Competing Risks Learner

This Learner specializes Learner for competing risks problems:

• task_type is set to "cmprsk"

• Creates Predictions of class PredictionCompRisks.

• The only currently available option for predict_types is "cif", which represents the predicted cumulative incidence function for each observation in the test set.

Super class

mlr3::Learner -> LearnerCompRisks

Methods

Public methods

• LearnerCompRisks$new()

• LearnerCompRisks$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerCompRisks$new(
  id,
  param_set = ps(),
  predict_types = "cif",
  feature_types = character(),
  properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)

Arguments

id

(character(1))
Identifier for the new instance.

param_set

(paradox::ParamSet)
Set of hyperparameters.

predict_types

(character())
Supported predict types. Must be a subset of mlr_reflections$learner_predict_types.

feature_types

(character())
Feature types the learner operates on. Must be a subset of mlr_reflections$task_feature_types.

properties

(character())
Set of properties of the Learner (see initialization method $new(). Must be a subset of mlr_reflections$learner_properties.

packages

(character())
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand via requireNamespace().

label

(character(1))
Label for the new instance.

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. The referenced help package can be opened via method $help().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerCompRisks$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
# get all survival learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^cmprsk"))
names(lrns)
#> [1] "cmprsk.aalen"

# get a specific learner from mlr_learners:
mlr_learners$get("cmprsk.aalen")
#> 
#> ── <LearnerCompRisksAalenJohansen> (cmprsk.aalen): Aalen Johansen Estimator ────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3cmprsk, and survival
#> • Predict Types: [cif]
#> • Feature Types: logical, integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: weights
#> • Other settings: use_weights = 'use'
lrn("cmprsk.aalen")
#> 
#> ── <LearnerCompRisksAalenJohansen> (cmprsk.aalen): Aalen Johansen Estimator ────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3cmprsk, and survival
#> • Predict Types: [cif]
#> • Feature Types: logical, integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: weights
#> • Other settings: use_weights = 'use'