# Function for normalizing the mean and variance (or just the variance) of single replicate log ratios

### Description

This performs a robust loess normalization of the variance of the log ratios in a single replicate experiment by regressing the absolute (mean normalized) log ratios on the log intensities and using the fitted values to scale the (mean normalized) log ratio for each gene.

### Usage

1 | ```
norm1b(logratio, logintensity, span1 = 0.6, span2 = 0.2, mean.norm=TRUE)
``` |

### Arguments

`logratio` |
A vector or single-column matrix of log (base 2) ratios of gene expressions in two samples, if mean.norm is FALSE the log ratios should be already mean normalized. |

`logintensity` |
A vector or single-column matrix of log (base 2) total intensities (defined as the product) of gene expressions in the two samples. |

`span1` |
Proportion of data used to fit the loess regression of the log ratios on the log intensities for the mean normalization. |

`span2` |
Proportion of data used to fit the loess regression of the absolute (mean normalized) log ratios on the log intensities for the variance normalization. |

`mean.norm` |
A logical value indicating whether or not a mean normalization should be performed prior to the variance normalization. |

### Value

A vector or single-column matrix of mean and variance normalized log (base 2) ratios of gene expressions in two samples.

### Author(s)

N. Dean and A. E. Raftery

### References

N. Dean and A. E. Raftery (2005). Normal uniform mixture differential gene expression detection for cDNA microarrays. BMC Bioinformatics. 6, 173-186.

http://www.biomedcentral.com/1471-2105/6/173

### See Also

`norm1a`

,`norm1c`

,`norm1d`

,`norm2c`

,`norm2d`

### Examples

1 2 3 4 5 |

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