#' @export
makeRLearner.surv.gamboost = function() {
makeRLearnerSurv(
cl = "surv.gamboost",
package = c("!survival", "mboost"),
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "baselearner", values = c("bbs", "bols", "btree")),
makeIntegerLearnerParam(id = "dfbase", default = 4L),
makeNumericLearnerParam(id = "offset"),
makeDiscreteLearnerParam(id = "family", default = "CoxPH", values = c("CoxPH", "Weibull", "Loglog", "Lognormal", "Gehan", "custom.family")),
makeNumericVectorLearnerParam(id = "nuirange", default = c(0, 100), requires = quote(family %in% c("Weibull", "Loglog", "Lognormal"))),
makeUntypedLearnerParam(id = "custom.family.definition", requires = quote(family == "custom.family")),
makeIntegerLearnerParam(id = "mstop", default = 100L, lower = 1L),
makeNumericLearnerParam(id = "nu", default = 0.1, lower = 0, upper = 1),
makeDiscreteLearnerParam(id = "risk", values = c("inbag", "oobag", "none")),
makeLogicalLearnerParam(id = "stopintern", default = FALSE),
# 'risk' and 'stopintern' will be kept for completeness sake
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE)
),
par.vals = list(
family = "CoxPH"
),
properties = c("numerics", "factors", "ordered", "weights"),
name = "Gradient boosting with smooth components",
short.name = "gamboost",
note = "`family` has been set to `CoxPH()` by default.",
callees = c("gamboost", "mboost_fit", "boost_control", "CoxPH", "Weibull", "Loglog", "Lognormal", "Gehan")
)
}
#' @export
trainLearner.surv.gamboost = function(.learner, .task, .subset, .weights = NULL, nuirange = c(0, 100), family, custom.family.definition, mstop, nu, risk, stopintern, trace, ...) {
requirePackages("mboost", why = "argument 'baselearner' requires package", suppress.warnings = TRUE)
ctrl = learnerArgsToControl(mboost::boost_control, mstop, nu, risk, trace, stopintern)
family = switch(family,
CoxPH = mboost::CoxPH(),
Weibull = mboost::Weibull(nuirange = nuirange),
Loglog = mboost::Loglog(nuirange = nuirange),
Lognormal = mboost::Lognormal(nuirange = nuirange),
Gehan = mboost::Gehan(),
custom.family = custom.family.definition
)
f = getTaskFormula(.task)
data = getTaskData(.task, subset = .subset, recode.target = "surv")
if (is.null(.weights)) {
model = mboost::gamboost(f, data = data, control = ctrl, family = family, ...)
} else {
model = mboost::gamboost(f, data = getTaskData(.task, subset = .subset, recode.target = "surv"), control = ctrl, weights = .weights, family = family, ...)
}
}
#' @export
predictLearner.surv.gamboost = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata, type = "link")
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.