Daniel Ezra Johnson <johnson4 <at> babel.ling.upenn.edu> writes: ... > More broadly, is it hopeless to analyze this data in this manner, or > else, what should I try doing differently? It would be very useful to > be able to have reliable estimates of random effect sizes, even when > they are rather small. ...
You might try with mcmcsamp to get a better view of posterior distributions of your parameters. It might be the case that MLE for item variance is near 0, while its posterior distribution covers also values that are higher than 0. 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.
