[R-sig-ME] testing fixed effects in binomial lmer...again?
bolker at ufl.edu
Tue Jan 8 23:21:31 CET 2008
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David Duffy wrote:
| On Tue, 8 Jan 2008, Dimitris Rizopoulos wrote:
|>> On Jan 8, 2008 5:38 AM, Achaz von Hardenberg <fauna at pngp.it> wrote:
|>>> 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
| 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
| aren't in the observed data), and for genetic type data (the kind I have),
| that pseudosample people are appropriately related to one another.
| clusters works for that kind of data, though I think you need many
| There are several papers in the area of genetic linkage analysis that
| 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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