\name{expFarms}
          \alias{expFarms}
          \title{Factor Analysis for Robust Microarray Summarization}
          \description{ This function converts an instance of \code{\link[affy:AffyBatch-class]{AffyBatch}}
	into an instance of  \code{\link[Biobase]{exprSet-class}}  using
	a factor analysis model for which a Bayesian Maximum
	a Posteriori method optimizes the model parameters under the assumption
	of Gaussian measurement noise.}
\usage{
          expFarms(object, bgcorrect.method = "none", pmcorrect.method = "pmonly", 
        normalize.method = "quantiles",  weight, mu,  weighted.mean, laplacian, robust, correction, centering, ...)
          }
\arguments{
    \item{object}{An instance of \code{\link[affy:AffyBatch-class]{AffyBatch}}.}
	\item{weight}{Hyperparameter value in the range of [0,1]  which determines the influence of the prior. The default value is 0.5 } 
	\item{bgcorrect.method}{the name of the background adjustment method}
	\item{pmcorrect.method}{the name of the PM adjustment method} 
	\item{normalize.method}{the normalization method to use}
	\item{mu}{Hyper-parameter value which allows to quantify different aspects of 
	potential prior knowledge. Values near zero assumes that most genes do not
	contain a signal, and introduces a bias for loading matrix elements near zero. Default value is 0}
 	\item{weighted.mean}{Boolean flag, that indicates wether a weighted mean or a least square fit is used to summarize the loading matrix. The default value is set to TRUE .}
 	 \item{laplacian}{Boolean flag, indicates whether a Laplacian prior for the factor is employed or not. Default value is FALSE.}
 	\item{robust}{Boolean flag, that ensures non-constant results. Default value is TRUE.}
 	\item{correction}{Value that indicates whether the covariance matrix should be corrected for negative eigenvalues 
	which might emerge from the non-negative correlation constraints or not. Default = O  (means that no correction is done), 
	1 (minimal noise (0.0001) is added to the diagonal elements of the covariance matrix to force positive definiteness), 
	2 (Maximum Likelihood solution to compute the nearest positive definite matrix under the given non-negative correlation constraints of the covariance matrix)}
	\item{centering}{Indicates whether the data is "median" or "mean"  centered. Default value is "median".}
	\item{...}{other arguments to be passed to \code{\link[affy]{expresso}}.}
          }\details{
  	This function is a wrapper for  \code{\link[affy]{expresso}}.}
\value{ \code{\link[Biobase]{exprSet-class}} }
\seealso{ \code{\link[affy]{expresso}}, \code{\link{qFarms}}, \code{\link{lFarms}}.}
\examples{
data(testAffyBatch)
eset <- expFarms(testAffyBatch, bgcorrect.method = "none", pmcorrect.method = "pmonly", normalize.method = "constant", weight=0.5, weighted.mean=TRUE)
}

\keyword{manip}