Boosted Generalized Linear Survival Learner

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

Id	Type	Default	Levels	Range
offset	numeric	-		\((-\infty, \infty)\)
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	-
custom.family	untyped	-		-
nuirange	untyped	c , 0 , 100		-
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		\([0, \infty)\)
nu	numeric	0.1		\([0, 1]\)
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped			-
stopintern	logical	FALSE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
sigma	numeric	0.1		\([0, 1]\)
ipcw	untyped	1		-
na.action	untyped	:: , stats , na.omit		-
contrasts.arg	untyped	-		-

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

• 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 -> LearnerSurvGLMBoost

Methods

Public methods

• LearnerSurvGLMBoost$new()

• LearnerSurvGLMBoost$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()

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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()

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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 Types:  crank, [distr], lp
#> * Feature Types: logical, integer, numeric, factor
#> * 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"