mlr_learners_surv.rfsrc: Survival Random Forest SRC Learner

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

Description

A mlr3proba::LearnerSurv implementing rfsrc from package randomForestSRC. Calls randomForestSRC::rfsrc().

Details

randomForestSRC::predict.rfsrc() returns both cumulative hazard function (chf) and survival function (surv) but uses different estimators to derive these. chf uses a bootstrapped Nelson-Aalen estimator, (Ishwaran, 2008) whereas surv uses a bootstrapped Kaplan-Meier estimator. The choice of which estimator to use is given by the extra estimator hyper-parameter, default is nelson.

Dictionary

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

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

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvRandomForestSRC

Methods

Public methods

• LearnerSurvRandomForestSRC$new()

• LearnerSurvRandomForestSRC$importance()

• LearnerSurvRandomForestSRC$selected_features()

• LearnerSurvRandomForestSRC$oob_error()

• LearnerSurvRandomForestSRC$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

LearnerSurvRandomForestSRC$new()

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Method importance()

The importance scores are extracted from the model slot importance.

Usage

LearnerSurvRandomForestSRC$importance()

Returns

Named numeric().

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Method selected_features()

Selected features are extracted from the model slot var.used.

Usage

LearnerSurvRandomForestSRC$selected_features()

Returns

character().

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Method oob_error()

OOB error extracted from the model slot err.rate.

Usage

LearnerSurvRandomForestSRC$oob_error()

Returns

numeric().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRandomForestSRC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS, others (2008). “Random survival forests.” The annals of applied statistics, 2(3), 841–860.

Breiman L (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi: 10.1023/A:1010933404324.

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

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

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

mlr3learners/mlr3learners.randomforestsrc documentation built on Aug. 3, 2020, 1:55 a.m.