Hello, I am interested in running generalized estimating equation models in R. Currently there are two main packages for doing so in R, geepack and gee. I understand that even though one can obtain similar to almost identical results using either of the two, that there are differences between the packages.
The paper that introduces the geepack package ( https://www.jstatsoft.org/article/view/v015i02) states that the three features that distinguishes this package from others in R and other stats software programs include: 1. There is an interface function geeglm which is designed to be as similar to glm as possible 2. A jackknife variance estimator is available as an alternative to the sandwich estimator 3. Covariates can be incorporated into the scale and correlation parameters in a similar fashion to the mean modeling However from other pages, it also appears that the underlying algorithm these packages use to obtain the results are slightly different. According to this post (https://stat.ethz.ch/pipermail/r-help/2003-October/040995.html), The two packages are using different estimators for the correlation parameter, and therefore different weights for the observations. From my understanding, while you can choose between multiple estimators using the geepack package, that both involve the use of the sandwich estimators as the default. I tried reading the documentation for both packages, but could not find anything going into detail about the estimators used by these packages and the specific differences between them. >From my use of both these packages on the same data and regression, results can be near identical or at least very close. Is there anything out there that goes into specifics about the differences between these packages? Thanks, Mohamad [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
