\name{clusterSamplesByComp_multiple}
 \alias{clusterSamplesByComp_multiple}
 \title{Cluster samples from an IcaSet}
 \usage{
   clusterSamplesByComp_multiple(icaSet, params,
     funClus = c("Mclust", "kmeans", "pam", "pamk", "hclust", "agnes"),
     filename, clusterOn = c("A", "S"),
     level = c("genes", "features"), nbClus,
     metric = "euclidean", method = "ward", ...)
 }
 \arguments{
   \item{icaSet}{An \code{IcaSet} object}
 
   \item{params}{A \code{MineICAParams} object}
 
   \item{funClus}{The function to be used for clustering,
   must be several of
   \code{c("Mclust","kmeans","pam","pamk","hclust","agnes")}}
 
   \item{filename}{A file name to write the results of the
   clustering in}
 
   \item{clusterOn}{Specifies the matrix used to apply
   clustering, can be several of: \describe{
   \item{\code{"A"}:}{the clustering is performed in one
   dimension, on the vector of sample contributions,}
   \item{\code{"S"}:}{the clustering is performed on the
   original data restricted to the contributing
   individuals.}}}
 
   \item{level}{The level of projections to be used when
   \code{clusterOn="S"}, either \code{"features"} or
   \code{"genes"}.}
 
   \item{nbClus}{The number of clusters to be computed,
   either a single number or a numeric vector whose length
   equals the number of components. If missing (only allowed
   if \code{funClus} is one of \code{c("Mclust","pamk")})}
 
   \item{metric}{Metric used in \code{pam} and
   \code{hclust}, default is \code{"euclidean"}}
 
   \item{method}{Method of hierarchical clustering, used in
   \code{hclust} and \code{agnes}}
 
   \item{...}{Additional parameters required by the
   clustering function \code{funClus}.}
 }
 \value{
   A list consisting of three elements
   \describe{\item{clus:}{a data.frame specifying the sample
   clustering for each component using the different ways of
   clustering,}\item{resClus:}{the complete output of the
   clustering function(s),}\item{comparClus:}{the adjusted
   Rand indices, used to compare the clusterings obtained
   for a same component.}}
 }
 \description{
   This function allows to cluster samples according to the
   results of an ICA decomposition. Several clustering
   functions and several levels of data for clustering can
   be performed by the function.
 }
 \details{
   One clustering is run independently for each component.
 }
 \examples{
 data(icaSetCarbayo)
 params <- buildMineICAParams(resPath="carbayo/", selCutoff=3)
 
 ## compare kmeans clustering applied to A and data restricted to the contributing genes
 ## on components 1 to 3
 res <- clusterSamplesByComp_multiple(icaSet=icaSetCarbayo[,,1:3], params=params, funClus="kmeans",
                                      nbClus=2, clusterOn=c("A","S"), level="features")
 head(res$clus)
 }
 \author{
   Anne
 }
 \seealso{
   \code{Mclust}, \code{adjustedRandIndex}, \code{kmeans},
   \code{pam}, \code{pamk}, \code{hclust}, \code{agnes},
   \code{cutree}
 }