# Compares the distribution of several vectors at a time using either boxplots or density curves

### Description

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.

### Usage

1 |

### Arguments

`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 |

### Details

This function is used to compare the normalized log ratios *M* obtained with several normalization methods.

### Value

NULL, this function only displays charts and prints on the screen some statistics.

### Author(s)

Tarca, A.L.

### References

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.

### See Also

`maNormNN`

### Examples

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")
``` |