[R] Logistic regression : dicrepancies between glm and nls ?

[Emmanuel Charpentier]

Prof Brian Ripley wrote:

>[ ... ]
>
>>So my only hope is to embark on ML-estimations and likelihood ratio (or
>>Akaike IC) tests ...
>>
>>What would you recommend for this task ? I am not aware of a R package
>>directly built to do that, except GLMM, which I do not yet know how to
>>use (but I'll have a serious look at it).
>>
>
>For binomial GLMMs?  Well Lindsey's glmm() function only does the (very)
>special case of a single random intercept, and the glmm() in GLMMgibbs
>does more but is slow and often fails with binomial GLMMs, especially
>binary ones.
>
Well ... I was considering GLMMGibbs. But, since I'm already (for other 
reasons :see the answers to mu question las week about meta-analyses) 
trying to get some knowledge of Bayesian fundamentals, I'll also have a 
look at what WinBUGS has to say about this.

>
>The next version of the MASS package (on test for 1.4.0) has glmmPQL, a
>wrapper around lme that does a reasonable job of estimation.  But just as
>nlme is not good for testing (the likelihoods are very approximate)
>so does glmmPQL.  We have better methods under development but not ready
>for release yet.
>
>As for writing your own code: deciding what to implement is hard enough.
>I had a Masters' student last summer investigate a number of packages,
>including R ones, and about half the answers were not credible!
>
Hmmm ... I will therefore abstain to pursue.

                                    Emmanuel Charpentier



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