weighted.mcr {CMA} | R Documentation

## Tuning / Selection bias correction

### Description

Performs subsampling for several classifiers or a single classifiers with different tuning parameter values or numbers of selected genes. Eventually, a specific procedure for correcting for the tuning or selection bias, which is caused by optimal selection of classifiers or tuning parameters, is applied.

### Usage

    weighted.mcr(classifiers,parameters,nbgenes,sel.method,X,y,portion,niter=100,shrinkage=F)

### Arguments

`classifiers` | A character vector of the several CMA classifiers that shall be used. If the same classifier shall be used with different tuning parameters it must appear several times in this vector.
`parameters` | A character containing the tuning parameter values corresponding to the classification methods in `classifiers`. Must have the same length as `classifiers`.
`nbgenes` | A numeric vector indicating how many variables shall be selected by `sel.method` for the corresponding classifier. Must have the same length as `classifiers`.
`sel.method` | The CMA-method (represented as a string) that shall be applied for variable selection. If this parameter is set to `'none'` no variable selection is performed.
`X` | The matrix of gene expression data. Can be one of the following. Rows correspond to observations, columns to variables.
`y` | Class labels. Can be one of the following: A `numeric` vector. A `factor`. WARNING: The class labels will be re-coded to range from `0` to `K-1`, where `K` is the total number of different classes in the learning set.
`portion` | A numeric value which indicates the portion of observations that will be used for training the classifiers.
`niter` | The number of subsampling iterations.
`shrinkage` | A logical value indicating whether shrinkage (WMCS) shall be applied.

### Details

The algorithm tries to avoid the additional computational costs of a nested cross validation by estimating the corrected misclassification rate of the best classifier by a weighted mean of all classifiers included in the subsampling approach.

### Value

An object of class `wmcr.result` which provides the corrected and uncorrected misclassification rate of the best classifier as well as weights and misclassifcation rates for all classifiers used in the subsampling approach.

### Author(s)

Christoph Bernau bernau@ibe.med.uni-muenchen.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

### References

Bernau Ch., Augustin, Th. and Boulesteix, A.-L. (2011): Correcting the optimally selected resampling-based error rate: A smooth analytical alternative to nested cross-validation. Department of Statistics: Technical Reports, Nr. 105.

### See Also

`wmc`,`classification`,`GeneSelection`, `tune`, `evaluation`,

### Examples

    #inputs
    classifiers<-rep('knnCMA',7)
    nbgenes<-rep(50,7)
    parameters<-c('k=1','k=3','k=5','k=7','k=9','k=11','k=13')
    portion<-0.8
    niter<-100
    data(golub)
    X<-as.matrix(golub[,-1])
    y<-golub[,1]
    sel.method<-'t.test'
    #function call
    wmcr<-weighted.mcr(classifiers=classifiers,parameters=parameters,nbgenes=nbgenes,sel.method=sel.method,X=X,y=y,portion=portion,niter=niter)

[Package CMA version 1.38.0 Index]