At 07:01 AM 12/16/2006, Prof Brian Ripley <[EMAIL PROTECTED]> wrote:
>What 'glmm' did you have in mind? Looks like e.g. glmmML and >glmmPQL will work with the new link. > >Someone may have been here already: e.g. >http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1434755 Thank you for your reply and the above link. I am not sure yet which function to use. I am new at logistic regression for repeated-measure designs and its various implementations in R. glmmML seems to require changes at the C-level: <http://www.stat.umu.se/forskning/reports/glmmML.pdf>http://www.stat.umu.se/forskning/reports/glmmML.pdf The new link might apparently be added to glmmPQL entirely at the R-level. >Looking at make.link() should give you enough to go on. If I understood your suggestion well, it is sufficient to add a new link for the binomial family by extending the make.link function and adding, say, a "half-logit" link (both for the glm and lme call in glmPQL). Then, the call to glmmPQL could be glmmPQL(resp~shape*ecc*kappa, random=~1|subject, family=binomial("half-logit"), data, correlation=corCompSymm(form=~1|subject)) assuming that resp=1 if response is correct and 0 otherwise, a random intercept (as in glmmML) and an "exchangeable" correlation structure. Note that it might be better to specify a "random slope" in this context (since proportions are expected to vary between 0.5 and 1 for all subjects, the main difference being that some subjects might have a steeper S-shaped curve than others) but I am not sure how to do it. Regards, Gabriel Baud-Bovy UHSR University, Milan, Italy ______________________________________________ [email protected] 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.
