classifyLDA            package:maigesPack            R Documentation

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

     Function to search by groups of few genes, also called cliques,
     that can discriminate (or classify) between two distinct
     biological sample types, using the Fisher's linear discriminat
     analysis. This function uses exhaustive search.

_U_s_a_g_e:

     classifyLDA(obj=NULL, sLabelID="Classification", facToClass=NULL,
                 gNameID="GeneName", geneGrp=1, path=NULL, nGenes=3,
                 sortBy="cv")

_A_r_g_u_m_e_n_t_s:

     obj: object of class 'maiges' to search the classifiers.

sLabelID: character string with the identification of the sample label
          to be used.

facToClass: named list with 2 character vectors specifying the samples
          to be compared. If NULL (default) the first 2 types of
          sLabelID are used.

 gNameID: character string with the identification of gene label ID.

 geneGrp: character or integer specifying the gene group to be tested
          ('colnames' of 'GeneGrps' slot). If both 'geneGrp' and 'path'
          are NULL all genes are used. Defaults to 1 (first group).

    path: character or integer specifying the gene network to be tested
          ('names' of 'Paths' slot). If both 'geneGrp' and 'path' are
          NULL all genes are used. Defaults to NULL.

  nGenes: integer specifying the number of genes in the clique, or
          classifier.

  sortBy: character string with field to sort the result. May be 'cv'
          (default) or 'svd' for cross validation by leave-one-out or
          the singular value decomposition, respectively.

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

     Pay attention with the arguments 'geneGrp' and 'path', if both of
     them is NULL an exhaustive search for all dataset will be done,
     and this search may be extremely computational intensive, which
     may result in a process running during some weeks or months
     depending on the number of genes in your dataset.

     If you want to construct classifiers from a group of several
     genes, the _search and choose_ (SC) method may be an interesting
     option. It is implemented in the function 'classifyLDAsc'. This
     function uses the function 'lda' from package _MASS_ to search by
     classifiers using Fisher's linear discriminant analysis. The
     functions 'classifySVM' and 'classifyKNN' were also dedined to
     construct classifiers by support vector machines ans k-neighbours,
     respectively.

_V_a_l_u_e:

     The result of this function is an object of class 'maigesClass'.

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

     Elier B. Cristo, adapted by Gustavo H. Esteves
     <gesteves@vision.ime.usp.br>

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

     'lda', 'classifySVM', 'classifyKNN', 'classifyLDAsc'.

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

     ## Loading the dataset
     data(gastro)

     ## Doing LDA classifier with 2 genes for the 6th gene group comparing
     ## the 2 categories from 'Type' sample label.
     gastro.class = classifyLDA(gastro.summ, sLabelID="Type",
       gNameID="GeneName", nGenes=2, geneGrp=6)
     gastro.class

     ## To do classifier with 3 genes for the 6th gene group comparing
     ## normal vs adenocarcinomas from 'Tissue' sample label
     gastro.class = classifyLDA(gastro.summ, sLabelID="Tissue",
       gNameID="GeneName", nGenes=3, geneGrp=6,
       facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))

