Calls survival::survfit().

  • distr is predicted by estimating the cumulative hazard function with survival::survfit()

  • crank is predicted as the expectation of the survival distribution, distr

Dictionary

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

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

Meta Information

  • Type: "surv"

  • Predict Types: crank, distr

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

  • Properties: missings

  • Packages: survival distr6

References

Nelson W (1969). “Hazard Plotting for Incomplete Failure Data.” Journal of Quality Technology, 1(1), 27--52. doi: 10.1080/00224065.1969.11980344 .

Nelson W (1972). “Theory and Applications of Hazard Plotting for Censored Failure Data.” Technometrics, 14(4), 945--966. doi: 10.1080/00401706.1972.10488991 .

Aalen O (1978). “Nonparametric Inference for a Family of Counting Processes.” The Annals of Statistics, 6(4), 701--726. http://www.jstor.org/stable/2958850.

See also

Super classes

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

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvNelson$new()


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.