Hi Francois, When there the mixed model contains only one random effect, apart from the residual, I use the function randomizedBlock() from the statmod package which is quite a bit faster than lme() and also (I think) more likely to converge successfully. When there are two or more random effects, I don't know of any alternatives in R to lme(). Doug has promised a faster version of lme() as part of a large revision of the nlme package, but I don't know when that will be available. Unfortunately, remlscore() fits a very specialized type of model. It estimates a variance function rather than random effects and so isn't an alternative for this problem. Best regards Gordon At 03:25 AM 10/02/2004, Francois Collin wrote: >Hi Gordon, >I'd like to fit a mixed effect model to expression >values for each probe set on the U133A microarray. >Many on the BioConductor list have the same problem. >One solution is to call lme() within a loop (apply(), >or for()). I would have thought that using the >remlscore() function would provide a more efficient >alternative (in part because we can specify the X and >Y matrices once outside the loop, maybe other >reasons). I intend to make the test myself, but if >you know of a reason why this is not going to work I'd >appreciate the tip. >Thanks. > >-francois > >ps. If this question deserves an answer that would >benefit others, feel free to post it on the BioC list.