Fran?oise; I am glad that helps. Generally speaking, block effect is random from a distribution. But if you don't have enough blocks to estimate this distribution ( in the case of two levels within block), I do think that a fixed effect model is better, especially when you have many units within each block. Bests; Fangxin > Fangxin, > > Thank you very much for your answer. I derived my models from the > technical replicates examples in the Limma manual. I am glad you can > confirm that model 1 corresponds to a random effect model and model 2 to a > fixed effect model. You seem to hint that with two levels for block a > fixed-effects model would be more appropriate. Is this correct? For a > given factor, is a fixed-effects model better if you have fewer levels? > > Thanks, > > Fran?oise > > -----Original Message----- > From: Fangxin Hong [mailto:fhong@salk.edu] > Sent: Friday, November 05, 2004 6:17 PM > To: Thibaud-Nissen, Francoise > Cc: bioconductor@stat.math.ethz.ch > Subject: Re: [BioC] limma and blocks > > I think you can read the new limma User's Guide, section 9.4. should > answer your questions. I am sorry that I did notice that you have only two > blocks (growth condition), tht way maybe model 2 is better. > > Limma User's Guide, available from > http://bioinf.wehi.edu.au/limma/usersguide.pdf > > Fx > >> Hi, >> >> >> >> I am analyzing an experiment using 32 Arabidopsis Affymetrix chips. It > is >> basically a 2x2x4 design repeated twice using different biological > replicates (the third replicate will be provided later). The plants > within >> each biological replicate were grown at the same time, and in that sense > are related and form a block. There are no technical replicates within > each block. >> >> >> >> I am using limma. In the model I calculate separate coefficients for > each >> of the 16 conditions. I then use contrasts matrices to evaluate > contrasts >> of interest. >> >> >> >> I now would like to incorporate the block effect in my model in order to > account for random variation in the growth conditions between the two > biological replicates. >> >> >> >> I tried two models that give different results, but I am not sure any of > them is correct: >> >> >> >> If the first biological replicate appears first in my design, and > "design" >> is my design matrix for the 16 coefficients: >> >> >> >> Model 1: >> >> biorep <- c(rep(1,16),rep(2,16)) >> >> fit <- lmFit(mydata, design, block= biorep) >> >> fit <- eBayes(fit) >> >> ... > >> Model 2: >> >> blockdiff <- c(rep(1,16),rep(-1,16)) >> >> blockdesign <- cbind(design, Block=blockdiff) >> >> fitblock <-lmFit(mydata, blockdesign) >> >> fitblock <- eBayes(fitblock) >> >> ... >> >> I would appreciate any tip that could put me in the right track! >> >> >> >> Thanks, >> >> >> >> Fran?oise >> > > > -- Fangxin Hong, Ph.D. Plant Biology Laboratory The Salk Institute 10010 N. Torrey Pines Rd. La Jolla, CA 92037 E-mail: fhong@salk.edu