_glmnet <- function() {
parsnip::set_model_engine(
"proportional_hazards",
mode = "censored regression",
eng = "glmnet"
)
parsnip::set_dependency(
"proportional_hazards",
eng = "glmnet",
pkg = "glmnet",
mode = "censored regression"
)
parsnip::set_dependency(
"proportional_hazards",
eng = "glmnet",
pkg = "censored",
mode = "censored regression"
)
parsnip::set_fit(
model = "proportional_hazards",
eng = "glmnet",
mode = "censored regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "censored", fun = "coxnet_train"),
defaults = list()
)
)
parsnip::set_encoding(
model = "proportional_hazards",
eng = "glmnet",
mode = "censored regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
parsnip::set_model_arg(
model = "proportional_hazards",
eng = "glmnet",
parsnip = "penalty",
original = "lambda",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = TRUE
)
parsnip::set_model_arg(
model = "proportional_hazards",
eng = "glmnet",
parsnip = "mixture",
original = "alpha",
func = list(pkg = "dials", fun = "mixture"),
has_submodel = FALSE
)
parsnip::set_pred(
model = "proportional_hazards",
eng = "glmnet",
mode = "censored regression",
type = "linear_pred",
value = list(
pre = coxnet_prepare_x,
post = parsnip::.organize_glmnet_pred,
func = c(fun = "predict"),
args =
list(
object = expr(object$fit),
newx = expr(new_data),
type = "link",
s = expr(object$spec$args$penalty)
)
)
)
parsnip::set_pred(
model = "proportional_hazards",
eng = "glmnet",
mode = "censored regression",
type = "survival",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "censored", fun = "survival_prob_coxnet"),
args =
list(
object = expr(object),
new_data = expr(new_data),
eval_time = expr(eval_time),
penalty = expr(object$spec$args$penalty)
)
)
)
parsnip::set_pred(
model = "proportional_hazards",
eng = "glmnet",
mode = "censored regression",
type = "time",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "censored", fun = "survival_time_coxnet"),
args =
list(
object = quote(object),
new_data = quote(new_data),
penalty = expr(object$spec$args$penalty)
)
)
)
parsnip::set_pred(
model = "proportional_hazards",
eng = "glmnet",
mode = "censored regression",
type = "raw",
value = list(
pre = coxnet_prepare_x,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = expr(object$fit),
newx = expr(new_data)
)
)
)
}
# nocov end
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