Calculates the standard error of MeasureSurvIntLogloss.

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.

## Dictionary

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")

## Meta Information

• Type: "surv"

• Range: $$[0, \infty)$$

• Minimize: TRUE

• Required prediction: distr

## References

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 .

## Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> mlr3proba::MeasureSurvIntegrated -> MeasureSurvIntLoglossSE

## Active bindings

eps

(numeric(1))
Very small number used to prevent log(0) error.

## Methods

### Public methods

Inherited methods

### Method new()

Creates a new instance of this R6 class.

#### Arguments

deep

Whether to make a deep clone.