R: Standard Error of Integrated Graf Score Survival Measure

mlr_measures_surv.grafSE {mlr3proba} | R Documentation

## Standard Error of Integrated Graf Score Survival Measure

### Description

Calculates the standard error of MeasureSurvGraf.

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.

### Dictionary

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")

### Meta Information

- Type: `"surv"`

- Range: [0, Inf)

- Minimize: `TRUE`

- Required prediction: `distr`

### Super classes

`mlr3::Measure` -> `mlr3proba::MeasureSurv` -> `mlr3proba::MeasureSurvIntegrated` -> `MeasureSurvGrafSE`

### Methods

#### Public methods

- `MeasureSurvGrafSE$new()`

- `MeasureSurvGrafSE$clone()`

Inherited methods

- `mlr3::Measure$aggregate()`

- `mlr3::Measure$format()`

- `mlr3::Measure$help()`

- `mlr3::Measure$score()`

- `mlr3proba::MeasureSurv$print()`

#### Method `new()`

Creates a new instance of this R6 class.

##### Usage

    MeasureSurvGrafSE$new(integrated = TRUE, times)

##### Arguments

`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.

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    MeasureSurvGrafSE$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### 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.

### See Also

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`

[Package mlr3proba version 0.3.2 Index]