There are several possibilities, including glmmPQL (MASS) and GLMM (lme4). Be careful with the interpretation, as you don't have the advantages of balance that the classical AoV gives you.
On Thu, 4 Mar 2004, Christoph Scherber wrote: > I have proportion data with binomial errors. The problem is that the > whole experiment was laid out as a split-plot design. > > Ideally, what I would like is having a glm with an Error term such as > glm(y~x+Error(A/B)) but I fear this is not possible. Would using lme be > an alternative? How could I state the variance structure, then? -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
