Nothing
#' @export
makeRLearner.surv.coxph = function() {
makeRLearnerSurv(
cl = "surv.coxph",
package = "survival",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "ties", default = "efron", values = c("efron", "breslow", "exact")),
makeLogicalLearnerParam(id = "singular.ok", default = TRUE),
makeNumericLearnerParam(id = "eps", default = 1e-09, lower = 0),
makeNumericLearnerParam(id = "toler.chol", default = .Machine$double.eps^0.75, lower = 0),
makeIntegerLearnerParam(id = "iter.max", default = 20L, lower = 1L),
makeNumericLearnerParam(id = "toler.inf", default = sqrt(.Machine$double.eps^0.75), lower = 0),
makeIntegerLearnerParam(id = "outer.max", default = 10L, lower = 1L),
makeLogicalLearnerParam(id = "model", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "x", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "y", default = TRUE, tunable = FALSE)
),
properties = c("numerics", "factors", "weights"),
name = "Cox Proportional Hazard Model",
short.name = "coxph",
callees = c("coxph", "coxph.control")
)
}
#' @export
trainLearner.surv.coxph = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
data = getTaskData(.task, subset = .subset)
if (is.null(.weights)) {
survival::coxph(formula = f, data = data, ...)
} else {
survival::coxph(formula = f, data = data, weights = .weights, ...)
}
}
#' @export
predictLearner.surv.coxph = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata, type = "lp", ...)
}
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