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Fit a Survival Cox model with a likelihood based boosting algorithm. Calls CoxBoost::CoxBoost() from package '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.

Dictionary

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

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

Meta Information

Parameters

IdTypeDefaultLevelsRange
unpen.indexuntyped--
standardizelogicalTRUETRUE, FALSE-
stepnointeger100\([0, \infty)\)
penaltynumeric-\((-\infty, \infty)\)
criterioncharacterpscorepscore, score, hpscore, hscore-
stepsize.factornumeric1\((-\infty, \infty)\)
sf.schemecharactersigmoidsigmoid, linear-
pendistmatuntyped--
connected.indexuntyped--
x.is.01logicalFALSETRUE, FALSE-
return.scorelogicalTRUETRUE, FALSE-
tracelogicalFALSETRUE, FALSE-
at.stepuntyped--

Installation

The package 'CoxBoost' is not on CRAN and has to be installed from GitHub using remotes::install_github("binderh/CoxBoost").

References

Binder, Harald, Allignol, Arthur, Schumacher, Martin, Beyersmann, Jan (2009). “Boosting for high-dimensional time-to-event data with competing risks.” Bioinformatics, 25(7), 890--896.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCoxboost

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCoxboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.coxboost")
print(learner)
#> <LearnerSurvCoxboost:surv.coxboost>: Likelihood-based Boosting
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, CoxBoost, pracma
#> * Predict Type: distr
#> * Feature types: integer, numeric
#> * Properties: weights

# available parameters:
learner$param_set$ids()
#>  [1] "unpen.index"     "standardize"     "stepno"          "penalty"        
#>  [5] "criterion"       "stepsize.factor" "sf.scheme"       "pendistmat"     
#>  [9] "connected.index" "x.is.01"         "return.score"    "trace"          
#> [13] "at.step"