Skip to contents

Calls mboost::mboost 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.mboost")
lrn("surv.mboost")

Meta Information

  • Task type: “surv”

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

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

  • Required Packages: mlr3extralearners, mboost

Parameters

IdTypeDefaultLevelsRange
familycharactercoxphcoxph, weibull, loglog, lognormal, gehan, cindex, custom\((-\infty, \infty)\)
custom.familylist-\((-\infty, \infty)\)
nuirangelist0, 100\((-\infty, \infty)\)
offsetnumeric-\((-\infty, \infty)\)
centerlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
mstopinteger100\([0, \infty)\)
nunumeric0.1\([0, 1]\)
riskcharacterinbaginbag, oobag, none\((-\infty, \infty)\)
stopinternlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
tracelogicalFALSETRUE, FALSE\((-\infty, \infty)\)
oobweightslistNULL\((-\infty, \infty)\)
baselearnercharacterbbsbbs, bols, btree\((-\infty, \infty)\)
sigmanumeric0.1\([0, 1]\)
ipcwlist1\((-\infty, \infty)\)
na.actionlistfunction (object, ...) , UseMethod("na.omit")\((-\infty, \infty)\)
contrasts.argslist-\((-\infty, \infty)\)

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 -> 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

if (requireNamespace("mboost", quietly = TRUE)) {
  learner = mlr3::lrn("surv.mboost")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerSurvMBoost:surv.mboost>
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3extralearners, mboost
#> * Predict Type: distr
#> * Feature types: integer, numeric, factor, logical
#> * Properties: importance, selected_features, weights
#>  [1] "family"         "custom.family"  "nuirange"       "offset"        
#>  [5] "center"         "mstop"          "nu"             "risk"          
#>  [9] "stopintern"     "trace"          "oobweights"     "baselearner"   
#> [13] "sigma"          "ipcw"           "na.action"      "contrasts.args"