Dear all, This is my first post here--hope this is appropriate. I'm trying to figure out how to simulate quantities of interest from a mixed (multilevel) logit model. The logit.mixed documentation makes this look straightforward, but I'd like to know what's going on "under the hood". Accounting for estimation uncertainty for the fixed effects is straightforward--just the same as a single-level model. But what about taking into account uncertainty about the random effects? I've searched around on the Internet (and read the lme4 documentation) and haven't had any luck figuring out how to do this in R. (I realize that the random effects at each level are by definition a distribution with a mean of 0, so I guess in many instances simulations for quantities of interest can simply leave out the random effects, because their mean effect is 0. But if we want to simulate for a particular set of X values in a specific group versus in another group, the random effect will clearly matter.) Doug Bates is on record as saying that standard errors don't make sense for the random effects (http://www.stat.columbia.edu/~cook/movabletype/archives/2008/12/uncertainty… ), since each estimate is not likely to follow a symmetrical distribution. And lme4's mcmcsamp(), as I understand it, is not operational. Gelman and Hill (2007) suggest using a Bayesian approach and BUGS--getting a posterior distribution of estimates--but I don't know BUGS and would rather not have to learn just for this. MLwiN includes SEs for random effects, but doesn't have a straightforward facility for estimating quantities of interest. So my question (if this all makes sense) is how Zelig deals with this issue. Is it finding some way to take into account estimation uncertainty for the random effects? Or just treating each random effect as a given, and only taking into account estimation uncertainty for the fixed effects? Any illumination about this point would be much appreciated. Thanks, Malcolm Fairbrother University of Bristol