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 taking the mean over all
\(T\) unique time-points, and then the sample mean over all \(N\) observations.
$$L(S) = \frac{1}{NT} \sum_{i=1}^N \sum_{j=1}^T L(S_i,t_i|t^*_j)$$

`R6::R6Class()`

inheriting from `MeasureSurvIntegrated`

/MeasureSurv.

MeasureSurvIntLogloss$new(integrated = TRUE, times, eps = 1e-15, method = 2) mlr_measures$get("surv.intlogloss") msr("surv.intlogloss")

`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.`eps`

::`numeric(1)`

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

Type:

`"surv"`

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

Minimize:

`TRUE`

Required prediction:

`distr`

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

As well as

eps :: numeric(1)

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

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`

,
`MeasureSurvNagelkR2`

,
`MeasureSurvOQuigleyR2`

,
`MeasureSurvSongAUC`

,
`MeasureSurvSongTNR`

,
`MeasureSurvSongTPR`

,
`MeasureSurvUnoAUC`

,
`MeasureSurvUnoC`

,
`MeasureSurvUnoTNR`

,
`MeasureSurvUnoTPR`

,
`MeasureSurvXuR2`

Other Probabilistic survival measures:
`MeasureSurvGrafSE`

,
`MeasureSurvGraf`

,
`MeasureSurvIntLoglossSE`

,
`MeasureSurvLoglossSE`

,
`MeasureSurvLogloss`