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: | 0.8.5
Date: | 2008-2-13
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 martin.slawski@campus.lmu.de

Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

Maintainer: Martin Slawski martin.slawski@campus.lmu.de.

[Package CMA version 1.2.0 Index]