mlr_measures_surv.uno_auc: Uno's AUC Survival Measure

In mlr-org/mlr3proba: Probabilistic Supervised Learning for 'mlr3'

Uno's AUC Survival Measure

Description

Calls survAUC::AUC.uno().

Assumes random censoring.

Details

All measures implemented from survAUC should be used with care, we are aware of problems in implementation that sometimes cause fatal errors in R. In future updates some of these measures may be re-written and implemented directly in mlr3proba.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

MeasureSurvUnoAUC$new()
mlr_measures$get("surv.uno_auc")
msr("surv.uno_auc")

Parameters

Id	Type	Default	Levels
integrated	logical	TRUE	TRUE, FALSE
times	untyped	-

Meta Information

• Type: "surv"

• Range: [0, 1]

• Minimize: FALSE

• Required prediction: lp

Parameter details

• integrated (logical(1))
  If TRUE (default), returns the integrated score (eg across time points); otherwise, not integrated (eg at a single time point).

• times (numeric())
  If integrated == TRUE then a vector of time-points over which to integrate the score. If integrated == FALSE then a single time point at which to return the score.

Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> mlr3proba::MeasureSurvAUC -> MeasureSurvUnoAUC

Methods

Public methods

• MeasureSurvUnoAUC$new()

• MeasureSurvUnoAUC$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()

---

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvUnoAUC$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSurvUnoAUC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Uno H, Cai T, Tian L, Wei LJ (2007). “Evaluating Prediction Rules fort-Year Survivors With Censored Regression Models.” Journal of the American Statistical Association, 102(478), 527–537. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/016214507000000149")}.

See Also

Other survival measures: mlr_measures_surv.calib_alpha, mlr_measures_surv.calib_beta, mlr_measures_surv.calib_index, mlr_measures_surv.chambless_auc, mlr_measures_surv.cindex, mlr_measures_surv.dcalib, mlr_measures_surv.graf, mlr_measures_surv.hung_auc, mlr_measures_surv.intlogloss, mlr_measures_surv.logloss, mlr_measures_surv.mae, mlr_measures_surv.mse, mlr_measures_surv.nagelk_r2, mlr_measures_surv.oquigley_r2, mlr_measures_surv.rcll, mlr_measures_surv.rmse, mlr_measures_surv.schmid, mlr_measures_surv.song_auc, mlr_measures_surv.song_tnr, mlr_measures_surv.song_tpr, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr, mlr_measures_surv.xu_r2

Other AUC survival measures: mlr_measures_surv.chambless_auc, mlr_measures_surv.hung_auc, mlr_measures_surv.song_auc, mlr_measures_surv.song_tnr, mlr_measures_surv.song_tpr, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr

Other lp survival measures: mlr_measures_surv.calib_beta, mlr_measures_surv.chambless_auc, mlr_measures_surv.hung_auc, mlr_measures_surv.nagelk_r2, mlr_measures_surv.oquigley_r2, mlr_measures_surv.song_auc, mlr_measures_surv.song_tnr, mlr_measures_surv.song_tpr, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr, mlr_measures_surv.xu_r2

Examples

library(mlr3)

# Define a survival Task
task = tsk("lung")

# Create train and test set
part = partition(task)

# Train Cox learner on the train set
cox = lrn("surv.coxph")
cox$train(task, row_ids = part$train)

# Make predictions for the test set
p = cox$predict(task, row_ids = part$test)

# Integrated AUC score
p$score(msr("surv.uno_auc"), task = task,
        train_set = part$train, learner = cox)

# AUC at specific time point
p$score(msr("surv.uno_auc", times = 600), task = task,
        train_set = part$train, learner = cox)

# Integrated AUC at specific time points
p$score(msr("surv.uno_auc", times = c(100, 200, 300, 400, 500)),
        task = task, train_set = part$train, learner = cox)

mlr-org/mlr3proba documentation built on April 12, 2025, 4:38 p.m.