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Fits a survival Cox model using likelihood based boosting and interal cross-validation for the number of steps. Calls CoxBoost::CoxBoost() or CoxBoost::cv.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.

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():

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

Meta Information

Parameters

IdTypeDefaultLevelsRange
maxstepnointeger100\([0, \infty)\)
Kinteger10\([2, \infty)\)
typecharacterverweijverweij, naive-
foldsuntyped-
minstepnointeger50\([0, \infty)\)
start.penaltynumeric-\((-\infty, \infty)\)
iter.maxinteger10\([1, \infty)\)
upper.marginnumeric0.05\([0, 1]\)
unpen.indexuntyped--
standardizelogicalTRUETRUE, FALSE-
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 -> LearnerSurvCVCoxboost

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

LearnerSurvCVCoxboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.cv_coxboost")
print(learner)
#> <LearnerSurvCVCoxboost:surv.cv_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] "maxstepno"       "K"               "type"            "folds"          
#>  [5] "minstepno"       "start.penalty"   "iter.max"        "upper.margin"   
#>  [9] "unpen.index"     "standardize"     "penalty"         "criterion"      
#> [13] "stepsize.factor" "sf.scheme"       "pendistmat"      "connected.index"
#> [17] "x.is.01"         "return.score"    "trace"           "at.step"