compNorm {nnNorm}  R Documentation 
This function was concieved to easily compare several normalization methods in terms of variability of logratios, 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.
compNorm(x,...,bw="AUTO",xlim=c(3,3),titles="AUTO",type="d")
x 
A matrix of numerical values, e.q. the M values of a data set: maM(swirl) .

... 
An undefined number of objects similar with x .

bw 
Band width required to compute the density distribution. "AUTO"
will adjust bw to a suitable value.

xlim 
The range for abscissa of the density plots. 
titles 
Names to be displayed the charts legend. "AUTO" will use the matrices names passed as arguments. .

type 
If set to "d" , density plot will be shown; if set to "d" box plot will be shown.

This function is used to compare the normalized log ratios M obtained with several normalization methods.
NULL, this function only displays charts and prints on the screen some statistics.
Tarca, A.L.
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.
# Normalize swirl data with two methods data(swirl) swirlNN<maNormNN(swirl[,1])$batchn swirlLoess<maNormMain(swirl[,1]) nms<c("None","Loess","NNets") #compare distributions: density plot compNorm(maM(swirl[,1]),maM(swirlLoess),maM(swirlNN),xlim=c( 2,2),bw="AUTO",titles=nms,type="d") #compare distributions: box plot compNorm(maM(swirl[,1]),maM(swirlLoess),maM(swirlNN),xlim=c( 2,2),bw="AUTO",titles=nms,type="b")