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
.
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.
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.
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.