Calculates the Integrated Schmid Score, aka integrated absolute loss.
For an individual who dies at time \(t\), with predicted Survival function, \(S\), the Schmid Score at time \(t^*\) is given by $$L(S,t|t^*) = [(S(t^*))I(t \le t^*, \delta = 1)(1/G(t))] + [((1 - S(t^*)))I(t > t^*)(1/G(t^*))]$$ # nolint 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():
MeasureSurvSchmid$new()
mlr_measures$get("surv.schmid")
msr("surv.schmid")
Type: "surv"
Range: \([0, \infty)\)
Minimize: TRUE
Required prediction: distr
mlr3::Measure -> mlr3proba::MeasureSurv -> mlr3proba::MeasureSurvIntegrated -> MeasureSurvSchmid
se(logical(1))
If TRUE returns the standard error of the measure.
new()Creates a new instance of this R6 class.
MeasureSurvSchmid$new(integrated = TRUE, times, method = 2, se = FALSE)
integrated(logical(1))
If TRUE (default), returns the integrated score; otherwise, not integrated.
times(numeric())
If integrate == TRUE then a vector of time-points over which to integrate the score.
If integrate == FALSE then a single time point at which to return the score.
method(integer(1))
If integrate == TRUE selects the integration weighting method.
method == 1 corresponds to weighting each time-point equally and taking the mean score over
discrete time-points. method == 2 corresponds to calculating a mean weighted by the difference
between time-points. method == 2 is default to be in line with other packages.
se(logical(1))
If TRUE returns the standard error of the measure.
clone()The objects of this class are cloneable with this method.
MeasureSurvSchmid$clone(deep = FALSE)
deepWhether to make a deep clone.
mlr3probaschemper_2000 mlr3probaschmid_2011
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.loglossSE,
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.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.loglossSE,
mlr_measures_surv.logloss
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.loglossSE,
mlr_measures_surv.logloss