[R] wrong calculation of R-squared in summary.lm

[Stefan Schlager]

Dear all,

I just stumbled upon the fact, that when I perform a regression on 
multivariate responses, that are not centred, I get a ricilulously high 
R-squared value. After reading the code of summary.lm, I found a bug in 
the function summary.lm:

mss is calculated by:
  mss <-sum((f - mean(f))^2) -  where f are the fitted values.

This works only for a single response variable, because otherwise the 
matrix containing the fitted values is scaled by a scalar. replacing
this with:
mss <- scale(f,scale=FALSE) solved the problem for me.

Example for bug:
response<-cbind(rnorm(500)+1000,rnorm(500)+300)
predictor<-rnorm(500)
fit<-lm(response ~ predictor)
summary.lm(fit) now reports a R^2 value of 1!!!

Please correct me, if I'm wrong

Stefan

Stefan Schlager M.A.
Anthropologie
Medizinische Fakultät der der Albert Ludwigs- Universität Freiburg
Hebelstr. 29

79104 Freiburg

Anthropology
Faculty of Medicine, Albert-Ludwigs-University Freiburg
Hebelstr. 29
D- 79104 Freiburg

phone +49 (0)761 203-5522
fax +49 (0)761 203-6898



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