| mlr_measures_surv.schmid {mlr3proba} | R Documentation |
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 ≤ t*, δ = 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*) = 1/N ∑_i^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) = 1/(NT) ∑_i^N ∑_j^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, Inf)
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.
Schemper, Michael, Henderson, Robin (2000). “Predictive Accuracy and Explained Variation in Cox Regression.” Biometrics, 56, 249–255. doi: 10.1002/sim.1486.
Schmid, Matthias, Hielscher, Thomas, Augustin, Thomas, Gefeller, Olaf (2011). “A Robust Alternative to the Schemper-Henderson Estimator of Prediction Error.” Biometrics, 67(2), 524–535. doi: 10.1111/j.1541-0420.2010.01459.x.
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_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.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_se,
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.logloss_se,
mlr_measures_surv.logloss