Rsquared bug lm() (PR#10516)

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 R?? 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, R?? 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