I may be missing something obvious here, but consider the following simple dataset simulating repeated measures on 5 individuals with pretty strong between-individual variance.
set.seed(1003) n<-5 v<-rep(1:n,each=2) d<-data.frame(factor(v),v+rnorm(2*n)) names(d)<-c("id","y") Now consider the following two linear models that provide identical fitted values, residuals, and estimated residual variance: m1<-lm(y~id,data=d) m2<-lm(y~id-1,data=d) print(max(abs(fitted(m1)-fitted(m2)))) The r-squared reported by summary(m1) appears to be correct in that it is equal to the squared correlation between the fitted and observed values: print(summary(m1)$r.squared - cor(fitted(m1),d$y)^2) However, the same is not true of m2. print(summary(m2)$r.squared - cor(fitted(m2),d$y)^2) > R.version _ platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 1 minor 9.0 year 2004 month 04 day 12 language R J.R. Lockwood 412-683-2300 x4941 [EMAIL PROTECTED] http://www.rand.org/methodology/stat/members/lockwood/ -------------------- This email message is for the sole use of the intended recipient(s) and may contain privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of the original message. ______________________________________________ [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