mlr_learners_surv.penalized: Survival L1 and L2 Penalized Regression Learner

In mlr3learners/mlr3learners.penalized: Learners from the {penalized} package for 'mlr3'

Description

A mlr3proba::LearnerSurv implementing penalized from package penalized. Calls penalized::penalized().

Details

The penalized and unpenalized arguments in the learner are implemented slightly differently than in penalized::penalized(). Here, there is no parameter for penalized but instead it is assumed that every variable is penalized unless stated in the unpenalized parameter, see examples.

Dictionary

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

mlr_learners$get("surv.penalized")
lrn("surv.penalized")

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPenalized

Methods

Public methods

• LearnerSurvPenalized$new()

• LearnerSurvPenalized$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvPenalized$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvPenalized$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Goeman JJ (2009). “L1Penalized Estimation in the Cox Proportional Hazards Model.” Biometrical Journal doi: 10.1002/bimj.200900028.

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

if (requireNamespace("penalized")) {
  learner = mlr3::lrn("surv.penalized")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

mlr3learners/mlr3learners.penalized documentation built on Aug. 4, 2020, 5:19 a.m.