Full_Name: lieven clement Version: R version 2.4.0 Patched (2006-11-25 r39997) OS: i486-pc-linux-gnu Submission from: (NULL) (157.193.193.180)
summary.lm() does not calculate R² accurately for models without intercepts if one of the predictor variables is a factor. In order to avoid one of the factor levels to be considered as a reference class you can use the -1 option in a formula. When you use this, R² is not correctly calculated. > x1<-rnorm(100) > x2<-c(rep(0,25),rep(10,25),rep(20,25),rep(30,25)) > y<-10*x1+x2+rnorm(100,0,4) > x2<-as.factor(x2) > lmtest<-lm(y~-1+x1+x2) > summary(lmtest)$r.sq [1] 0.9650201 > 1-sum(lmtest$res^2)/sum((y-mean(y))^2) [1] 0.9342672 The R squared by summary is calculated as > 1-sum(lmtest$res^2)/sum((y)^2) [1] 0.9650201 apparently because lm.summary assumes the mean of y to be zero. In case of an intercept model everything seems ok > lmtest<-lm(y~x1+x2) > summary(lmtest)$r.sq [1] 0.9342672 > 1-sum(lmtest$res^2)/sum((y-mean(y))^2) [1] 0.9342672 ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel