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Model-based boosting for survival analysis. Calls mboost::mboost() from mboost.

Details

distr prediction made by mboost::survFit().

Dictionary

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

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

Meta Information

  • Task type: “surv”

  • Predict Types: “crank”, “distr”, “lp”

  • Feature Types: “logical”, “integer”, “numeric”, “factor”

  • Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost

Parameters

IdTypeDefaultLevelsRange
familycharactercoxphcoxph, weibull, loglog, lognormal, gehan, cindex, custom-
custom.familyuntyped--
nuirangeuntypedc , 0 , 100-
offsetnumeric-\((-\infty, \infty)\)
centerlogicalTRUETRUE, FALSE-
mstopinteger100\([0, \infty)\)
nunumeric0.1\([0, 1]\)
riskcharacterinbaginbag, oobag, none-
stopinternlogicalFALSETRUE, FALSE-
tracelogicalFALSETRUE, FALSE-
oobweightsuntyped-
baselearnercharacterbbsbbs, bols, btree-
sigmanumeric0.1\([0, 1]\)
ipcwuntyped1-
na.actionuntyped:: , stats , na.omit-

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324--339.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvMBoost

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method importance()

The importance scores are extracted with the function mboost::varimp() with the default arguments.

Usage

LearnerSurvMBoost$importance()

Returns

Named numeric().


Method selected_features()

Selected features are extracted with the function mboost::variable.names.mboost(), with used.only = TRUE.

Usage

LearnerSurvMBoost$selected_features()

Returns

character().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.mboost")
print(learner)
#> <LearnerSurvMBoost:surv.mboost>: Boosted Generalized Additive Model
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3proba, mlr3extralearners, mboost
#> * Predict Type: distr
#> * Feature types: integer, numeric, factor, logical
#> * Properties: importance, selected_features, weights

# available parameters:
learner$param_set$ids()
#>  [1] "family"        "custom.family" "nuirange"      "offset"       
#>  [5] "center"        "mstop"         "nu"            "risk"         
#>  [9] "stopintern"    "trace"         "oobweights"    "baselearner"  
#> [13] "sigma"         "ipcw"          "na.action"