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

In rama: Robust Analysis of MicroArrays

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

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)

rama documentation built on Nov. 8, 2020, 8:02 p.m.