[R-sig-ME] Error in .local(object, n, verbose, ...) : Update not yet written

[joana martelo]

Many thanks Ben! 

What I would like is to get the parameter estimates and confidence intervals
based both on the fixed and random effects, and not just on the fixed
effects. Is parametric bootstrapping the only alternative?

Thanks again!

Joana

-----Mensagem original-----
De: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Em nome de Ben Bolker
Enviada: quinta-feira, 22 de Novembro de 2012 22:38
Para: r-sig-mixed-models at r-project.org
Assunto: Re: [R-sig-ME] Error in .local(object, n, verbose, ...) : Update
not yet written

joana martelo <jmmartelo at ...> writes:

> 
> Dear list
> 
> I'm trying to model fish data with a binomial distribution for prey 
> capture success, using GLMM. My models look like  this:
> 
> Model1<-glmer(capture~velocity+(1|fish.id.),family=binomial,data=cap)
> 
> I'm using lme4, however, when I use the function mcmcsamp to obtain 
> the posterior distribution of the parameters I got this error:
> 
> Error in .local(object, n, verbose, ...) : Update not yet written
> 
> I'm using R version 2.15.2. Anyone knows what the problem might be?
> 
> Many thanks in advance!
> 
> Joana

  mcmcsamp has never worked for GLMMs and at this point probably never will,
because writing a reliable post-hoc MCMC sampler (i.e., one that doesn't get
stuck at low parameter valuables or have other undesirable behaviour) has
turned out to be really, really hard.

  If you really want the posterior distribution of the parameters, it should
be pretty easy to fit your model using MCMCglmm.  If you want confidence
intervals it's unfortunately harder than it should be, but you might try
parametric bootstrapping ... (the pbkrtest package has parametric
bootstrapping, but it is designed for parameter testing via model comparison
rather than for finding posterior/sampling distributions of parameters).

  Ben Bolker

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