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.

Format

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

Construction

MeasureSurvGrafSE$new(integrated = TRUE, times)
mlr_measures$get("surv.grafSE")
msr("surv.grafSE")
  • 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

Fields

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

References

Graf, E., Schmoor, C., Sauerbrei, W. and Schumacher, M. (1999).
Assessment and comparison of prognostic classification schemes for survival data.
Statistics in Medicine, 18(17), 2529-2545.
doi: 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5

See also