mlr_measures_surv.calib_alpha | R Documentation |
This calibration method is defined by estimating
α = ∑ δ_i / ∑ H_i(t_i)
where δ 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/√{∑ δ_i})
The model is well calibrated if the estimated α 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: (-Inf, Inf)
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)
deep
Whether 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. doi: 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
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