CMA-package {CMA} | R Documentation

## Synthesis of microarray-based classification

### Description

The aim of the package is to provide a user-friendly environment for the evaluation of classification methods using gene expression data. A strong focus is on combined variable selection, hyperparameter tuning, evaluation, visualization and comparison of (up to now) 21 classification methods from three main fields: Discriminant Analysis, Neural Networks and Machine Learning. Although the package has been created with the intention to be used for Microarray data, it can as well be used in various `(p > n)`-scenarios.

### Details

Package: | CMA
Type: | Package
Version: | 1.3.3
Date: | 2009-9-14
License: | GPL (version 2 or later)

Most Important Steps for the workflow are:

1.

  Generate evaluation datasets using `GenerateLearningsets`

2.

  (Optionally): Perform variable selection using `GeneSelection`

3.

  (Optionally): Peform hyperparameter tuning using `tune`

4.

  Perform `classification` using 1.-3.

5.

  Repeat 2.-4. based on 1. for several methods: `compBoostCMA`, `dldaCMA`, `ElasticNetCMA`, `fdaCMA`, `flexdaCMA`, `gbmCMA`, `knnCMA`, `ldaCMA`, `LassoCMA`, `nnetCMA`, `pknnCMA`, `plrCMA`, `pls_ldaCMA`, `pls_lrCMA`, `pls_rfCMA`, `pnnCMA`, `qdaCMA`, `rfCMA`, `scdaCMA`, `shrinkldaCMA`, `svmCMA`

6.

  Evaluate the results from 5. using `evaluation` and make a comparison by calling `compare`

### Author(s)

Martin Slawski ms@cs.uni-sb.de

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

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

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

### References

Slawski, M. Daumer, M. Boulesteix, A.-L. (2008) CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9: 439

[Package CMA version 1.38.0 Index]