[R-sig-ME] binomial fixed-effect p-values by simulation

[Ben Bolker]

  The other criterion is that the number of blocks has to be large.
I have seen *no* rules of thumb for how large ... in the presentation
that Doug Bates posted about recently ( 
http://www.stat.wisc.edu/~bates/PotsdamGLMM/GLMMD.pdf )
p. 32, 1934 observations, 60 groups, he uses LRT ... (you could try running
your stuff on his example -- I think all the code etc is available from 
his web site)
to see how well this works -- although here he gets p=0.796, so 
anti-conservative
would just make that larger ...

  Ben


Daniel Ezra Johnson wrote:
> We read, e.g. in Pinheiro and Bates, that one situation where
> fixed-effect LRTs are anti-conservative is when the number of fixed
> effect parameters being tested is large with respect to the number of
> groups.
>
> In the tests I'm doing, there's only a single binary fixed effect
> factor being tested - a between-subjects factor like gender, as noted
> earlier.
>
> I'm finding evidence for pretty serious anti-conservatism here too
> (e.g. LRT chi-square p=0.0001 vs. LRT simulation p=0.016), and I'm
> working on some reproducible code to demonstrate this.
>
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