As an extreme example of this sort of thing, consider fit <- lme(y ~ 1, random = ~ 1 | group)
where there is exactly one observation per group, so that it is not possible to get separate estimates of group and residual variances. Despite this, lme often (always?) provides a solution consistent with the data. Because of the singularity, the plot of residuals against fitted values for this solution shows a straight line. This is easily recognized as an aberration, but I can imagine configurations of data (e.g. with most groups having just one observation and a few with two or more) where the residual vs fitted value plot might show an apparent trend. On Wed, Aug 09, 2006 at 03:43:12PM -0400, Rick Bilonick 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? > > Rick B. > > ______________________________________________ > [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. -- ************************************************ * I.White * * University of Edinburgh * * Ashworth Laboratories, West Mains Road * * Edinburgh EH9 3JT * * Fax: 0131 650 6564 Tel: 0131 650 5490 * * E-mail: [EMAIL PROTECTED] * ______________________________________________ [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.
