Cross-Validated GLM with Elastic Net Regularization Survival Learner

Generalized linear models with elastic net regularization. Calls glmnet::cv.glmnet() from package glmnet.

The default for hyperparameter family is set to "cox".

Dictionary

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

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

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1--22. doi: 10.18637/jss.v033.i01 .

See also

Dictionary of Learners: mlr3::mlr_learners

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCVGlmnet

Methods

Public methods

• LearnerSurvCVGlmnet$new()

• LearnerSurvCVGlmnet$clone()

Inherited methods

• 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

LearnerSurvCVGlmnet$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCVGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("glmnet")) {
  learner = mlr3::lrn("surv.cv_glmnet")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerSurvCVGlmnet:surv.cv_glmnet>
#> * Model: -
#> * Parameters: list()
#> * Packages: glmnet
#> * Predict Type: crank
#> * Feature types: logical, integer, numeric
#> * Properties: weights
#>  [1] "offset"           "alpha"            "nfolds"           "foldid"          
#>  [5] "alignment"        "grouped"          "nlambda"          "lambda.min.ratio"
#>  [9] "lambda"           "type.measure"     "keep"             "parallel"        
#> [13] "trace.it"         "gamma"            "relax"            "standardize"     
#> [17] "intercept"        "thresh"           "dfmax"            "epsnr"           
#> [21] "pmax"             "exclude"          "penalty.factor"   "lower.limits"    
#> [25] "upper.limits"     "maxit"            "mxitnr"           "type.logistic"   
#> [29] "type.multinomial" "fdev"             "devmax"           "eps"             
#> [33] "big"              "mnlam"            "pmin"             "exmx"            
#> [37] "prec"             "mxit"             "s"