mlr_learners_surv.coxph: Cox Proportional Hazards Survival Learner

In mlr-org/mlr3proba: Probabilistic Supervised Learning for 'mlr3'

Cox Proportional Hazards Survival Learner

Description

Calls survival::coxph().

• lp is predicted by survival::predict.coxph()

• distr is predicted by survival::survfit.coxph()

• crank is identical to lp

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerSurvCoxPH$new()
mlr_learners$get("surv.coxph")
lrn("surv.coxph")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”, “lp”

• Feature Types: “logical”, “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3proba, survival, distr6

Parameters

Id	Type	Default	Levels	Range
ties	character	efron	efron, breslow, exact	-
singular.ok	logical	TRUE	TRUE, FALSE	-
type	character	efron	efron, aalen, kalbfleisch-prentice	-
stype	integer	2		[1, 2]

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCoxPH

Methods

Public methods

• LearnerSurvCoxPH$new()

• LearnerSurvCoxPH$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCoxPH$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCoxPH$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Cox DR (1972). “Regression Models and Life-Tables.” Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.2517-6161.1972.tb00899.x")}.

See Also

Other survival learners: mlr_learners_surv.kaplan, mlr_learners_surv.rpart

mlr-org/mlr3proba documentation built on April 12, 2025, 4:38 p.m.