On 2/21/2013 6:02 PM, Mitchell Maltenfort wrote:
> One more link to look at
>
> http://glmm.wikidot.com/faq
>
> This is the r-sig-mixed-models FAQ.
Thanks so much for pointing that out. That seems to confirm that what I
want is lme4, in particular glmer().
Ross
>
> On Thu, Feb 21, 2013 at 8:53 PM, Ross Boylan <r...@biostat.ucsf.edu
> <mailto:r...@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).
>
> 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)
> 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/%7Emanning/courses/ling289/GLMM.pdf> also
> use lme4.
>
> However,
> 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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