bsSCAD: Perform SCAD SVM bootstrapping.

In bootfs: Derive Robust Feature Sets for Two or Multiclass Classification Problems

Description

Use SCAD SVM algorithm and bootstrapping to derive a robust feature set.

Usage

bsSCAD(logX, groupings, DIR=NULL, params = NULL)

Arguments

logX	The data matrix.
groupings	The grouping variable.
DIR	The output base directory.
params	A parameter list with the following elements: seed: A random seed. bstr: Number of bootstrap iterations. jitter: Boolean. Use some additional noise for constant values. maxiter: Parameter for the SCAD SVM function. maxevals: Parameter for the SCAD SVM function. bounds: Parameter for the SCAD SVM function. saveres: Boolean. Stored results of bootstrapping in an R-data object. fs.method: The method for feature selection. One out of scad, 1norm, scad+L2, DrHSVM. See package penalizedSVM for the methods.

Details

Usually called from doBS.

Value

List holding the bootstrap results for each grouping variable in groupings.
This is a list with one element for each of the bstr bootstrap iterations. Each element is again a list, which is retrieved by function bstr_multi.

Author(s)

Christian Bender ([e-mail])

See Also

doBS

Examples

## Not run: 
#todo

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bootfs documentation built on May 2, 2019, 5:50 p.m.