Boosted Generalized Additive Survival Learner

Calls mboost::gamboost from package mboost.

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

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

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

Meta Information

• Task type: “surv”

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

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

• Required Packages: mlr3extralearners, mboost, pracma

• Task type: “surv”

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

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

• Required Packages: mlr3extralearners, mboost, pracma

Parameters

Id	Type	Default	Levels	Range
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	\((-\infty, \infty)\)
custom.family	list	-		\((-\infty, \infty)\)
nuirange	list	0, 100		\((-\infty, \infty)\)
offset	numeric	-		\((-\infty, \infty)\)
center	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
mstop	integer	100		\([0, \infty)\)
nu	numeric	0.1		\([0, 1]\)
risk	character	inbag	inbag, oobag, none	\((-\infty, \infty)\)
stopintern	list	FALSE		\((-\infty, \infty)\)
trace	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
oobweights	list	NULL		\((-\infty, \infty)\)
baselearner	character	bbs	bbs, bols, btree	\((-\infty, \infty)\)
dfbase	integer	4		\([0, \infty)\)
sigma	numeric	0.1		\([0, 1]\)
ipcw	list	1		\((-\infty, \infty)\)
na.action	list	function (object, ...) , UseMethod("na.omit")		\((-\infty, \infty)\)

Id	Type	Default	Levels	Range
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	\((-\infty, \infty)\)
custom.family	list	-		\((-\infty, \infty)\)
nuirange	list	0, 100		\((-\infty, \infty)\)
offset	numeric	-		\((-\infty, \infty)\)
center	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
mstop	integer	100		\([0, \infty)\)
nu	numeric	0.1		\([0, 1]\)
risk	character	inbag	inbag, oobag, none	\((-\infty, \infty)\)
stopintern	list	FALSE		\((-\infty, \infty)\)
trace	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
oobweights	list	NULL		\((-\infty, \infty)\)
baselearner	character	bbs	bbs, bols, btree	\((-\infty, \infty)\)
dfbase	integer	4		\([0, \infty)\)
sigma	numeric	0.1		\([0, 1]\)
ipcw	list	1		\((-\infty, \infty)\)
na.action	list	function (object, ...) , UseMethod("na.omit")		\((-\infty, \infty)\)

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

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGAMBoost

Methods

Public methods

• LearnerSurvGAMBoost$new()

• LearnerSurvGAMBoost$importance()

• LearnerSurvGAMBoost$selected_features()

• LearnerSurvGAMBoost$clone()

Inherited methods

• mlr3::Learner$base_learner()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvGAMBoost$new()

---

Method importance()

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

Usage

LearnerSurvGAMBoost$importance()

Returns

Named numeric().

---

Method selected_features()

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

Usage

LearnerSurvGAMBoost$selected_features()

Returns

character().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvGAMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerSurvGAMBoost:surv.gamboost>
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3extralearners, mboost, pracma
#> * 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] "dfbase"        "sigma"         "ipcw"          "na.action"