mlr3proba (version 0.2.4)

mlr_measures_surv.intloglossSE: Standard Error of Integrated Log loss Survival Measure

Description

Calculates the standard error of MeasureSurvIntLogloss.

If integrated == FALSE then the standard error of the loss, L, is approximated via, $$se(L) = sd(L)/\sqrt{N}$$ where \(N\) are the number of observations in the test set, and \(sd\) is the standard deviation.

If integrated == TRUE then correlations between time-points need to be taken into account, therefore $$se(L) = \sqrt{\frac{\sum_{i = 1}^M\sum_{j=1}^M \Sigma_{i,j}}{NT^2}}$$ where \(\Sigma_{i, j}\) is the sample covariance matrix over \(M\) distinct time-points.

Dictionary

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

MeasureSurvIntLoglossSE$new()
mlr_measures$get("surv.intloglossSE")
msr("surv.intloglossSE")

Meta Information

• Type: "surv"

• Range: \([0, \infty)\)

• Minimize: TRUE

• Required prediction: distr

Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> mlr3proba::MeasureSurvIntegrated -> MeasureSurvIntLoglossSE

Active bindings

eps

(numeric(1)) Very small number used to prevent log(0) error.

Methods

Public methods

• MeasureSurvIntLoglossSE$new()

• MeasureSurvIntLoglossSE$clone()

• mlr3::Measure$aggregate()

• mlr3::Measure$format()

• mlr3::Measure$help()

• mlr3::Measure$score()

• mlr3proba::MeasureSurv$print()

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvIntLoglossSE$new(integrated = TRUE, times, eps = 1e-15)

Arguments

integrated

(logical(1)) If TRUE (default), returns the integrated score; otherwise, not integrated.

times

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

eps

(numeric(1)) Very small number to set zero-valued predicted probabilities to in order to prevent errors in log(0) calculation.

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSurvIntLoglossSE$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Graf E, Schmoor C, Sauerbrei W, Schumacher M (1999). “Assessment and comparison of prognostic classification schemes for survival data.” Statistics in Medicine, 18(17-18), 2529--2545. 10.1002/(sici)1097-0258(19990915/30)18:17/18<2529::aid-sim274>3.0.co;2-5.

See Also

Other survival measures: mlr_measures_surv.beggC, mlr_measures_surv.calib_alpha, mlr_measures_surv.calib_beta, mlr_measures_surv.chambless_auc, mlr_measures_surv.cindex, mlr_measures_surv.gonenC, mlr_measures_surv.grafSE, mlr_measures_surv.graf, mlr_measures_surv.harrellC, mlr_measures_surv.hung_auc, mlr_measures_surv.intlogloss, mlr_measures_surv.logloss_se, mlr_measures_surv.logloss, mlr_measures_surv.maeSE, mlr_measures_surv.mae, mlr_measures_surv.mseSE, mlr_measures_surv.mse, mlr_measures_surv.nagelk_r2, mlr_measures_surv.oquigley_r2, mlr_measures_surv.rmseSE, 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.unoC, mlr_measures_surv.uno_auc, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr, mlr_measures_surv.xu_r2

Other Probabilistic survival measures: mlr_measures_surv.grafSE, mlr_measures_surv.graf, mlr_measures_surv.intlogloss, mlr_measures_surv.logloss_se, mlr_measures_surv.logloss, mlr_measures_surv.schmid

Other distr survival measures: mlr_measures_surv.calib_alpha, mlr_measures_surv.grafSE, mlr_measures_surv.graf, mlr_measures_surv.intlogloss, mlr_measures_surv.logloss_se, mlr_measures_surv.logloss, mlr_measures_surv.schmid