mlr_learners_surv.cv_coxboost: Survival Cox Model with Cross-Validation Likelihood Based...

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival Cox Model with Cross-Validation Likelihood Based Boosting Learner

Description

Fits a survival Cox model using likelihood based boosting and internal cross-validation for the number of steps. Calls CoxBoost::CoxBoost() or CoxBoost::cv.CoxBoost() from package 'CoxBoost'.

Details

Use LearnerSurvCoxboost and LearnerSurvCVCoxboost for Cox boosting without and with internal cross-validation of boosting step number, respectively. Tuning using the internal optimizer in LearnerSurvCVCoxboost may be more efficient when tuning stepno only. However, for tuning multiple hyperparameters, mlr3tuning and LearnerSurvCoxboost will likely give better results.

If penalty == "optimCoxBoostPenalty" then CoxBoost::optimCoxBoostPenalty is used to determine the penalty value to be used in CoxBoost::cv.CoxBoost.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.cv_coxboost")

Meta Information

• Task type: “surv”

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

• Feature Types: “integer”, “numeric”

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

Parameters

Id	Type	Default	Levels	Range
maxstepno	integer	100		[0, \infty)
K	integer	10		[2, \infty)
type	character	verweij	verweij, naive	-
folds	untyped	NULL		-
minstepno	integer	50		[0, \infty)
start.penalty	numeric	-		(-\infty, \infty)
iter.max	integer	10		[1, \infty)
upper.margin	numeric	0.05		[0, 1]
unpen.index	untyped	-		-
standardize	logical	TRUE	TRUE, FALSE	-
penalty	numeric	-		(-\infty, \infty)
criterion	character	pscore	pscore, score, hpscore, hscore	-
stepsize.factor	numeric	1		(-\infty, \infty)
sf.scheme	character	sigmoid	sigmoid, linear	-
pendistmat	untyped	-		-
connected.index	untyped	-		-
x.is.01	logical	FALSE	TRUE, FALSE	-
return.score	logical	TRUE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
at.step	untyped	-		-

Installation

The package 'CoxBoost' is not on CRAN and has to be installed from GitHub using remotes::install_github("binderh/CoxBoost").

Prediction types

This learner returns three prediction types, using the internal predict.CoxBoost() function:

1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation.

2. crank: same as lp.

3. distr: a 2d survival matrix, with observations as rows and time points as columns. The internal transformation uses the Breslow estimator to compute the baseline hazard and compose the survival distributions from the lp predictions.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCVCoxboost

Methods

Public methods

• LearnerSurvCVCoxboost$new()

• LearnerSurvCVCoxboost$selected_features()

• LearnerSurvCVCoxboost$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCVCoxboost$new()

---

Method selected_features()

Returns the set of selected features which have non-zero coefficients. Calls the internal coef.CoxBoost() function.

Usage

LearnerSurvCVCoxboost$selected_features(at_step = NULL)

Arguments

at_step

(integer(1))
Which boosting step to get the coefficients for. If no step is given (default), the final boosting step is used.

Returns

(character()) vector of feature names.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCVCoxboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Binder, Harald, Allignol, Arthur, Schumacher, Martin, Beyersmann, Jan (2009). “Boosting for high-dimensional time-to-event data with competing risks.” Bioinformatics, 25(7), 890–896.

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.

Examples

task = tsk("rats")
task$col_roles$feature = c("litter", "rx")
learner = lrn("surv.cv_coxboost", maxstepno = 20)
splits = partition(task)
learner$train(task, splits$train)
pred = learner$predict(task, splits$test)

mlr-org/mlr3extralearners documentation built on Dec. 21, 2024, 2:21 p.m.