mlr_learners_surv.cv_glmnet: Cross-Validated GLM with Elastic Net Regularization Survival...

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Cross-Validated GLM with Elastic Net Regularization Survival Learner

Description

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

Initial parameter values

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

Prediction types

This learner returns three prediction types:

1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using glmnet::predict.cv.glmnet().

2. crank: same as lp.

3. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using glmnet::survfit.cv.glmnet(). 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 for computing the baseline hazard.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.cv_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]
exclude	untyped	-		-
exmx	numeric	250		(-\infty, \infty)
fdev	numeric	1e-05		[0, 1]
foldid	untyped	NULL		-
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)
nfolds	integer	10		[3, \infty)
nlambda	integer	100		[1, \infty)
offset	untyped	NULL		-
newoffset	untyped	-		-
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	lambda.1se		[0, \infty)
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	untyped	Inf		-
stype	integer	2		[1, 2]
ctype	integer	-		[1, 2]

Super classes

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

Methods

Public methods

• LearnerSurvCVGlmnet$new()

• LearnerSurvCVGlmnet$selected_features()

• LearnerSurvCVGlmnet$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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.

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

# Define the Learner
learner = mlr3::lrn("surv.cv_glmnet")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.