mlr_learners_surv.glmnet: GLM with Elastic Net Regularization Survival Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

GLM with Elastic Net Regularization Survival Learner

Description

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

Details

This learner returns two prediction types:

1. lp: a vector of linear predictors (relative risk scores), one per observation. Calculated using glmnet::predict.coxnet().

2. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using glmnet::survfit.coxnet(). Parameters stype and ctype relate to how lp predictions are transformed into survival predictions and are described in survival::survfit.coxph(). By default the Breslow estimator is used.

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 counterpart in 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).

Custom mlr3 parameters

• family is set to "cox" and cannot be changed.

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”, “distr”, “lp”

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

• Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet

Parameters

Id	Type	Default	Levels	Range
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	untyped	-		-
exmx	numeric	250		(-\infty, \infty)
fdev	numeric	1e-05		[0, 1]
gamma	untyped	-		-
grouped	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
lambda	untyped	-		-
lambda.min.ratio	numeric	-		[0, 1]
lower.limits	untyped	-Inf		-
maxit	integer	100000		[1, \infty)
mnlam	integer	5		[1, \infty)
mxit	integer	100		[1, \infty)
mxitnr	integer	25		[1, \infty)
newoffset	untyped	-		-
nlambda	integer	100		[1, \infty)
offset	untyped	NULL		-
parallel	logical	FALSE	TRUE, FALSE	-
penalty.factor	untyped	-		-
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	untyped	Inf		-
stype	integer	2		[1, 2]
ctype	integer	-		[1, 2]

Super classes

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

Methods

Public methods

• LearnerSurvGlmnet$new()

• LearnerSurvGlmnet$selected_features()

• LearnerSurvGlmnet$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

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.

Author(s)

be-marc

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v033.i01")}.

See Also

• Dictionary of Learners: mlr3::mlr_learners.

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

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

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

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

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

Examples

learner = mlr3::lrn("surv.glmnet")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3extralearners documentation built on April 13, 2024, 5:25 a.m.