testing fixed effects in binomial lmer...again?

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|>>
|>>> However, I am not sure about what I should do to test for the
|>>> significance of fixed effects in the binomial case
|> What about Bootstrap (parametric or not)? Would it be useful in this
|> case?
|>
|
| The only problem is specifying a bootstrap mechanism that respects the
| structure of the random effects.  So for time series data, your bootstrap
| samples have to remain AR1 or whatever (ie you don't want gaps
appearing that
| aren't in the observed data), and for genetic type data (the kind I have),
| that pseudosample people are appropriately related to one another.
Resampling
| clusters works for that kind of data, though I think you need many
clusters.
| There are several papers in the area of genetic linkage analysis that
have
| validated bootstrapping for a test that a variance component is zero.
|
| But for testing simple hypotheses about particular fixed effects,
| a permutation/randomization test should work, I think.
|
| David Duffy.

~  My favorite solution (which worked in nlme, I think, but might
take some time to get for lme4 ...) would to be able to generate
posterior simulations from the reduced model, then use these to
generate a null distribution for F statistics (or whatever) for
the model comparison.  This seems as though it would actually be
a relatively straightforward extension of mcmcsamp, once it exists --
although arguably once you have mcmcsamp you wouldn't need it
any more ...

~   Ben Bolker

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