\HeaderA{classifySVMsc}{Function to do discrimination analysis, by the search and choose method}{classifySVMsc}
\keyword{methods}{classifySVMsc}
\begin{Description}\relax
Function to search by groups of few genes, also called cliques, that
can discriminate (or classify) between two distinct biological
sample types using the Support Vector Machinnes method. This
function uses the search and choose method.
\end{Description}
\begin{Usage}
\begin{verbatim}
classifySVMsc(obj=NULL, sLabelID="Classification", func="wilcox.test",
              facToClass=NULL, gNameID="GeneName", geneGrp=1, path=NULL,
              nGenes=3, cliques=100)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{obj}] object of class \code{\LinkA{maiges}{maiges}} to search the classifiers.
\item[\code{sLabelID}] character string with the identification of the sample
label to be used.
\item[\code{func}] string specifying the function to be used to search by the
initial one-dimensional classifiers, like 'wilcox.test' or 't.test'.
\item[\code{facToClass}] named list with 2 character vectors specifying the
samples to be compared. If NULL (default) the first 2 types of
sLabelID are used.
\item[\code{gNameID}] character string with the identification of gene label ID.
\item[\code{geneGrp}] character or integer specifying the gene group to be
tested (\code{colnames} of \code{GeneGrps} slot). If both \code{geneGrp} and
\code{path} are NULL all genes are used. Defaults to 1 (first group).
\item[\code{path}] character or integer specifying the gene network to be
tested (\code{names} of \code{Paths} slot). If both \code{geneGrp}
and \code{path} are NULL all genes are used. Defaults to NULL.
\item[\code{nGenes}] integer specifying the number of genes in the clique, or
classifier.
\item[\code{cliques}] integer specifying the number of cliques or classifiers
to be generated.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
This function implements the method known as Search and choose
proposed by Cristo (2003). If you want to use an exhaustive search use
the function \code{\LinkA{classifySVM}{classifySVM}}.

This method uses the function \code{\LinkA{svm}{svm}} from
package \emph{e1071} to search classifiers by Support Vector
Machines. It is possible to search by classifiers using Fisher's
linear discriminant analysis and k nearest neighbours methods using
the functions \code{\LinkA{classifyLDAsc}{classifyLDAsc}} and
\code{\LinkA{classifyKNNsc}{classifyKNNsc}}, respectively.
\end{Details}
\begin{Value}
The result of this function is an object of class \code{\LinkA{maigesClass}{maigesClass}}.
\end{Value}
\begin{Author}\relax
Elier B. Cristo, adapted by Gustavo H. Esteves <\email{gesteves@vision.ime.usp.br}>
\end{Author}
\begin{References}\relax
Cristo, E.B. Metodos Estatisticos na Analise de Experimentos de
Microarray. Masther's thesis, Instituto de Matematica
e Estatistica - Universidade de Sao Paulo, 2003 (in portuguese).
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{svm}{svm}}, \code{\LinkA{classifySVM}{classifySVM}},
\code{\LinkA{classifyLDAsc}{classifyLDAsc}} and \code{\LinkA{classifyKNNsc}{classifyKNNsc}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## Loading the dataset
data(gastro)

## Doing SVM classifier with 2 genes for the 6th gene group comparing
## the 2 categories from 'Type' sample label.
gastro.class = classifySVMsc(gastro.summ, sLabelID="Type",
  gNameID="GeneName", nGenes=2, geneGrp=1, cliques=10)
gastro.class

## To do classifier with 3 genes for the 6th gene group comparing
## normal vs adenocarcinomas from 'Tissue' sample label
gastro.class = classifySVMsc(gastro.summ, sLabelID="Tissue",
  gNameID="GeneName", nGenes=3, geneGrp=1, cliques=10,
  facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))
\end{ExampleCode}
\end{Examples}

