| mlr_measures_surv.intloglossSE {mlr3proba} | R Documentation |
Calculates the standard error of MeasureSurvIntLogloss.
If integrated == FALSE then the standard error of the loss, L, is approximated via,
se(L) = sd(L)/√ 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) = √((∑_{i = 1}^M∑_{j=1}^M cov(T_i, T_j)) / (NT^2))
where cov(T_i, T_j) is the sample covariance matrix over M distinct time-points.
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")
Type: "surv"
Range: [0, Inf)
Minimize: TRUE
Required prediction: distr
mlr3::Measure -> mlr3proba::MeasureSurv -> mlr3proba::MeasureSurvIntegrated -> MeasureSurvIntLoglossSE
eps(numeric(1))
Very small number used to prevent log(0) error.
new()Creates a new instance of this R6 class.
MeasureSurvIntLoglossSE$new(integrated = TRUE, times, eps = 1e-15)
integrated(logical(1))
If TRUE (default), returns the integrated score; otherwise, not integrated.
times(numeric())
If integrate == TRUE then a vector of time-points over which to integrate the score.
If integrate == FALSE then a single time point at which to return the score.
eps(numeric(1))
Very small number to set zero-valued predicted probabilities to in order to prevent errors
in log(0) calculation.
clone()The objects of this class are cloneable with this method.
MeasureSurvIntLoglossSE$clone(deep = FALSE)
deepWhether to make a deep clone.
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. doi: 10.1002/(sici)1097-0258(19990915/30)18:17/18<2529::aid-sim274>3.0.co;2-5.
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