Calls flexsurv::flexsurvspline().

  • lp is predicted by using an internally defined predict method, see details

  • distr is predicted by using an internally defined predict method, see details

  • crank is identical to lp

Parameter k is changed to 1 and scale is changed to odds, as these are more in line with the Royston/Parmar proposed models, and the package defaults are equivalent to fitting a parametric model and therefore surv.parametric should be used instead.

If fitting a model with k = 0 then consider using surv.parametric as this is likely to have more optimal results, and has more options for tuning.

Details

The distr prediction is estimated using the fitted custom distributions from flexsurv::flexsurvspline() and the estimated coefficients.

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, \(\beta = (\beta_0,...,\beta_P)\), and covariates \(X^T = (X_0,...,X_P)^T\), where \(X_0\) is a column of \(1\)s: \(lp = \beta X\).

Dictionary

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

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

Meta Information

  • Type: "surv"

  • Predict Types: distr, lp, crank

  • Feature Types: logical, integer, factor, numeric

  • Properties: weights

  • Packages: flexsurv survival distr6 set6

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

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.