Calls mboost::glmboost from package 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")

Traits

  • Packages: mboost, pracma

  • Predict Types: distr, crank, lp

  • Feature Types: integer, numeric, factor, logical

  • Properties: weights

References

Bühlmann P, Yu B (2003). “Boosting With the L2 Loss.” Journal of the American Statistical Association, 98(462), 324–339. doi: 10.1198/016214503000125

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGLMBoost

Methods

Public 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

LearnerSurvGLMBoost$new()


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

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.glmboost")) print(learner)
#> <LearnerSurvGLMBoost:surv.glmboost> #> * Model: - #> * Parameters: family=coxph #> * Packages: 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"