quasi-binomial family in lme4

On Tue, Nov 9, 2010 at 9:02 AM, Jarrod Hadfield  
<j.hadfield at ed.ac.uk> wrote:

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?

I agree.  I should have removed them from lme4 long ago if I suspected
that the results were not correct.

I will do so (once I figure out how to forbid them).

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

Hi,
I am analyzing some data that seems to require a quasibinomial  
model,
but the model returns incredibly small standard errors (and
correspondingly inflated t-values) that do not seem to be justified
given my data.
I've been reading (I think) all available posts on problems with the
quasi-binomial family in lme4. But I can't judge from the posts
whether all issues with the  quasi-binomial models in lme4 are  
assumed
to be resolved. So, I am wondering, are there any known issues with
quasi-binomial models in lme4?

I am using

Package:            lme4
Version:            0.999375-35
Date:               2010-08-18

in environment:
              _
platform       i386-pc-mingw32
arch           i386
os             mingw32
system         i386, mingw32
status
major          2
minor          11.1
year           2010
month          05
day            31
svn rev        52157
language       R
version.string R version 2.11.1 (2010-05-31)

cheers,
Florian

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