Survival Nelson-Aalen Estimator Learner

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

Dictionary

This Learner can be instantiated via lrn():

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

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.

Author

RaphaelS1

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.

Examples

# Define the Learner
learner = mlr3::lrn("surv.nelson")
print(learner)
#> <LearnerSurvNelson:surv.nelson>: Nelson-Aalen Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, survival, pracma
#> * Predict Types:  [crank], distr
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: missings

# 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$model)
#> Call: survfit(formula = task$formula(1), data = task$data())
#> 
#>        n events median 0.95LCL 0.95UCL
#> [1,] 670    212     NA      NA      NA


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

# Score the predictions
predictions$score()
#> surv.cindex 
#>         0.5