gcl-package               package:gcl               R Documentation

_G_C_L: _A _p_a_c_k_a_g_e _f_o_r _C_o_m_p_u_t_i_n_g _f_u_z_z_y _r_u_l_e_s _o_r _t_r_e_e _c_l_a_s_s_i_f_i_e_r_s _f_r_o_m _n_u_m_e_r_i_c _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     'gcl' is a R (<URL: http://www.r-project.org>) package for
     computing fuzzy rules and tree classifiers given numeric input
     data.

_D_e_t_a_i_l_s:


       Package:  gcl
       Type:     Package
       Version:  1.06.5
       License:  GPL Version 2
       URL:      <URL: http://www.r-project.org>,
                 <URL: http://www.mit.edu/~sav/fuzzy/latest/>

     Function index:


     acc.eval                Function that evaluates the accuracy of a
                             classifier function on a data frame
     ci.eval                 Function that evaluates the cindex of a
                             classifier function on a data frame
     cindex                  Compute the c-index
     cv                      N-fold crossvalidation
     cv52                    5x2 fold crossvalidation
     cvcomp                  Compare two models using crossvalidation
     gain                    Computes information theoretic gain
     gainr                   Computes information theoretic gain ratio
     gcl                     GCL: a fuzzy rule classifier generator
     hanley                  Statistically compare C-indices
     loocv                   Leave one out crossvalidation

     *gcl*

     This function computes a fuzzy rules classifier given numeric
     input data as the data frame or matrix mydata. 

     The algorithm for doing so is described in Vinterbo et al., 2005. 

     When applied, 'gcl' returns another R function that implements the
     found classifier. This computed classifier function takes one
     argument, a vector, matrix or data frame to be classified, and
     outputs a vector of class memberships for each input vector,
     matrix or data frame row. (See examples section below). 

     Even though the paper cited above is on classification using gene
     expression data, numerical data in general can be used. For
     instance


     > library(gcl)
     > library(datasets)
     > data(iris)
     > classifier <- gcl(iris, nlev=5)
     > acc.eval(classifier, iris)

     computes a fuzzy rule classifier for Edgar Anderson's Iris Data
     set and evaluates the classifier accuracy on the same data set.

_A_v_a_i_l_a_b_i_l_i_t_y:

     The gcl program should be considered as being testing software. It
     is supplied as is, and NO WARRANTY whatsoever is given.

     GCL is free software; you can redistribute it and/or modify it
     under the terms of the GNU General Public License <URL:
     http://www.gnu.org/copyleft/gpl.html> as published by the Free
     Software Foundation; either version 2 of the License, or (at your
     option) any later version.

     GCL is distributed in the hope that it will be useful, but WITHOUT
     ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
     or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public
     License for more details.

     You should have received a copy of the GNU General Public License
     along with GCL; if not, write to the Free Software Foundation,
     Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA

     The gzipped tar archive containing the latest gcl R package
     version can also be gotten here: <URL:
     http://www.mit.edu/~sav/fuzzy/latest/>

_A_u_t_h_o_r(_s):

     Staal A. Vinterbo

     Maintainer: complain to <staal at dsg fullstop harvard fullstop
     edu>

_R_e_f_e_r_e_n_c_e_s:

     Vinterbo, S.A.; Kim, E. and Ohno-Machado, L. _Small, fuzzy and
     interpretable gene expression based classifiers_. Bioinformatics,
     2005, 21, 1964-1970. <URL:
     http://bioinformatics.oxfordjournals.org/cgi/content/abstract/21/9/1964>

_S_e_e _A_l_s_o:

     'gcl'

_E_x_a_m_p_l_e_s:

     ## run the demo
     demo(gcldemo)

     ## play with the iris data set:
     ## Not run: 
     library(gcl)
     library(datasets)
     data(iris)
     classifier <- gcl(iris, nlev=5)
     acc.eval(classifier, iris)
     ## End(Not run)

     ## compare performance of gcl and tcl
     ## Not run: 
     library(gcl)
     library(datasets)
     data(iris)
     cv52(iris, gcl, tcl, acc.eval, nlev=5, t.nlev=5)
     ## End(Not run)

