James and all, I thought of reporting here a few final remarks on this issue, in case anyone will be every taking advantage from this thread. Here I was trying to estimate 8 parameters when I only had 6 data points (with four replicates each). At maximum, I could estimate just 6 parameters. In my 2^3 factorial design, model.matrix() was neatly preparing for me a design matrix with eight parameters, though the last two could not be estimated by lmFit(). It turned out that I needed those last two parameters and that I had to drop other two 'in bewteen', which could not be estimated in my experiment as they made no real sense. They were not needed in my model. Therefore, I proceeded from my design matrix by cutting two of the columns that were created by model.matrix(). > Dose <- factor(targets$Dose, levels=c("Cn", "1Gy")) # It's very important that you define the levels now, with their order, as otherwise he will do it for you and this may not be the way you want it. > Lab <- factor(targets$Lab, levels=c("ISS", "LNGS")) > Ageing <- factor(targets$Ageing, levels=c("t0", "6mo")) > disegno <- model.matrix(~Dose*Ageing*Lab) # makes a design matrix with several interaction parameters. > designo <- disegno[,1:3] > designo1 <- disegno[,5] # I don't need column 4 > designo <- cbind(designo, designo1) > colnames(designo[4])="Dose1Gy:Ageing6mo" > designo <- cbind(designo, disegno[,7:8]) # I don't need column 6 either > is.fullrank(designo) [1] TRUE After this, my lmFit() went well and it did not take too long before I could define all the contrasts of my interest. Massimo Massimo Pinto Post Doctoral Research Fellow Enrico Fermi Centre and Italian Public Health Research Institute (ISS), Rome http://claimid.com/massimopinto On Fri, Aug 7, 2009 at 4:34 PM, Massimo Pinto<pintarello at="" gmail.com=""> wrote: > thank you Jim, > > you are indeed correct: I did not specify how I created my matrix. > > Here it comes > ==== > Dose <- factor(targets$Dose, levels=c("Cn", "1Gy")) # It's very > important that you define the levels now, with their order, as > otherwise he will do it for you and this may not be the way you want > it. > Lab <- factor(targets$Lab, levels=c("ISS", "LNGS")) > Ageing <- factor(targets$Ageing, levels=c("t0", "6mo")) > disegno <- model.matrix(~Dose*Ageing*Lab) # makes a design matrix with > several interaction parameters and a total of eight param. > ==== > > given that my data set includes six independent samples (with four > biological replicates each), I suppose I cannot estimate more than 6 > parameters. But model.matrix() went on anyway... > > I suppose I could erase the last two columns of my 'disegno' > > Yours > Massimo > > On Wed, Aug 5, 2009 at 6:30 PM, James W. MacDonald<jmacdon at="" med.umich.edu=""> wrote: >> Hi Massimo, >> >> Massimo Pinto wrote: >>> >>> Greetings all, >>> >>> I have noticed that several users have presented the issue of >>> parameters not estimated in some cases of linear model fittings with >>> the limma function lmFit(). >>> >>> In trying to implement my 2^3 factorial design, I have followed >>> Bioinformatics and Computational Biology Solutions using R and >>> Bioconductor's examples as at Chapter 14 (Multifactor experiments). >>> ?I have encountered my own problem with two such parameters: >>> >>>> fit <- lmFit(esetSub, disegno) >>> >>> Coefficients not estimable: Ageing6mo:LabLNGS Dose1Gy:Ageing6mo:LabLNGS >>> Warning message: >>> Partial NA coefficients for 2176 probe(s) >> >> Well, you don't show how you built this design matrix, but I would bet you >> didn't use model.matrix because the matrix isn't of full rank, so you can't >> solve for all the coefficients you are trying to estimate here. >> >> The lmFit function is nice enough to let you know that, but you could have >> checked using either is.fullrank() to see if the matrix is of full rank, or >> nonEstimable() to see which coefficients aren't going to be estimated. >> >> Best, >> >> Jim >> >> >>> >>> whereby >>> >>>> disegno >>> >>> ? (Intercept) Dose1Gy Ageing6mo LabLNGS Dose1Gy:Ageing6mo >>> Dose1Gy:LabLNGS Ageing6mo:LabLNGS Dose1Gy:Ageing6mo:LabLNGS >>> 1 ? ? ? ? ? ?1 ? ? ? 