Calculates the integrated logarithmic (log), loss, aka integrated cross entropy.

For an individual who dies at time \(t\), with predicted Survival function, \(S\), the probabilistic log loss at time \(t^*\) is given by $$L(S,t|t^*) = - [log(1 - S(t^*))I(t \le t^*, \delta = 1)(1/G(t))] - [log(S(t^*))I(t > t^*)(1/G(t^*))]$$ where \(G\) is the Kaplan-Meier estimate of the censoring distribution.

If `integrated == FALSE`

then the sample mean is taken for the single specified `times`

, \(t^*\), and the returned
score is given by
$$L(S,t|t^*) = \frac{1}{N} \sum_{i=1}^N L(S_i,t_i|t^*)$$
where \(N\) is the number of observations, \(S_i\) is the predicted survival function for
individual \(i\) and \(t_i\) is their true survival time.

If `integrated == TRUE`

then an approximation to integration is made by either taking the sample
mean over all \(T\) unique time-points (`method == 1`

), or by taking a mean weighted by the difference
between time-points (`method == 2`

). Then the sample mean is taken over all \(N\) observations.
$$L(S) = \frac{1}{NT} \sum_{i=1}^N \sum_{j=1}^T L(S_i,t_i|t^*_j)$$

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

MeasureSurvIntLogloss$new() mlr_measures$get("surv.intlogloss") msr("surv.intlogloss")

Type:

`"surv"`

Range: \([0, \infty)\)

Minimize:

`TRUE`

Required prediction:

`distr`

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
.

Other survival measures:
`MeasureSurvBeggC`

,
`MeasureSurvChamblessAUC`

,
`MeasureSurvGonenC`

,
`MeasureSurvGrafSE`

,
`MeasureSurvGraf`

,
`MeasureSurvHarrellC`

,
`MeasureSurvHungAUC`

,
`MeasureSurvIntLoglossSE`

,
`MeasureSurvLoglossSE`

,
`MeasureSurvLogloss`

,
`MeasureSurvMAESE`

,
`MeasureSurvMAE`

,
`MeasureSurvMSESE`

,
`MeasureSurvMSE`

,
`MeasureSurvNagelkR2`

,
`MeasureSurvOQuigleyR2`

,
`MeasureSurvRMSESE`

,
`MeasureSurvRMSE`

,
`MeasureSurvSongAUC`

,
`MeasureSurvSongTNR`

,
`MeasureSurvSongTPR`

,
`MeasureSurvUnoAUC`

,
`MeasureSurvUnoC`

,
`MeasureSurvUnoTNR`

,
`MeasureSurvUnoTPR`

,
`MeasureSurvXuR2`

Other Probabilistic survival measures:
`MeasureSurvGrafSE`

,
`MeasureSurvGraf`

,
`MeasureSurvIntLoglossSE`

,
`MeasureSurvLoglossSE`

,
`MeasureSurvLogloss`

Other distr survival measures:
`MeasureSurvGrafSE`

,
`MeasureSurvGraf`

,
`MeasureSurvIntLoglossSE`

,
`MeasureSurvLoglossSE`

,
`MeasureSurvLogloss`

`mlr3::Measure`

-> `mlr3proba::MeasureSurv`

-> `mlr3proba::MeasureSurvIntegrated`

-> `MeasureSurvIntLogloss`

`eps`

(

`numeric(1)`

)

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

`new()`

Creates a new instance of this R6 class.

MeasureSurvIntLogloss$new(integrated = TRUE, times, eps = 1e-15, method = 2)

`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.`eps`

(

`numeric(1)`

)

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

(

`integer(1)`

)

If`integrate == TRUE`

selects the integration weighting method.`method == 1`

corresponds to weighting each time-point equally and taking the mean score over discrete time-points.`method == 2`

corresponds to calculating a mean weighted by the difference between time-points.`method == 2`

is default to be in line with other packages.

`clone()`

The objects of this class are cloneable with this method.

MeasureSurvIntLogloss$clone(deep = FALSE)

`deep`

Whether to make a deep clone.