Gregor, Thanks for your replies.
1) Yes, I have tweaked the data to show as clearly as I can that this is a bug, that a tiny change in initial conditions causes the collapse of a reasonable 'parameter' estimate. 2) mcmcsamp() does not work (currently) for binomial fitted models. 3) This is an issue of what happens when the sample is too small. For all larger data sets I have gotten a ranef variance between 0.05 and 1.00 or so. It makes no sense to say that as the data set gets smaller, the systematic variation between Items goes away. It doesn't, as I've shown. In the data above, certain Items were still 10+ times as likely (log-odds wise) to have Response==1 as others. It may make sense to say that the effect becomes unestimable, due to its small size. But my understanding is not that this should make the algorithm return zero as an estimated value. D ______________________________________________ R-help@stat.math.ethz.ch 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.