ls.effect {rama}R Documentation

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.

Details

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

Author(s)

Raphael Gottardo

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)

[Package rama version 1.0.1 Index]