\title{Cluster samples from an IcaSet}
  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", ...)
  \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

  \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

  \item{level}{The level of projections to be used when
  \code{clusterOn="S"}, either \code{"features"} or

  \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}.}
  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.}}
  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.
  One clustering is run independently for each component.
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")
  \code{Mclust}, \code{adjustedRandIndex}, \code{kmeans},
  \code{pam}, \code{pamk}, \code{hclust}, \code{agnes},