Calculates the right-censored logarithmic (log), loss.
The RCLL, in the context of probabilistic predictions, is defined by $$L(f, t, \Delta) = -log(\Delta f(t) + (1 - \Delta) S(t))$$ where \(\Delta\) is the censoring indicator.
Parameters
eps
(numeric(1)) - Value to set zero-valued scores to prevent log(0) errors, default1e-15
.se
(logical(1)) - IfTRUE
then returns standard error of the loss otherwise returns mean across all individual scores.ERV
(logical(1)) - IfTRUE
then the Explained Residual Variation method is applied, which means the score is standardised against a Kaplan-Meier baseline.na.rm
(logical(1)) - IfTRUE
(default) then removes any NAs in individual score calculations.
Dictionary
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
$new()
MeasureSurvRCLL$get("surv.rcll")
mlr_measuresmsr("surv.rcll")
References
Avati, A., Duan, T., Zhou, S., Jung, K., Shah, N. H., & Ng, A. (2018). Countdown Regression: Sharp and Calibrated Survival Predictions. http://arxiv.org/abs/1806.08324
See also
Other survival measures:
mlr_measures_surv.calib_alpha
,
mlr_measures_surv.calib_beta
,
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.rmse
,
mlr_measures_surv.schmid
,
mlr_measures_surv.song_auc
,
mlr_measures_surv.song_tnr
,
mlr_measures_surv.song_tpr
,
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.graf
,
mlr_measures_surv.intlogloss
,
mlr_measures_surv.logloss
,
mlr_measures_surv.schmid
Other distr survival measures:
mlr_measures_surv.calib_alpha
,
mlr_measures_surv.dcalib
,
mlr_measures_surv.graf
,
mlr_measures_surv.intlogloss
,
mlr_measures_surv.logloss
,
mlr_measures_surv.schmid
Super classes
mlr3::Measure
-> mlr3proba::MeasureSurv
-> MeasureSurvRCLL