CMA: Synthesis of microarray-based classification

Description Usage Arguments Details Value Author(s) References See Also

Perform tuning / selection bias correction for a matrix of subsampling fold errors.

1	wmc(mcr.m,n.tr,n.ts,shrinkage=F)

`mcr.m`	A matrix of resampling fold errors. Columns correspond the the fold errors of a single classifier.
`n.tr`	Number of observations in the resampling training sets.
`n.ts`	Number of observations in the resampling test sets.
`shrinkage`	A logical value indicating whether shrinkage (WMCS) shall be applied.

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.

A list containing the corrected misclassification rate, the index of the best method and a logical value indicating whether shrinkage has been applied.

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

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

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.

weighted.mcr,classification,GeneSelection, tune, evaluation,