Survival Akritas Estimator Learner

Survival akritas estimator. Calls survivalmodels::akritas() from package 'survivalmodels'.

Prediction types

This learner returns two prediction types:

1. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.akritas() function. The survival matrix uses the unique time points from the training set. We advise to set the parameter ntime which allows to adjust the granularity of these time points to a reasonable number (e.g. 150). This avoids large execution times during prediction.

2. crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.akritas")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

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

• Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6

Parameters

Id	Type	Default	Levels	Range
lambda	numeric	0.5		\([0, 1]\)
reverse	logical	FALSE	TRUE, FALSE	-
ntime	integer	NULL		\([1, \infty)\)
round_time	integer	2		\([0, \infty)\)

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

References

Akritas, G M (1994). “Nearest neighbor estimation of a bivariate distribution under random censoring.” The Annals of Statistics, 1299–1327.

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 -> LearnerSurvAkritas

Methods

Public methods

• LearnerSurvAkritas$new()

• LearnerSurvAkritas$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

LearnerSurvAkritas$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvAkritas$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("surv.akritas")
print(learner)
#> 
#> ── <LearnerSurvAkritas> (surv.akritas): Akritas Estimator ──────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, and distr6
#> • Predict Types: [crank] and distr
#> • Feature Types: logical, integer, numeric, character, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties:
#> • Other settings: use_weights = 'error'

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> 
#>  Akritas Estimator 
#> 
#> Call:
#>   survivalmodels::akritas(data = data.table::setDF(task$data()),      time_variable = task$target_names[1L], status_variable = task$target_names[2L])
#> 
#> Response:
#>   Surv(time, status)
#> Features:
#>   {age, los, revasc, revascdays, stchange, sysbp} 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> surv.cindex 
#>         0.5