Re: [R] generalized linear mixed models - how to compare?

From: Nestor Fernandez <nestor.fernandez_at_ufz.de>
Date: Thu 21 Apr 2005 - 02:38:24 EST

Dear all,

Thanks for the responses to this post.
I understand that the topic still requires more research. However, I am a non-statistician in a desperate need to analyze my ecological data with the currently available tools. Please excuse again my non-expert question: Would I commit a huge mistake if I use the likelihood estimates from GLMM as a "good approximate" to the "real" log-likelihood, and therefore calculate AIC from it? Should I use instead any of the existing corrections for AIC? Otherwise, can you suggest any other model selection approach suitable for generalized mixed models?

Nestor

Deepayan Sarkar wrote:
> On Sunday 17 April 2005 12:07, Prof Brian Ripley wrote:
>

>>On Sun, 17 Apr 2005, Deepayan Sarkar wrote:

>
>
> [...]
>
>
>>>GLMM uses (mostly) the same procedure to get parameter estimates,
>>>but as a final step calculates the likelihood for the correct model
>>>for those estimates (so the likelihood reported by it should be
>>>fairly reliable).
>>
>>Well, perhaps but I need more convincing.  The likelihood involves
>>many high-dimensional non-analytic integrations, so I do not see how
>>GLMM can do those integrals -- it might approximate them, but that
>>would not be `calculates the likelihood for the correct model'.  It
>>would be helpful to have a clarification of this claim.  (Our
>>experiments show that finding an accurate value of the log-likelihood
>>is difficult and many available pieces of software differ in their
>>values by large amounts.)

>
>
> You are right, of course. I left out too much trying to be brief (partly
> because this issue has been discussed before). I'll try to refrain from
> giving such partial answers in future.
>
> Deepayan
>
> [...]


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