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quasi-binomial family in lme4

1 message · Ben Bolker

#
Alas, I may have (in my own small way) contributed to this, by
including a quasi-Poisson example (of Arabidopsis fruiting) in a review
article in TREE.  We did look at the results reasonably carefully, and
they seemed to make sense in that case (although of course we didn't
know and still don't know what the 'true' answer is).  I should try to
re-run those analyses in various ways (primarily individual-level random
effects in glmer and MCMCglmm; the structure of the random effects is
currently too complicated for glmm.admb and gnlmm, I think ...)

-------- Original Message --------
Subject: Re: [R-sig-ME] quasi-binomial family in lme4
Date: Tue, 9 Nov 2010 15:02:45 +0000
From: Jarrod Hadfield <j.hadfield at ed.ac.uk>
To: T. Florian Jaeger <tiflo at csli.stanford.edu>
CC: r-sig-mixed-models at r-project.org

Hi Florian,

This comes up regularly and the list (nearly) always stays silent. As
far as I am aware quasi models in lmer do not, and never have, given
sensible results. To model over-dispersion you can try fitting an
observation-level random effect. For example:

data$resid<-as.factor(1:dim(data)[1])

and fitting (1|resid) in the model formula.

This year  I have reviewed four papers that have used quasi models in
lmer, and its no fun to tell the authors that their results may not be
meaningful. To paraphrase an earlier post, why are they there if they
do not work - it's irresponsible?

Jarrod
On 8 Nov 2010, at 03:30, T. Florian Jaeger wrote: