Calls obliqueRSF::ORSF from package obliqueRSF.

Dictionary

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

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

Traits

  • Packages: obliqueRSF, pracma

  • Predict Types: crank, distr

  • Feature Types: integer, numeric, factor, ordered

  • Properties: missings, oob_error

Custom mlr3 defaults

  • verbose:

    • Actual default: TRUE

    • Adjusted default: FALSE

    • Reason for change: mlr3 already has it's own verbose set to TRUE by default

References

Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min Y, Mcclure LA, Howard G, Simon N (2019). “Oblique random survival forests.” The Annals of Applied Statistics, 13(3), 1847–1883. ISSN 1932-6157, 1941-7330, doi: 10.1214/19-AOAS1261, https://projecteuclid.org/euclid.aoas/1571277776.

See also

Author

adibender

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvObliqueRSF

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvObliqueRSF$new()


Method oob_error()

Integrated brier score OOB error extracted from the model slot oob_error. Concordance is also available.

Usage

LearnerSurvObliqueRSF$oob_error()

Returns

numeric().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvObliqueRSF$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.obliqueRSF")) print(learner)
#> <LearnerSurvObliqueRSF:surv.obliqueRSF> #> * Model: - #> * Parameters: verbose=FALSE #> * Packages: obliqueRSF, pracma #> * Predict Type: crank #> * Feature types: integer, numeric, factor, ordered #> * Properties: missings, oob_error
# available parameters: learner$param_set$ids()
#> [1] "alpha" "ntree" #> [3] "eval_times" "min_events_to_split_node" #> [5] "min_obs_to_split_node" "min_obs_in_leaf_node" #> [7] "min_events_in_leaf_node" "nsplit" #> [9] "gamma" "max_pval_to_split_node" #> [11] "mtry" "dfmax" #> [13] "use.cv" "verbose" #> [15] "compute_oob_predictions" "random_seed"