On Sun, Jun 28, 2009 at 3:38 AM, Dieter Menne<dieter.me...@menne-biomed.de> wrote:
> It seems odd to me that dropping the intercept > would cause the R^2 and F stats to rise so dramatically, and the p > value to consequently drop so much. In my implementation, I get the > same beta1 and beta2, and the R2 I compute using the > > Removing the intercept can harm your sanity. See > > http://markmail.org/message/q67jf7uaig7d4tkm > > for an example. I read the paper and the example so thanks for sending those along. The paper made some good arguments from a modeling perspective why one should keep the intercept -- the most convincing to me is that you would like the modeling to be robust to a location and scale transformation. But my question was more numerical: in particular, the R^2 of the model should be equal to the square of the correlation between the fit values and the actual values. It is with the intercept and is not w/o it, as my code example shows. Am I correct in assuming these should always be the same, and if they are not, does it reflect a bug in R or perhaps a numerical instability? You also wrote in your post "There are reasons why the standard textbooks...". I read the reasons Venables addressed in his "Exegeses", but none of these seem to address my particular concern. Can you elaborate on these or provide additional links ? Thanks! JDH ______________________________________________ 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.
