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")

Traits

  • Packages: randomForestSRC, pracma

  • Predict Types: crank, distr

  • Feature Types: logical, integer, numeric, factor

  • Properties: importance, missings, oob_error, weights

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

Author

RaphaelS1

Super classes

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

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvRandomForestSRC$new()


Method importance()

The importance scores are extracted from the model slot importance.

Usage

LearnerSurvRandomForestSRC$importance()

Returns

Named numeric().


Method selected_features()

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

Usage

LearnerSurvRandomForestSRC$selected_features()

Returns

character().


Method oob_error()

OOB error extracted from the model slot err.rate.

Usage

LearnerSurvRandomForestSRC$oob_error()

Returns

numeric().


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.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.rfsrc")) print(learner)
#> <LearnerSurvRandomForestSRC:surv.rfsrc> #> * Model: - #> * Parameters: list() #> * Packages: randomForestSRC, pracma #> * Predict Type: crank #> * Feature types: logical, integer, numeric, factor #> * Properties: importance, missings, oob_error, weights
# available parameters: learner$param_set$ids()
#> [1] "ntree" "mtry" "nodesize" "nodedepth" "splitrule" #> [6] "nsplit" "importance" "block.size" "ensemble" "bootstrap" #> [11] "samptype" "samp" "membership" "sampsize" "na.action" #> [16] "nimpute" "ntime" "cause" "proximity" "distance" #> [21] "forest.wt" "xvar.wt" "split.wt" "forest" "var.used" #> [26] "split.depth" "seed" "do.trace" "statistics" "get.tree" #> [31] "outcome" "ptn.count" "estimator"