[Fwd: Re: Wald F tests]
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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