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.

1 |

`x` |
A vector of numerical values, e.q. the |

`...` |
An undefined number of objects similar with |

`bw` |
Band width required to compute the density distribution. |

`xlim` |
The range for abscissa of the density plots. |

`titles` |
Names to be displayed the charts legend. |

`type` |
If set to |

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.

1 2 3 4 5 6 7 8 9 | ```
# 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")
``` |

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