R: Standard Error of Log loss Survival Measure

mlr_measures_surv.logloss_se {mlr3proba} | R Documentation

## Standard Error of Log loss Survival Measure

### Description

Calculates the standard error of MeasureSurvLogloss.

The standard error of the Logloss, 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.

Censored observations in the test set are ignored.

### Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

    MeasureSurvLoglossSE$new()
    mlr_measures$get("surv.logloss_se")
    msr("surv.logloss_se")

### Meta Information

- Type: `"surv"`

- Range: [0, Inf)

- Minimize: `TRUE`

- Required prediction: `distr`

### Super classes

`mlr3::Measure` -> `mlr3proba::MeasureSurv` -> `mlr3proba::MeasureSurvLogloss` -> `MeasureSurvLoglossSE`

### Methods

#### Public methods

- `MeasureSurvLoglossSE$new()`

- `MeasureSurvLoglossSE$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

    MeasureSurvLoglossSE$new(eps = 1e-15)

##### Arguments

`eps`

  (`numeric(1)`)
  Very small number to set zero-valued predicted probabilities to in order to prevent errors in log(0) calculation.

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    MeasureSurvLoglossSE$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### 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.grafSE`, `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`, `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.grafSE`, `mlr_measures_surv.graf`, `mlr_measures_surv.intloglossSE`, `mlr_measures_surv.intlogloss`, `mlr_measures_surv.logloss`, `mlr_measures_surv.schmid`

Other distr survival measures: `mlr_measures_surv.calib_alpha`, `mlr_measures_surv.grafSE`, `mlr_measures_surv.graf`, `mlr_measures_surv.intloglossSE`, `mlr_measures_surv.intlogloss`, `mlr_measures_surv.logloss`, `mlr_measures_surv.schmid`

[Package mlr3proba version 0.3.2 Index]