Survival L1 and L2 Penalized Regression Learner

Calls penalized::penalized from package penalized.

Details

The penalized and unpenalized arguments in the learner are implemented slightly differently than in penalized::penalized(). Here, there is no parameter for penalized but instead it is assumed that every variable is penalized unless stated in the unpenalized parameter, see examples.

Dictionary

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

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

Meta Information

• Task type: “surv”

• Predict Types: “distr”, “crank”

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

• Required Packages: mlr3extralearners, penalized, pracma

Parameters

Id	Type	Default	Levels	Range
unpenalized	list	-		\((-\infty, \infty)\)
lambda1	list	0		\((-\infty, \infty)\)
lambda2	list	0		\((-\infty, \infty)\)
positive	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
fusedl	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
startbeta	numeric	-		\((-\infty, \infty)\)
startgamma	numeric	-		\((-\infty, \infty)\)
steps	integer	1		\([1, \infty)\)
epsilon	numeric	1e-10		\([0, 1]\)
maxiter	integer	-		\([1, \infty)\)
standardize	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
trace	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)

References

Goeman JJ (2009). “L1Penalized Estimation in the Cox Proportional Hazards Model.” Biometrical Journal doi: 10.1002/bimj.200900028.

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 -> LearnerSurvPenalized

Methods

Public methods

• LearnerSurvPenalized$new()

• LearnerSurvPenalized$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.

Usage

LearnerSurvPenalized$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvPenalized$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerSurvPenalized:surv.penalized>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, penalized, pracma
#> * Predict Type: distr
#> * Feature types: integer, numeric, factor, logical
#> * Properties: -
#>  [1] "unpenalized" "lambda1"     "lambda2"     "positive"    "fusedl"     
#>  [6] "startbeta"   "startgamma"  "steps"       "epsilon"     "maxiter"    
#> [11] "standardize" "trace"