mlr_learners_surv.parametric: Survival Fully Parametric Learner

In mlr3learners/mlr3learners.survival: Learners from the {survival} package for 'mlr3'

Description

A mlr3proba::LearnerSurv implementing survreg from package survival. Calls survival::survreg().

Details

This learner allows you to choose a distribution and a model form to compose a predicted survival probability distribution. Note: Just because any combination of distribution and model form is possible, this does not mean it will necessarily be sensible or interpretable.

The internal predict method is implemented in this package as our implementation is more efficient for composition to distributions than survival::predict.survreg().

lp is predicted using the formula lp = Xβ where X are the variables in the test data set and β are the fitted coefficients.

The distribution distr is composed using the lp and specifying a model form in the type hyper-parameter. These are as follows, with respective survival functions,

• Accelerated Failure Time (aft)

  S(t) = S0(t/exp(lp))

• Proportional Hazards (ph)

  S(t) = S0(t)^exp(lp)

• Proportional Odds (po)

  S(t) = S0(t) / [exp(-lp) + S0(t) (1-exp(-lp))]

where S0 is the estimated baseline survival distribution (in this case with a given parametric form), and lp is the predicted linear predictor.

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")

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvParametric

Methods

Public methods

• LearnerSurvParametric$new()

• LearnerSurvParametric$clone()

Inherited methods

• 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

LearnerSurvParametric$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvParametric$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Kalbfleisch, J. D., & Prentice, R. L. (2011). The statistical analysis of failure time data (Vol. 360). John Wiley & Sons.

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

if (requireNamespace("survival")) {
  learner = mlr3::lrn("surv.nelson")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

mlr3learners/mlr3learners.survival documentation built on Aug. 9, 2020, 3:46 a.m.