Daniel Caro <dcarov <at> gmail.com> writes: > > Dear all > > I have a model that looks like this: > > m1 <- lmer(Difference ~ 1+ (1|Examiner) + (1|Item), data=englisho.data) > > I know it is not possible to estimate random effects but one can > obtain BLUPs of the conditional modes with > > re1 <- ranef(m1, postVar=T) > > And then dotplot(re1) for the examiner and item levels gives me a nice > prediction interval. But I would like to have the prediction interval > for the individual intercepts, not the conditional modes of the random > effects, that is, the fixed effect (overall estimated intercept) + the > conditional mode of the random effect (examiner or item level). Does > this make sense? And if so, how would I calculate this? I'd like to do > the same thing to obtain prediction intervals of individual growth > rates in longitudinal models (i.e., overall growth rate + random > effect).
I think this belongs on the r-sig-mixed-mod...@r-project.org list. Could you please re-post it there? (I would redirect it myself but am reading via gmane ...) For a start, I would probably assume independence of the uncertainty in the conditional modes and in the overall slope parameter and compute the overall variance by adding the variances ... ? (Not sure that's right.) Ben Bolker ______________________________________________ R-help@r-project.org 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.
