Daniel Ezra Johnson <johnson4 <at> babel.ling.upenn.edu> writes: > 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.
I would not call this a bug, since this is related to data and not to the software. I might be wrong! > 2) mcmcsamp() does not work (currently) for binomial fitted models. Sorry, for wrong pointer. You could try with some other packages if they have support for binomial models with "random" effects. I would just try in BUGS --> take a look at R2WinBUGS or Brugs. > 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 I believe that when data gets smaller such parameters are harder to estimate and you can easily get 0 as MLE. > above, certain Items were still 10+ times as likely (log-odds wise) to > have Response==1 as others. Gregor ______________________________________________ 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.
