mlr_learners_surv.cvcoxboost: Survival Cox Model with Cross-Validation Likelihood Based...

Description Details Dictionary Super classes Methods References See Also Examples

Description

A mlr3proba::LearnerSurv implementing cvcoxboost from package CoxBoost. Calls CoxBoost::cv.CoxBoost().

Details

Use LearnerSurvCoxboost and LearnerSurvCVCoxboost for Cox boosting without and with internal cross-validation of boosting step number, respectively. Tuning using the internal optimizer in LearnerSurvCVCoxboost may be more efficient when tuning stepno only. However, for tuning multiple hyperparameters, mlr3tuning and LearnerSurvCoxboost will likely give better results.

If penalty == "optimCoxBoostPenalty" then CoxBoost::optimCoxBoostPenalty is used to determine the penalty value to be used in CoxBoost::cv.CoxBoost.

Dictionary

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

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

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCVCoxboost

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerSurvCVCoxboost$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerSurvCVCoxboost$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Binder, H., Allignol, A., Schumacher, M., and Beyersmann, J. (2009). Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics, 25:890-896.

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

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

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

mlr3learners/mlr3learners.coxboost documentation built on July 31, 2020, 10:17 p.m.