[ADMB Users] NLMM Model Selection

[Chris Gast]

So, ADMB reports the (optimal) marginal loglikelihood approximation in
the .par file (correct?).

In order to obtain the (maximum) conditional loglikelihood value, one
would re-evaluate the likelihood function at the MLEs along with the
empirical Bayes estimates of RE terms.  Is that correct?

If the latter statement is correct, could this be obtained by
outputting the final objective function value (following SE
estimation) in the report file?  Or do I need to re-evaluate the
function after fitting the model?



Thanks again,

Chris Gast



-----------------------------
Chris Gast
cmgast at gmail.com



On Sun, Feb 6, 2011 at 11:54 AM, Ben Bolker <bbolker at gmail.com> wrote:
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> On 11-02-06 02:47 PM, dave fournier wrote:
>> Something that has always bothered me about this random effects stuff
>> is that if I fit a model with a neg bin dist it is just a parametric
>> model with one more parameter than a Poisson with a Poisson at the
>> end. I can do standard LR tests and random effects never come up. But
>> that is just because one can do the integration analytically so that
>> the RE nature or interpretation never comes up. How can that be?  Why
>> are other RE models different?
>
>  It really depends what you want to do. Dealing with random effects by
> integrating them out (when that is possible) is called a marginal model,
> and there are plenty of methods that take this approach (e.g.
> generalized estimating equations).  Sometimes you're actually interested
> in estimates of the 'random effects', which disappear in the marginal
> approach. In some cases the marginal approach doesn't give you separate
> estimates for different processes (e.g. variances from different random
> effects components) that you would ideally like to distinguish.
> Sometimes you wouldn't mind a marginal approach but it's just too hard.
>  There are also differences in interpretation -- for example, estimated
> slopes from marginal models (which give the overall expected,
> unconditional slope) are shallower than those from 'non-marginal'
> (conditional? don't know the right term) models, where one is estimating
> the slope conditional on individuals within a group.
>
>  Alan Agresti's book on Categorical Data Analysis has a very nice
> discussion of this stuff, I think.
>
>  Ben Bolker
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