mlr_learners_surv.nelson: Survival Nelson-Aalen Estimator Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival Nelson-Aalen Estimator Learner

Description

Non-parametric estimator of the cumulative hazard rate function. Calls survival::survfit() from survival.

Dictionary

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

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

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, survival, pracma

Parameters

Empty ParamSet

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvNelson

Methods

Public methods

• LearnerSurvNelson$new()

• LearnerSurvNelson$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()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvNelson$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvNelson$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Nelson, Wayne (1969). “Hazard plotting for incomplete failure data.” Journal of Quality Technology, 1(1), 27–52.

Nelson, Wayne (1972). “Theory and applications of hazard plotting for censored failure data.” Technometrics, 14(4), 945–966.

Aalen, Odd (1978). “Nonparametric inference for a family of counting processes.” The Annals of Statistics, 701–726.

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.nelson")
print(learner)

# available parameters:
learner$param_set$ids()

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