rama (version 1.46.0)

ls.effect: Compute the least squares estimates of the all the effects of the general model.

Description

Compute the least squares estimates of the all the effects of the general model.

Usage

ls.effect(sample1,sample2,dye.swap=FALSE,nb.col1=NULL)

Arguments

sample1

The matrix of intensity from the sample 1. Each row corresponds to a different gene.

sample2

The matrix of intensity from the sample 2. Each row corresponds to a different gene.

dye.swap

A logical value indicating if the experiment was a dye swap experiment.

nb.col1

An integer value correspinding to the number of arrays (columns) in the first group of the dye swap experiment. In other words, the number of replicates before the dyes have been swaped.

Value

mu

The baseline intensity

alpha2

The sample effect

beta2

The dye effect

delta22

The dye*sample interaction

eta

The array effects

gamma1

The genes effects in sample 1

gamma2

The genes effect in sample 2

M1

The main effects in sample 1

M2

The main effects in sample 2

R1

The residuals from the sample 1

R2

The residuals from the sample 2

Details

References

Robust Estimation of cDNA Microarray Intensities with Replicates Raphael Gottardo, Adrian E. Raftery, Ka Yee Yeung, and Roger Bumgarner Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322

See Also

fit.model

Examples

### Compute the least squares effects on the log scale
data(hiv)
ls.fx<-ls.effect(log2(hiv[,c(1:4)]),log2(hiv[,c(5:8)]),dye.swap=TRUE,nb.col1=2)