Monte Carlo simulations for lmer with binomial link

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Hello,

I fitted a linear mixed effects model (lmer) with the binomial link
function (family = binomial). I tried to obtain pMCMC values for this
regression model by using pvals.fnc, but this does not work. Similarly,
mcmcsamp does not seem to work with binomial dependent variables. Are
there alternative methods for obtaining p-values for these lmer models
with Monte Carlo (or other) simulations? Many thanks in advance!

Cheers,

Marco van de Ven
MPI Nijmegen

Not extremely easily.  See if anything at
<http://glmm.wikidot.com/faq> helps.
Hi Marco,

The newest version of MCMCglmm calculates p-values when you call  
summary. These should be reasonably close to what should be obtained  
under mcmcsamp/pvals.func unless there is a lot of over-dispersion. In  
this case the p-values will be larger  - as they should be.

For testing groups of effects as in anova, I recently recommended to  
someone that they calculate the posterior covariance matrix and pass  
it to Wald.test in the aod package. I am not advocating this, but I  
would be interested in other people's thoughts. If the posterior  
distribution of the effects is close to multivariate normal (its  
multivariate-t for Gaussian models) I can't see a problem, but perhaps  
there are some issues....

Cheers,

Jarrod

Cheers,

Jarrod

Quoting Marco van de Ven <m.a.m.vande.ven at gmail.com>:
Hello,

I fitted a linear mixed effects model (lmer) with the binomial link
function (family = binomial). I tried to obtain pMCMC values for this
regression model by using pvals.fnc, but this does not work. Similarly,
mcmcsamp does not seem to work with binomial dependent variables. Are
there alternative methods for obtaining p-values for these lmer models
with Monte Carlo (or other) simulations? Many thanks in advance!

Cheers,

Marco van de Ven
MPI Nijmegen

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