Survival Akritas Estimator Learner

Survival akritas estimator. Calls survivalmodels::akritas() from package 'survivalmodels'.

Dictionary

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

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

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

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

• Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6

Parameters

Id	Type	Default	Levels	Range
lambda	numeric	0.5		\([0, 1]\)
reverse	logical	FALSE	TRUE, FALSE	-

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

References

Akritas, G M (1994). “Nearest neighbor estimation of a bivariate distribution under random censoring.” The Annals of Statistics, 1299--1327.

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 -> LearnerSurvAkritas

Methods

Public methods

• LearnerSurvAkritas$new()

• LearnerSurvAkritas$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

LearnerSurvAkritas$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvAkritas$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.akritas")
print(learner)
#> <LearnerSurvAkritas:surv.akritas>: Akritas Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6
#> * Predict Type: crank
#> * Feature types: logical, integer, character, numeric, factor
#> * Properties: -

# available parameters:
learner$param_set$ids()
#> [1] "lambda"  "reverse"