mlr_learners_surv.flexible: Survival Flexible Parametric Spline Learner

Description Details Custom mlr3 defaults Dictionary Super classes Methods References See Also Examples

Description

A mlr3proba::LearnerSurv implementing flexible from package flexsurv. Calls flexsurv::flexsurvspline().

Details

The distr prediction is estimated using the fitted custom distributions from flexsurv::flexsurvspline() and the estimated coefficients however the prediction takes place in this package and not in flexsurv for a much faster and more efficient implementation.

As flexible spline models estimate the baseline hazard as the intercept, the linear predictor, lp, can be calculated as in the classical setting. i.e. For fitted coefficients, β = (β0,...,βP), and covariates X^T = (X0,...,XP)^T, where X0 is a column of 1s: lp = βX.

Custom mlr3 defaults

Dictionary

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

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

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvFlexible

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerSurvFlexible$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerSurvFlexible$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Royston P, Parmar MKB (2002). “Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.” Statistics in Medicine, 21(15), 2175–2197. doi: 10.1002/sim.1203.

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

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if (requireNamespace("flexsurv")) {
  learner = mlr3::lrn("surv.flexible")
  print(learner)

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

mlr3learners/mlr3learners.flexsurv documentation built on Aug. 8, 2020, 6:49 p.m.