mlr_learners_surv.penalized.Rd
Calls penalized::penalized from package penalized.
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.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.penalized") lrn("surv.penalized")
Packages: penalized, pracma
Predict Types: distr, crank
Feature Types: integer, numeric, factor, logical
Properties:
Goeman JJ (2009). “L1Penalized Estimation in the Cox Proportional Hazards Model.” Biometrical Journal doi: 10.1002/bimj.200900028.
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvPenalized
new()
Creates a new instance of this R6 class.
LearnerSurvPenalized$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvPenalized$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.penalized")) print(learner)#> <LearnerSurvPenalized:surv.penalized> #> * Model: - #> * Parameters: list() #> * Packages: penalized, pracma #> * Predict Type: distr #> * Feature types: integer, numeric, factor, logical #> * Properties: -# available parameters: learner$param_set$ids()#> [1] "unpenalized" "lambda1" "lambda2" "positive" "fusedl" #> [6] "startbeta" "startgamma" "steps" "epsilon" "maxiter" #> [11] "standardize" "trace"