Please could someone help me understand the difference between the following two models?
lme (Y ~ X, random= ~1|G1/G2) aov (Y ~ X + Error (G1/G2)) Since random effects can be considered as additional error terms, I would have thought these two model formulations should amount to the same analysis, but the outputs (using suitable data, with X, G1 and G2 being factors) look very different. I note that the lme() model can be fitted even where the levels of X do not vary within levels of G2, whereas the aov() model output is accompanied by a warning message ("Error() model is singular...") in this case. Thanks, Richard. -- Richard Gunton PhD student - Ecology and Evolution group School of Biology, University of Leeds, LS2 9JT, UK Room 10.16, Miall Building Tel: 0113 3432825 http://www.personal.leeds.ac.uk/~bgyrg ~ Opinions expressed in this message are not attributable to the University of Leeds ~ ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.