One more link to look at

http://glmm.wikidot.com/faq

This is the r-sig-mixed-models FAQ.

On Thu, Feb 21, 2013 at 8:53 PM, Ross Boylan <ross@biostat.ucsf.edu> wrote:

> I want to analyze binary, multinomial, and count outcomes (as well as the
> occasional continuous one) for clustered data.
> The more I search the less I know, and so I'm hoping the list can provide
> me some guidance about which of the many alternatives to choose.
>
> The nlme package seemed the obvious place to start.  However, it seems to
> be using specifications from nls, which does non-linear least squares.  I
> found the documentation opaque, and I'd prefer to stay in the generalized
> linear model framework and, ideally, maximum likelihood estimators.  (A
> recent review found maximum likelihood estimators using quadrature
> performed better than penalized likelhood methods, which specifically
> included glmmPQL in MASS: http://www.ncbi.nlm.nih.gov/**pubmed/20949128<http://www.ncbi.nlm.nih.gov/pubmed/20949128>
> ).
>
> The lme4 package apparently supports generalized linear models.  The title
> of the package is "lme4: Linear mixed-effects models using S4 classes" but
> the brief description is "Fit linear and generalized linear mixed-effects
> models."
>
> Various people, including Douglas Bates in 2011 (http://lme4.r-forge.r-**
> project.org/slides/2011-01-11-**Madison/5GLMM.pdf<http://lme4.r-forge.r-project.org/slides/2011-01-11-Madison/5GLMM.pdf>)
> who is an author of both nlme and lme4, seem to use it. Some 2007 slides by
> Chris Manning: http://nlp.stanford.edu/~**manning/courses/ling289/GLMM.**
> pdf <http://nlp.stanford.edu/~manning/courses/ling289/GLMM.pdf> also use
> lme4.
>
> However, http://cran.cnr.berkeley.edu/**web/views/SocialSciences.html<http://cran.cnr.berkeley.edu/web/views/SocialSciences.html>says "the lme4 package, which largely supersedes nlme for *linear* mixed
> models", suggesting nlme is the most appropriate choice.
>
> Finally, there's gee in the same problem area.  Since I'm fuzzy on the
> underlying theory, and actually want to use the models to generate
> individual level imputations (and I know GEE is about the marginal
> distributions), I'd also rather avoid it.
>
> Thanks for any guidance.  Summarizing, the candidates include at least
> nlme
> glmmPQL (in MASS)
> lme4
> gee
>
> I think lme4 is what I want, despite the title and the Social Science task
> page.
>
> Ross Boylan
>
>
> P.S. Zero inflated models would be nice too.
>
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