R: Control paramters for cross-validation in 'iCoxBoost'

cvcb.control {CoxBoost} | R Documentation

## Control paramters 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)

### 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.

### Value

List with elements corresponding to the call arguments.

### Author(s)

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

### References

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`

[Package CoxBoost version 1.4 Index]