CMA-package               package:CMA               R Documentation

_S_y_n_t_h_e_s_i_s _o_f _m_i_c_r_o_a_r_r_a_y-_b_a_s_e_d _c_l_a_s_s_i_f_i_c_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_D_e_t_a_i_l_s:


       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'

_A_u_t_h_o_r(_s):

     Martin Slawski martin.slawski@campus.lmu.de

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

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

