I am running a mixed effects model with random intercepts
fit.courseCross <- lme(fixed= zGrade ~ Rep + ISE
+P7APrior+Female+White+HSGPA+MATH+Years+Course+Course*P7APrior ,
random= ~1|SID,
data = Master.complete[Master.complete$Course !=
"P7A",])
where all variables are factors except for HSGPA, MATH and Years
I noticed that predict.lm has an option for standard error, but
predict.nlme does not. I understand that this might be because there is a
difference between SE's that conditioned or not on random effects.
I have looked at this stack overflow question
<http://stackoverflow.com/questions/14358811/extract-prediction-band-from-lme-fit>
(extract-prediction-band-from-lme-fit) but do not understand what is being
done.
And would like to show the predicted fit of zGrade vs Years with a
confidence interval.
a-la ggplot's geom_smooth. The particular intercept does not mater ( I
don't care what the intercept is, though given a choice I'd prefer grand
mean centered) I would be happy with either conditional on unconditional
CI's.
Robert
[[alternative HTML version deleted]]