cvcb.control: Control paramters for cross-validation in 'iCoxBoost'

Description Usage Arguments Value Author(s) References See Also


This function allows to set the control parameters for cross-validation to be passed into a call to iCoxBoost.





number of folds to be used for cross-validation. If K is larger or equal to the number of events in the data to be analyzed, leave-one-out cross-validation is performed.


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).


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 iCoxBoost.


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.


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.


if not NULL, this has to be a list of length K, each element being a vector of indices of fold elements. Useful for employing the same folds for repeated runs.


List with elements corresponding to the call arguments.


Written by Harald Binder


Verweij, P. J. M. and van Houwelingen, H. C. (1993). Cross-validation in survival analysis. Statistics in Medicine, 12(24):2305-2314.

See Also

iCoxBoost, cv.CoxBoost

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