Marco Barbàra <jabbba <at> gmail.com> writes: > > DeaR userRs, > > I recently read this Liang-Zeger article: > > http://sankhya.isical.ac.in/search/62b1/fpaper7.html > > in which (among other things) they adopt a random intercept model for > pre-post designed trials, using a conditional likelihood approach > (I didn't think it possible with only two measurements per subject) > > I'm trying to figure out (if and) how it is possible to reproduce > straightforwardly their model using R standard mixed model tools, but > I cannot even try to reproduce their work, since they used a > non-available dataset (I found an extract on prof. Diggle's web site > where it is explicitly reported to be "confidential"), so I have to > review a bit of likelihood theory along with some implementation > details. > > In the meantime, I wonder if anyone here could point out any related > documentation to me. >
This might get more attention on r-sig-mixed-mod...@r-project.org. I took a quick look at the paper, but it's not a case where the answer is immediately obvious. The paper of reference for lme4 (see http://cran.r-project.org/web/packages/lme4/citation.html ) gives technical details of lme4's implementation, in case that's useful. ______________________________________________ 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.
