This calibration method is defined by estimating $$\alpha = \sum \delta_i / \sum H_i(t_i)$$ where \(\delta\) is the observed censoring indicator from the test data, \(H_i\) is the predicted cumulative hazard, and \(t_i\) is the observed survival time.
The standard error is given by $$exp(1/\sqrt{\sum \delta_i})$$
The model is well calibrated if the estimated \(\alpha\) coefficient is equal to 1.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
MeasureSurvCalibrationAlpha$new()
mlr_measures$get("surv.calib_alpha")
msr("surv.calib_alpha")
Type: "surv"
Range: \((-\infty, \infty)\)
Minimize: FALSE
Required prediction: distr
mlr3::Measure -> mlr3proba::MeasureSurv -> MeasureSurvCalibrationAlpha
new()Creates a new instance of this R6 class.
MeasureSurvCalibrationAlpha$new()
clone()The objects of this class are cloneable with this method.
MeasureSurvCalibrationAlpha$clone(deep = FALSE)
deepWhether to make a deep clone.
Van Houwelingen, C. H (2000). “Validation, calibration, revision and combination of prognostic survival models.” Statistics in Medicine, 19(24), 3401--3415. 10.1002/1097-0258(20001230)19:24<3401::AID-SIM554>3.0.CO;2-2.
Other survival measures:
mlr_measures_surv.calib_beta,
mlr_measures_surv.chambless_auc,
mlr_measures_surv.cindex,
mlr_measures_surv.dcalib,
mlr_measures_surv.graf,
mlr_measures_surv.hung_auc,
mlr_measures_surv.intlogloss,
mlr_measures_surv.logloss,
mlr_measures_surv.mae,
mlr_measures_surv.mse,
mlr_measures_surv.nagelk_r2,
mlr_measures_surv.oquigley_r2,
mlr_measures_surv.rcll,
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.uno_auc,
mlr_measures_surv.uno_tnr,
mlr_measures_surv.uno_tpr,
mlr_measures_surv.xu_r2
Other calibration survival measures:
mlr_measures_surv.calib_beta,
mlr_measures_surv.dcalib
Other distr survival measures:
mlr_measures_surv.dcalib,
mlr_measures_surv.graf,
mlr_measures_surv.intlogloss,
mlr_measures_surv.logloss,
mlr_measures_surv.rcll,
mlr_measures_surv.schmid