Calculates the standard error of MeasureSurvGraf.
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.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
MeasureSurvGrafSE$new() mlr_measures$get("surv.grafSE") msr("surv.grafSE")
Type: "surv"
Range: \([0, \infty)\)
Minimize: TRUE
Required prediction: distr
mlr3::Measure
-> mlr3proba::MeasureSurv
-> mlr3proba::MeasureSurvIntegrated
-> MeasureSurvGrafSE
new()
Creates a new instance of this R6 class.
MeasureSurvGrafSE$new(integrated = TRUE, times)
integrated
(logical(1)
)
If TRUE
(default), returns the integrated score; otherwise, not integrated.
times
(numeric()
)
If integrate == TRUE
then a vector of time-points over which to integrate the score.
If integrate == FALSE
then a single time point at which to return the score.
clone()
The objects of this class are cloneable with this method.
MeasureSurvGrafSE$clone(deep = FALSE)
deep
Whether 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. 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.graf
,
mlr_measures_surv.harrellC
,
mlr_measures_surv.hung_auc
,
mlr_measures_surv.intloglossSE
,
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.graf
,
mlr_measures_surv.intloglossSE
,
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.graf
,
mlr_measures_surv.intloglossSE
,
mlr_measures_surv.intlogloss
,
mlr_measures_surv.logloss_se
,
mlr_measures_surv.logloss
,
mlr_measures_surv.schmid