Boosted Generalized Additive Survival Learner

Fits a generalized additive survival model using a boosting algorithm. Calls mboost::gamboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.gamboost")

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
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
offset	numeric	-		\((-\infty, \infty)\)
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		\([0, \infty)\)
nu	numeric	0.1		\([0, 1]\)
risk	character	inbag	inbag, oobag, none	-
stopintern	untyped	FALSE		-
trace	logical	FALSE	TRUE, FALSE	-
oobweights	untyped	NULL		-
baselearner	character	bbs	bbs, bols, btree	-
dfbase	integer	4		\([0, \infty)\)
sigma	numeric	0.1		\([0, 1]\)
ipcw	untyped	1		-
na.action	untyped	stats::na.omit		-

Prediction types

This learner returns two to three prediction types:

1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using mboost::predict.gamboost(). If the family parameter is not "coxph", -lp is returned, since non-coxph families represent AFT-style distributions where lower lp values indicate higher risk.

2. crank: same as lp.

3. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using mboost::survFit(). This prediction type is present only when the family distribution parameter is equal to "coxph" (default). By default the Breslow estimator is used for computing the baseline hazard.

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 -> LearnerSurvGAMBoost

Methods

Public methods

• LearnerSurvGAMBoost$new()

• LearnerSurvGAMBoost$importance()

• LearnerSurvGAMBoost$selected_features()

• LearnerSurvGAMBoost$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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

lrn("surv.gamboost")
#> <LearnerSurvGAMBoost:surv.gamboost>: Boosted Generalized Additive Model
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma
#> * Predict Types:  [crank], distr, lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: importance, selected_features, weights