cvcb.control: Control parameters for cross-validation in `iCoxBoost`

Description

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

Usage

cvcb.control(
  K = 10,
  type = c("verweij", "naive"),
  parallel = FALSE,
  upload.x = TRUE,
  multicore = FALSE,
  folds = NULL
)

Value

List with elements corresponding to the call arguments.

Arguments

K: 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.
type: 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).
parallel: 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.
upload.x: 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.
multicore: 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.
folds: 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.

Author

Written by Harald Binder binderh@uni-mainz.de.

References