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 <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<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. > > ______________________________**________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help> > PLEASE do read the posting guide http://www.R-project.org/** > posting-guide.html <http://www.R-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
