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 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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