\name{normalizeAffyBatchLoessIterPara}
\alias{normalizeAffyBatchLoessIterPara}
\title{Parallelized partial loess normalization with permutation}
\description{
Parallelized partial cyclic loess normalization of arrays with permutation.
}
\usage{
normalizeAffyBatchLoessIterPara(cluster,
		object,
		percentPerm = 0.75,
		phenoData = new("AnnotatedDataFrame"), cdfname = NULL,
		type=c("separate","pmonly","mmonly","together"), 
		subset = NULL,
		epsilon = 10^-2, maxit = 1, log.it = TRUE, 
		span = 2/3, family.loess ="symmetric",
		verbose=FALSE) 
}

\arguments{
  \item{cluster}{ A cluster object obtained from the function \link[snow:snow-startstop]{makeCluster} in the \code{SNOW} package. }
  \item{object}{ An object of class \link[affy:AffyBatch-class]{AffyBatch} OR a \code{character} vector with the names of CEL files OR a (partitioned) list of \code{character} vectors with CEL file names.}
 
  \item{percentPerm}{Percent of permutations to do.}
 
  \item{phenoData}{ An \link[Biobase]{AnnotatedDataFrame} object. }
  \item{cdfname}{ Used to specify the name of an alternative cdf package. If set to \code{NULL}, the usual cdf package based on Affymetrix' mappings will be used. }
  \item{type}{A string specifying how the normalization should be applied.}
  \item{subset}{a subset of the data to fit a loess to.}
  \item{epsilon}{a tolerance value (supposed to be a small value - used as a stopping criterium).}
  \item{maxit}{maximum number of iterations.}
  \item{log.it}{logical. If \code{TRUE} it takes the log2 of mat}
  \item{span}{parameter to be passed the function loess}
  \item{family.loess}{parameter to be passed the function loess. "gaussian" or "symmetric" are acceptable values for this parameter.}
  \item{verbose}{ A logical value. If \code{TRUE} it writes out some messages. } 
}
\details{
Parallelized partial cyclic loess normalization of arrays with permutation. This is a new kind of normalization based on cyclic loess normalization. 

In the partial cyclic loess normalization the loess normalization will be done only at the slaves with the arrays at the slaves. Therefore we only have to do loess normalization for some pairs and have a big saving of time.
But this is no enough for good normalization. We have to do some interations of array permutation between the slaves and again loess normalization at the slaves. If we did about 75 percent of the complete cyclic loess normalization we can achieve same results and save computation time.

For the similar serial function and more details to loess normalization see the function \code{normalize.AffyBatch.loess}.

For using this function a computer cluster using the \code{snow} package has to be started.
In the loess normalization the arrays will compared by pairs. Therefore at every node minimum two arrays have to be!
}

\value{
An \link[affy:AffyBatch-class]{AffyBatch} of normalized objects.
}

\author{ Markus Schmidberger \email{schmidb@ibe.med.uni-muenchen.de}, Ulrich Mansmann \email{mansmann@ibe.med.uni-muenchen.de} }

\examples{
\dontrun{
library(affyPara)
if (require(affydata)) {
  data(Dilution)

  c1 <- makeCluster(3)

  AffyBatch <- normalizeAffyBatchLoessIterPara(c1, percentPerm=0.75, Dilution, verbose=TRUE)

  stopCluster(c1)
}
}
}

\keyword{programming}
\keyword{manip}