On Wed, 2006-08-09 at 15:04 -0500, Douglas Bates wrote: > 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.
I tried using geeglm from geepack to fit a marginal model. I understand this is not the same as a mixed effects model but the residuals don't have the linear trend. Should I avoid using lme in this case? Rick B. ______________________________________________ R-help@stat.math.ethz.ch 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.
