Calls penalized::penalized().

  • distr is predicted using penalized::predict()

  • crank is predicted as the expectation of the survival distribution, distr

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.

Format

R6::R6Class() inheriting from LearnerSurv.

Construction

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

Meta Information

  • Type: "surv"

  • Predict Types: distr, crank

  • Feature Types: integer, numeric, factor, ordered

  • Packages: penalized distr6

References

Goeman, J. J., L1 penalized estimation in the Cox proportional hazards model. Biometrical Journal 52(1), 7084.

See also

Examples

library(mlr3) task = tgen("simsurv")$generate(200) learner = lrn("surv.penalized") resampling = rsmp("cv", folds = 3) resample(task, learner, resampling)
#> <ResampleResult> of 3 iterations #> * Task: simsurv #> * Learner: surv.penalized #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations
# specifying penalized and unpenalized variables task = tgen("simsurv")$generate(200) learner = lrn("surv.penalized", unpenalized = c("height")) learner$train(task) learner$model@penalized
#> treatment weight #> -0.05306844 -0.02195813
learner$model@unpenalized
#> height #> -0.001442097