Dear Friends, I have been following the discussions of mixed-effects models with some confusion, and I realize that much of this is work in progress, and that much of the discussion is beyond my knowledge of statistics. My question is simple, though: Is there a set of commands that will produce an output equivalent to the exceedingly useful predict(bl.lm, newdata = bl.new, type = 'response', interval = 'confidence') for (at least) gaussian mixed-effects model (but preferably for glmm models)? I have looked at predict.glmmPQL {MASS} predict.lme {nlme} and neither of them offer exactly what I need, and I just don't know enough to go from what they do offer to what I need. I have Pinheiro & Bates as well as MASS, and both come tantalizingly close to what I need, but I can't figure out the next step. AFAIK, there's nothing of the sort for aov(. ~ ., Error(...) ...) either. In order to publish results of designed psychological experiments (most of which are of the classic anova variety, with all the predictors being factors), we need to plot error bars on our interaction plots. I suspect (from the discussion on this list) that my colleagues are using SPSS or SAS, and reporting incorrect CIs. Not being a statistician I have hit a wall here. I'm not sure if the transition to the results I need is staring me in the face, and I don't know enough to take the next step, or if the tools aren't yet available. In any event, I would be very grateful to you for guidance on how to proceed. _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400 Charlottesville, VA 22904-4400 Parcels: Room 102 Gilmer Hall McCormick Road Charlottesville, VA 22903 Office: B011 +1-434-982-4729 Lab: B019 +1-434-982-4751 Fax: +1-434-982-4766 WWW: http://www.people.virginia.edu/~mk9y/