GLM with Elastic Net Regularization Survival Learner

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

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

Details

Caution: This learner is different to learners calling glmnet::cv.glmnet() in that it does not use the internal optimization of parameter lambda. Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning). When lambda is tuned, the glmnet will be trained for each tuning iteration. While fitting the whole path of lambdas would be more efficient, as is done by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time (using parameter s) is currently not supported in mlr3 (at least not in efficient manner). Tuning the s parameter is, therefore, currently discouraged.

When the data are i.i.d. and efficiency is key, we recommend using the respective auto-tuning counterparts in mlr_learners_classif.cv_glmnet(), mlr_learners_regr.cv_glmnet(), or mlr_learners_surv.cv_glmnet(). However, in some situations this is not applicable, usually when data are imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires custom resampling strategies (blocked design, stratification).

Dictionary

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

mlr_learners$get("surv.glmnet")
lrn("surv.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	$[0, 1]$	-
exact	logical	FALSE	-	TRUE, FALSE
exclude	list	-	-	-
exmx	numeric	250	$(-\infty, \infty)$	-
fdev	numeric	1e-05	$[0, 1]$	-
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	$[1, \infty)$	-
newoffset	list	-	-	-
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	0.01	$[0, \infty)$	-
standardize	logical	TRUE	-	TRUE, FALSE
thresh	numeric	1e-07	$[0, \infty)$	-
trace.it	integer	0	$[0, 1]$	-
type.logistic	character	Newton	-	Newton, modified.Newton
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 .

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.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGlmnet

Methods

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvGlmnet$new()

Method `selected_features()`

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

Usage

LearnerSurvGlmnet$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

LearnerSurvGlmnet$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  learner = mlr3::lrn("surv.glmnet")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerSurvGlmnet:surv.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"              "devmax"          
#>  [5] "dfmax"            "eps"              "epsnr"            "exact"           
#>  [9] "exclude"          "exmx"             "fdev"             "gamma"           
#> [13] "grouped"          "intercept"        "keep"             "lambda"          
#> [17] "lambda.min.ratio" "lower.limits"     "maxit"            "mnlam"           
#> [21] "mxit"             "mxitnr"           "newoffset"        "nlambda"         
#> [25] "offset"           "parallel"         "penalty.factor"   "pmax"            
#> [29] "pmin"             "prec"             "predict.gamma"    "relax"           
#> [33] "s"                "standardize"      "thresh"           "trace.it"        
#> [37] "type.logistic"    "type.multinomial" "upper.limits"

Details

Dictionary

Meta Information

Parameters

References

See also

Super classes

Methods

Public methods

Method new()

Usage

Method selected_features()

Usage

Arguments

Returns

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `selected_features()`

Method `clone()`