Argument of: CoxBoost::cv.CoxBoost

maximum number of boosting steps to evaluate, i.e, the returned “optimal” number of boosting steps will be in the range [0,maxstepno].

Argument of: CoxBoost::cv.CoxBoost

number of folds to be used for cross-validation. If K is larger or equal to the number of non-zero elements in status, leave-one-out cross-validation is performed.

Argument of: CoxBoost::cv.CoxBoost

way of calculating the partial likelihood contribution of the observation in the hold-out folds: "verweij" uses the more appropriate method described in Verweij and van Houwelingen (1996), "naive" uses the approach where the observations that are not in the hold-out folds are ignored (often found in other R packages).

Argument of: CoxBoost::cv.CoxBoost

logical value indicating whether computations in the cross-validation folds should be performed in parallel on a compute cluster, using package snowfall. Parallelization is performed via the package snowfall and the initialization function of of this package, sfInit, should be called before calling cv.CoxBoost.

Argument of: CoxBoost::cv.CoxBoost

indicates whether computations in the cross-validation folds should be performed in parallel, using package parallel. If TRUE, package parallel is employed using the default number of cores. A value larger than 1 is taken to be the number of cores that should be employed.

Argument of: CoxBoost::cv.CoxBoost

logical value indicating whether x should/has to be uploaded to the compute cluster for parallel computation. Uploading this only once (using sfExport(x) from library snowfall) can save much time for large data sets.

Fraction of all pairs of subjects whose predicted survival times are correctly ordered among all subjects that can actually be ordered. In other words, it is the probability of concordance between the predicted and the observed survival. Corrected by weighting with IPCW as suggested by Uno. Implemented in survAUC::UnoC.

Integrated brier score using Kaplan-Meier estimator for weighting