Survival Flexible Parametric Spline Learner

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

Dictionary

This Learner can be instantiated via lrn():

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	-
spline	character	rp	rp, splines2ns	-
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		\((-\infty, \infty)\)

Prediction types

This learner returns three prediction types:

1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using flexsurv::flexsurvspline() and the estimated coefficients. For fitted coefficients, \(\hat{\beta} = (\hat{\beta_0},...,\hat{\beta_P})\), and the test data covariates \(X^T = (X_0,...,X_P)^T\), where \(X_0\) is a column of \(1\)s, the linear predictor vector is \(lp = \hat{\beta} X^T\).

2. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using predict.flexsurvreg().

3. crank: same as lp.

Initial parameter values

• k:

  • Actual default: 0

  • Initial value: 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$base_learner()
• mlr3::Learner$encapsulate()
• 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

lrn("surv.flexible")
#> <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