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). Many thanks for your help, Daniel
lmer - BLUP prediction intervals
2 messages · Daniel Caro, Ben Bolker
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-models at 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