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
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