0 ? ? ? ? 0 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 2 ? ? ? ? ? ?1 ? ? ? 0 ? ? ? ? 0 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 3 ? ? ? ? ? ?1 ? ? ? 0 ? ? ? ? 0 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 4 ? ? ? ? ? ?1 ? ? ? 0 ? ? ? ? 0 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 5 ? ? ? ? ? ?1 ? ? ? 1 ? ? ? ? 0 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 6 ? ? ? ? ? ?1 ? ? ? 1 ? ? ? ? 0 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 7 ? ? ? ? ? ?1 ? ? ? 1 ? ? ? ? 0 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 8 ? ? ? ? ? ?1 ? ? ? 1 ? ? ? ? 0 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 9 ? ? ? ? ? ?1 ? ? ? 0 ? ? ? ? 1 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 10 ? ? ? ? ? 1 ? ? ? 0 ? ? ? ? 1 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 11 ? ? ? ? ? 1 ? ? ? 0 ? ? ? ? 1 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 12 ? ? ? ? ? 1 ? ? ? 0 ? ? ? ? 1 ? ? ? 0 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 13 ? ? ? ? ? 1 ? ? ? 1 ? ? ? ? 1 ? ? ? 0 ? ? ? ? ? ? ? ? 1 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 14 ? ? ? ? ? 1 ? ? ? 1 ? ? ? ? 1 ? ? ? 0 ? ? ? ? ? ? ? ? 1 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 15 ? ? ? ? ? 1 ? ? ? 1 ? ? ? ? 1 ? ? ? 0 ? ? ? ? ? ? ? ? 1 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 16 ? ? ? ? ? 1 ? ? ? 1 ? ? ? ? 1 ? ? ? 0 ? ? ? ? ? ? ? ? 1 >>> ?0 ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 17 ? ? ? ? ? 1 ? ? ? 0 ? ? ? ? 1 ? ? ? 1 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 1 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 18 ? ? ? ? ? 1 ? ? ? 0 ? ? ? ? 1 ? ? ? 1 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 1 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 19 ? ? ? ? ? 1 ? ? ? 0 ? ? ? ? 1 ? ? ? 1 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 1 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 20 ? ? ? ? ? 1 ? ? ? 0 ? ? ? ? 1 ? ? ? 1 ? ? ? ? ? ? ? ? 0 >>> ?0 ? ? ? ? ? ? ? ? 1 ? ? ? ? ? ? ? ? ? ? ? ? 0 >>> 21 ? ? ? ? ? 1 ? ? ? 1 ? ? ? ? 1 ? ? ? 1 ? ? ? ? ? ? ? ? 1 >>> ?1 ? ? ? ? ? ? ? ? 1 ? ? ? ? ? ? ? ? ? ? ? ? 1 >>> 22 ? ? ? ? ? 1 ? ? ? 1 ? ? ? ? 1 ? ? ? 1 ? ? ? ? ? ? ? ? 1 >>> ?1 ? ? ? ? ? ? ? ? 1 ? ? ? ? ? ? ? ? ? ? ? ? 1 >>> 23 ? ? ? ? ? 1 ? ? ? 1 ? ? ? ? 1 ? ? ? 1 ? ? ? ? ? ? ? ? 1 >>> ?1 ? ? ? ? ? ? ? ? 1 ? ? ? ? ? ? ? ? ? ? ? ? 1 >>> 24 ? ? ? ? ? 1 ? ? ? 1 ? ? ? ? 1 ? ? ? 1 ? ? ? ? ? ? ? ? 1 >>> ?1 ? ? ? ? ? ? ? ? 1 ? ? ? ? ? ? ? ? ? ? ? ? 1 >>> attr(,"assign") >>> [1] 0 1 2 3 4 5 6 7 >>> attr(,"contrasts") >>> attr(,"contrasts")$Dose >>> [1] "contr.treatment" >>> >>> attr(,"contrasts")$Ageing >>> [1] "contr.treatment" >>> >>> attr(,"contrasts")$Lab >>> [1] "contr.treatment" >>> >>> I have checked my design and it does make good sense to me. But, I >>> believe I was asking too much from my parameter estimation. >>> I am wondering how is this going to change the way I design my >>> contrast matrix? I am trying to define my contrast matrix following >>> the formulation of null hypotheses as at the 'yellow' book, p244. >>> >>> Thanking you very much, >>> >>> Massimo >>> >>> Massimo Pinto >>> Post Doctoral Research Fellow >>> Enrico Fermi Centre and Italian Public Health Research Institute (ISS), >>> Rome >>> http://claimid.com/massimopinto >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at stat.math.ethz.ch >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> -- >> James W. MacDonald, M.S. >> Biostatistician >> Douglas Lab >> University of Michigan >> Department of Human Genetics >> 5912 Buhl >> 1241 E. Catherine St. >> Ann Arbor MI 48109-5618 >> 734-615-7826 >> >