On 8/9/06, Rick Bilonick <[EMAIL PROTECTED]> wrote: > I'm fitting a mixed effects model: > > fit.1 <- lme(y~x,random=~1|id,data=df) > > There are two different observations for each id for both x and y. When > I use plot(fit.1), there is a strong increasing linear trend in the > residuals versus the fitted values (with no outliers). This also happens > if I use random=~x|id. Am I specifying something incorrectly?
Could you provide a reproducible example please? I suspect that the problem comes from having only two observations per level of id. When you have very few observations per group the roles of the random effect and the per-observation noise term in explaining the variation become confounded. However, I can't check if this is the case without looking at some data and model fits. ______________________________________________ [email protected] 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.
