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")

Traits

  • Packages: penalized, pracma

  • Predict Types: distr, crank

  • Feature Types: integer, numeric, factor, logical

  • Properties:

References

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

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPenalized

Methods

Public methods

Inherited methods

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

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.penalized")) print(learner)
#> <LearnerSurvPenalized:surv.penalized> #> * Model: - #> * Parameters: list() #> * Packages: penalized, pracma #> * Predict Type: distr #> * Feature types: integer, numeric, factor, logical #> * Properties: -
# available parameters: learner$param_set$ids()
#> [1] "unpenalized" "lambda1" "lambda2" "positive" "fusedl" #> [6] "startbeta" "startgamma" "steps" "epsilon" "maxiter" #> [11] "standardize" "trace"