Survival Flexible Parametric Spline Learner

Flexible parametric spline learner. Calls flexsurv::flexsurvspline() from flexsurv.

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, \(\beta = (\beta_0,...,\beta_P)\), 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():

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

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”, “lp”

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

• Required Packages: mlr3, mlr3proba, mlr3extralearners, flexsurv, pracma

Parameters

Id	Type	Default	Levels	Range
bhazard	untyped	-		-
k	integer	0		\([0, \infty)\)
knots	untyped	-		-
bknots	untyped	-		-
scale	character	hazard	hazard, odds, normal	-
timescale	character	log	log, identity	-
inits	untyped	-		-
rtrunc	untyped	-		-
fixedpars	untyped	-		-
cl	numeric	0.95		\([0, 1]\)
maxiter	integer	30		\((-\infty, \infty)\)
rel.tolerance	numeric	1e-09		\((-\infty, \infty)\)
toler.chol	numeric	1e-10		\((-\infty, \infty)\)
debug	integer	0		\([0, 1]\)
outer.max	integer	10		\((-\infty, \infty)\)

Custom mlr3 defaults

• k:

  • Actual default: 0

  • Adjusted default: 1

  • Reason for change: The default value of 0 is equivalent to, and a much less efficient implementation of, LearnerSurvParametric.

References

Royston, Patrick, Parmar, KB M (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.

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 -> LearnerSurvFlexible

Methods

Public methods

• LearnerSurvFlexible$new()

• LearnerSurvFlexible$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

LearnerSurvFlexible$new()

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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.

Examples

learner = mlr3::lrn("surv.flexible")
print(learner)
#> <LearnerSurvFlexible:surv.flexible>: Flexible Parametric Splines
#> * Model: -
#> * Parameters: k=1
#> * Packages: mlr3, mlr3proba, mlr3extralearners, flexsurv, pracma
#> * Predict Types:  crank, [distr], lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: weights

# available parameters:
learner$param_set$ids()
#>  [1] "bhazard"       "k"             "knots"         "bknots"       
#>  [5] "scale"         "timescale"     "inits"         "rtrunc"       
#>  [9] "fixedpars"     "cl"            "maxiter"       "rel.tolerance"
#> [13] "toler.chol"    "debug"         "outer.max"