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")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “lp”

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

• Required Packages: mlr3, mlr3learners, glmnet

Parameters

Id	Type	Default	Range	Levels
alignment	character	lambda	-	lambda, fraction
alpha	numeric	1	\([0, 1]\)	-
big	numeric	9.9e+35	\((-\infty, \infty)\)	-
devmax	numeric	0.999	\([0, 1]\)	-
dfmax	integer	-	\([0, \infty)\)	-
eps	numeric	1e-06	\([0, 1]\)	-
epsnr	numeric	1e-08	\([0, 1]\)	-
exclude	list	-	-	-
exmx	numeric	250	\((-\infty, \infty)\)	-
fdev	numeric	1e-05	\([0, 1]\)	-
foldid	list	NULL	-	-
gamma	list	-	-	-
grouped	logical	TRUE	-	TRUE, FALSE
intercept	logical	TRUE	-	TRUE, FALSE
keep	logical	FALSE	-	TRUE, FALSE
lambda	list	-	-	-
lambda.min.ratio	numeric	-	\([0, 1]\)	-
lower.limits	list	-Inf	-	-
maxit	integer	100000	\([1, \infty)\)	-
mnlam	integer	5	\([1, \infty)\)	-
mxit	integer	100	\([1, \infty)\)	-
mxitnr	integer	25	\([1, \infty)\)	-
nfolds	integer	10	\([3, \infty)\)	-
nlambda	integer	100	\([1, \infty)\)	-
offset	list	NULL	-	-
parallel	logical	FALSE	-	TRUE, FALSE
penalty.factor	list	-	-	-
pmax	integer	-	\([0, \infty)\)	-
pmin	numeric	1e-09	\([0, 1]\)	-
prec	numeric	1e-10	\((-\infty, \infty)\)	-
predict.gamma	numeric	gamma.1se	\((-\infty, \infty)\)	-
relax	logical	FALSE	-	TRUE, FALSE
s	numeric	lambda.1se	\([0, 1]\)	-
standardize	logical	TRUE	-	TRUE, FALSE
standardize.response	logical	FALSE	-	TRUE, FALSE
thresh	numeric	1e-07	\([0, \infty)\)	-
trace.it	integer	0	\([0, 1]\)	-
type.gaussian	character	-	-	covariance, naive
type.logistic	character	Newton	-	Newton, modified.Newton
type.measure	character	deviance	-	deviance, C
type.multinomial	character	ungrouped	-	ungrouped, grouped
upper.limits	list	Inf	-	-

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

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost, mlr_learners_surv.glmnet, mlr_learners_surv.ranger, mlr_learners_surv.xgboost

Super classes

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

Methods

Public methods

• LearnerSurvCVGlmnet$new()

• LearnerSurvCVGlmnet$selected_features()

• LearnerSurvCVGlmnet$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCVGlmnet$new()

---

Method selected_features()

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerSurvCVGlmnet$selected_features(lambda = NULL)

Arguments

lambda

(numeric(1))
Custom lambda, defaults to the active lambda depending on parameter set.

Returns

(character()) of feature names.

---

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", quietly = TRUE)) {
  learner = mlr3::lrn("surv.cv_glmnet")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerSurvCVGlmnet:surv.cv_glmnet>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3learners, glmnet
#> * Predict Type: crank
#> * Feature types: logical, integer, numeric
#> * Properties: selected_features, weights
#>  [1] "alignment"            "alpha"                "big"                 
#>  [4] "devmax"               "dfmax"                "eps"                 
#>  [7] "epsnr"                "exclude"              "exmx"                
#> [10] "fdev"                 "foldid"               "gamma"               
#> [13] "grouped"              "intercept"            "keep"                
#> [16] "lambda"               "lambda.min.ratio"     "lower.limits"        
#> [19] "maxit"                "mnlam"                "mxit"                
#> [22] "mxitnr"               "nfolds"               "nlambda"             
#> [25] "offset"               "parallel"             "penalty.factor"      
#> [28] "pmax"                 "pmin"                 "prec"                
#> [31] "predict.gamma"        "relax"                "s"                   
#> [34] "standardize"          "standardize.response" "thresh"              
#> [37] "trace.it"             "type.gaussian"        "type.logistic"       
#> [40] "type.measure"         "type.multinomial"     "upper.limits"