Spencer Graves:
> Bates' condemnation of R^2 has merit, but I would not go as far as
> he did in the comment cited below (dated 13 Aug 2000). A standard
> definition of R^2 is as follows:
>
> R^2 = (1 - var(prediction error) / var(obs)).
>
> I can name several different ways of getting a negative R^2 in
> this case. When that happens, it says the model is worse than useless,
> and you would be better off using the training set mean.
>
> If I have an audience who wants an R^2 in an application where it
> is not clear what it even means, I try to briefly explain some of the
> difficulties while asking what question they are trying to solve using
> R^2. Their answers will help me make a recommendation, which may
> include selecting which of the possible generalizations of R^2 to use.
I would like to recommend the following two articles on R²:
Model Comparisons and R²
Richard Anderson-Sprecher
The American Statistician, Vol. 48, No. 2. (May, 1994), pp. 113-117.
http://www.jstor.org/stable/2684259
Cautionary Note about R²
Tarald O. Kvalseth
The American Statistician, Vol. 39, No. 4, (Nov., 1985), pp. 279-285.
http://www.jstor.org/stable/2683704
--
Karl Ove Hufthammer
______________________________________________
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.
