[R-sig-ME] [Fwd: Re: Wald F tests]
Ben Bolker
bolker at ufl.edu
Tue Oct 7 23:51:01 CEST 2008
But ... LRTs are non-recommended (anticonservative) for
comparing fixed effects of LMMs hence (presumably) for
GLMMs, unless sample size (# blocks/"residual" total sample
size) is large, no?
I just got through telling readers of
a forthcoming TREE (Trends in Ecology and Evolution) article
that they should use Wald Z, chi^2, t, or F (depending on
whether testing a single or multiple parameters, and whether
there is overdispersion or not), in preference to LRTs,
for testing fixed effects ... ? Or do you consider LRT
better than Wald in this case (in which case as far as
we know _nothing_ works very well for GLMMs, and I might
just start to cry ...) Or perhaps I have to get busy
running some simulations ...
Where would _you_ go to find advice on inference
(as opposed to estimation) on estimated GLMM parameters?
cheers
Ben Bolker
Douglas Bates wrote:
> If I were using glmer to fit a generalized linear mixed model I would
> use likelihood ratio tests rather than Wald tests. That is, I would
> fit a model including a particular term then fit it again without that
> term and calculate the difference in the deviance values, comparing
> that to a chi-square.
>
> I'm not sure how one would do this using the results from glmmPQL.
>
> On Fri, Oct 3, 2008 at 3:37 PM, Ben Bolker <bolker at ufl.edu> wrote:
>> [forwarding to R-sig-mixed, where it is likely to get more
>> responses]
>>
>> Mark Fowler wrote:
>>
>> Hello,
>> Might anyone know how to conduct Wald-type F-tests of the fixed
>> effects estimated by glmmPQL? I see this implemented in SAS (GLIMMIX),
>> and have seen it recommended in user group discussions, but haven't come
>> across any code to accomplish it. I understand the anova function treats
>> a glmmPQL fit as an lme fit, with the test assumptions based on maximum
>> likelihood, which is inappropriate for PQL. I'm using S-Plus 7. I also
>> have R 2.7 and S-Plus 8 if necessary.
>>
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