Re: [R] Mixed-effects model for pre-post randomization design
Thank you very much, Ben. Your answer has been very 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.
Re: [R] Mixed-effects model for pre-post randomization design
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.
[R] Mixed-effects model for pre-post randomization design
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. Thank you. Marco. __ 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.
