\name{normalize.loess}
\alias{normalize.loess}
\title{Normalized chips using cyclic loess.}
\description{
	Takes a matrix and apply cyclic loess normalization. It is based in
	normalize.loess from package affy but supports NA.
}
\usage{
  normalize.loess(mat, subset = sample(1:(dim(mat)[1]), 
    min(c(5000,	nrow(mat)))), epsilon = 10^-2, maxit = 1, log.it = TRUE,
	verbose = FALSE, span = 2/3, family.loess = "symmetric", weights = NULL)
}
\arguments{
  \item{mat}{a matrix with columns containing the values of the chips
    to normalize.}
  \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 \code{mat}}
  \item{verbose}{logical. If \code{TRUE} displays current pair of chip being
    worked on.}
  \item{span}{parameter to be passed the function \code{\link[stats]{loess}}}
  \item{family.loess}{parameter to be passed the function
    \code{\link[stats]{loess}}. \code{"gaussian"} or \code{"symmetric"}
    are acceptable values for this parameter.}
  \item{weights}{a vector of weights for the individual measurements.}
}
\value{A matrix of normalized values.}
\author{Diego Diez}
\examples{
\dontrun{
	mat <- matrix(sample(500), 100, 5)
	mat <- normalize.loess(mat)
}
}
\keyword{documentation}
\keyword{utilities}