Skip to contents

Fits a generalized linear survival model using a boosting algorithm. Calls mboost::glmboost() 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.glmboost")
lrn("surv.glmboost")

Meta Information

  • Task type: “surv”

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

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

  • Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma

Parameters

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

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 -> LearnerSurvGLMBoost

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class. Importance is supported but fails tests as internally data is coerced to model matrix and original names can't be recovered.

Importance is supported but fails tests as internally data is coerced to model matrix and original names can't be recovered.

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

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvGLMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

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