\HeaderA{compNorm}{Compares the distribution of several vectors at a time using either boxplots or density curves}{compNorm}
\keyword{univar}{compNorm}
\begin{Description}\relax
This function was concieved to easily compare several normalization methods in terms 
of variability of log-ratios, M. Basically it produces two plots: The first is a the density 
plot of the several matrices passed as arguments, while the second is a box plot. 
Median of absolute deviations for each method is printed on screen.
\end{Description}
\begin{Usage}
\begin{verbatim}
compNorm(x,...,bw="AUTO",xlim=c(-3,3),titles="AUTO",type="d") 
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] A vector of numerical values, e.q. the \eqn{M}{} values of a data set: \code{as.vector(maM(swirl))}.

\item[\code{...}] An undefined number of objects similar with \code{x}.

\item[\code{bw}] Band width required to compute the density distribution. \code{"AUTO"} 
will adjust \code{bw} to a suitable value.

\item[\code{xlim}] The range for abscissa of the density plots. 

\item[\code{titles}] Names to be displayed the charts legend. \code{"AUTO"} will use the matrices names passed as arguments.              .

\item[\code{type}] If set to \code{"d"}, density plot will be shown; if set to \code{"d"} box plot will be shown.

\end{ldescription}
\end{Arguments}
\begin{Details}\relax
This function is used to compare the normalized log ratios \eqn{M}{} obtained with several normalization methods.
\end{Details}
\begin{Value}
NULL, this function only displays charts and prints on the screen some statistics.
\end{Value}
\begin{Author}\relax
Tarca, A.L.
\end{Author}
\begin{References}\relax
A. L. Tarca, J. E. K. Cooke, and J. Mackay. Robust neural networks approach for spatial and
intensity dependent normalization of cDNA data. Bioinformatics. 2004,submitted.\\
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{maNormNN}{maNormNN}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
 # Normalize swirl data with two methods
 data(swirl)
 swirlNN<-maNormNN(swirl[,1])
 swirlLoess<-maNormMain(swirl[,1]) 
 nms<-c("None","Loess","NNets")
 #compare distributions: density plot
 compNorm(as.vector(maM(swirl[,1])),as.vector(maM(swirlLoess)),as.vector(maM(swirlNN)),xlim=c(- 2,2),bw="AUTO",titles=nms,type="d")
 #compare distributions: box plot
 compNorm(as.vector(maM(swirl[,1])),as.vector(maM(swirlLoess)),as.vector(maM(swirlNN)),xlim=c(- 2,2),bw="AUTO",titles=nms,type="b")

\end{ExampleCode}
\end{Examples}

