Achim Zeileis, Donnerstag, 18. Oktober 2007: > On Thu, 18 Oct 2007, Toffin Etienne wrote: > > > Hi, > > A have small technical question about the calculation of R-squared > > using lm(). > > In a study case with experimental values, it seems more logical to > > force the regression line to pass through origin with lm(y ~ x +0). > > However, R-squared values are higher in this case than when I > > compute the linear regression with lm(y ~ x). > > It seems to be surprising to me: is this result normal ? Is there any > > problem in the R-squared value calculated in this case ? > > Have you considered reading the documentation? ?summary.lm has > > r.squared: R^2, the 'fraction of variance explained by the model', > > R^2 = 1 - Sum(R[i]^2) / Sum((y[i]- y*)^2), > > where y* is the mean of y[i] if there is an intercept and > zero otherwise.
I think there is reason to be surprised, I am, too. The fraction of variance explained should never be smaller when there are two values to fit the data to. Of course, if mean(y)=0 anyway there should be no difference in R^2 (except that the error df of the two models differ). What am I missing? Ralf ______________________________________________ R-help@r-project.org 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.
