\HeaderA{normRepLoess}{Bootstrap of LOWESS normalisation}{normRepLoess}
\keyword{methods}{normRepLoess}
\begin{Description}\relax
This function normalises a microarray object re-doing the LOWESS
fitting several times, selecting a pre-specified proportion of points
aleatorily.
\end{Description}
\begin{Usage}
\begin{verbatim}
normRepLoess(raw, span=0.4, propLoess=0.5, nRep=50, func="none",
             bkgSub="none", ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{raw}] an object of class \code{\LinkA{maigesRaw}{maigesRaw}} to be normalised.
\item[\code{span}] real number in (0,1) representing the proportion of points
to use in the loess regression.
\item[\code{propLoess}] real number in (0,1) representing the proportion of
points (spots) to be used in each iteration of loess.
\item[\code{nRep}] number of repetitions for loess procedure.
\item[\code{func}] character string giving the function to estimate the final
W value. You must use 'mean', 'median' or 'none' (default).
\item[\code{bkgSub}] character with background subtraction method, using the
function \code{\LinkA{backgroundcorrect}{backgroundcorrect}} from \emph{limma}
package.
\item[\code{...}] additional parameters for function
\code{\LinkA{loessFit}{loessFit}} from \emph{limma} package.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The LOWESS fitting for normalising microarray data is a computational
intensive task, so pay attention to not specify a very large argument
in \code{nRep}. If you do so, your process will take so much time to conclude.
\end{Details}
\begin{Value}
The result of this function is an object of class \code{\LinkA{maiges}{maiges}}.
\end{Value}
\begin{Author}\relax
Gustavo H. Esteves <\email{gesteves@vision.ime.usp.br}>
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{loessFit}{loessFit}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## Loading the dataset
data(gastro)

## Doing the repetition loess with default parameters. Be carefull, this
## is very time consuming
## Not run: 
gastro.norm = normRepLoess(gastro.raw2)
## End(Not run)

## Do the same normalization selecting 60% dos spots with 10
## repetitions and estimating the W by the mean value.
## Not run: 
gastro.norm = normRepLoess(gastro.raw2, propLoess=0.6, nRep=10, func="mean")
## End(Not run)

\end{ExampleCode}
\end{Examples}

