\name{genediff}
\alias{genediff}
\title{ Raw p-value calculation function
 }
\description{
  Computes two vectors of p-values per gene or probe
  using gene-by-gene ANOVA with individual gene MSE using 
  both the gene-specific MSE and the posterior mean MSE for
  each term in the ANOVA.
  \cr
  Assumes a fixed effects model and the correct denominator for all comparisons is the MSE.
}
\usage{
genediff(eS, model=NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{eS}{Array data. must be an \code{ExpressionSet} object and the log-transformation and
    the normalization of \code{exprs(eS)} are recommended.}
  \item{model}{Model used for comparison; see details and \code{\link{LMGene}}.}
}
\details{
  The argument \code{eS} must be an \code{ExpressionSet} object from the Biobase package. 
  If you have a data in a \code{matrix} and information about the considered factors, then you
  can use \code{\link{neweS}} to convert the data into an \code{ExpressionSet} object. Please see
  \code{\link{neweS}} in more detail.

  The \code{model} argument is an optional character string, constructed like the right-hand
  side of a formula for lm. It specifies which of the variables in the \code{ExpressionSet} will
  be used in the model and whether interaction terms will be included. If \code{model=NULL},
  it uses all variables from the \code{ExpressionSet} without interactions. Be careful of using
  interaction terms with factors; this often leads to overfitting, which will yield an error.
}
\value{
  \item{pvlist}{a list containing two sets of p-values obtained by gene specific MSE and the posterior MSE methods.}
}
\references{ 
David M. Rocke (2004), Design and analysis of experiments with high throughput biological assay data, Seminars in Cell & Developmental Biology, 15, 703-713. 

\url{http://www.idav.ucdavis.edu/~dmrocke/} 
}
\author{David Rocke and Geun-Cheol Lee}
\seealso{\code{\link{LMGene}}, \code{\link{rowaov}}}
\examples{
#library
library(Biobase)
library(LMGene)

#data
data(sample.mat)
data(vlist)
LoggedSmpd0 <- neweS(lnorm(log(sample.mat)),vlist)

pvlist <- genediff(LoggedSmpd0)
pvlist$Posterior[1:5,]
}
\keyword{models}