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.


R6::R6Class() inheriting from MeasureSurvIntegrated/MeasureSurv.


MeasureSurvGrafSE$new(integrated = TRUE, times)
  • integrated :: logical(1)
    If TRUE (default), returns the integrated score; otherwise, not integrated.

  • times :: vector()
    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.

Meta Information

  • Type: "surv"

  • Range: \([0, \infty)\)

  • Minimize: TRUE

  • Required prediction: distr


See MeasureSurv, as well as all variables passed to the constructor.


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>;2-5 .

See also