It is not available for a reason. The correct way would be to use lm() instead, if possible. This function reports an R² in the summary. In the case of glm, and if you're absolutely sure about what you're doing, you can use one of the approximations that is used when looking at prediction only, realizing very well you can't possibly use R² to compare models with a different number of variables and realizing very well that the R² doesn't mean what you think it does when using a link function :
x <- 1:100 y <- 1:100 + rnorm(100) mod <- glm(y~x) # possibility 1 R2 <- cor(y,predict(mod))^2 # possibility 2 R2 <- 1 - (sum((y-predict(mod))^2)/sum((y-mean(y))^2)) In the case where you use a link function, you should work on the converted data : convert the values of y, and use predict(mod,type="link") for a correct estimate. Cheers Joris On Mon, Jun 21, 2010 at 12:00 AM, elaine kuo <elaine.kuo...@gmail.com> wrote: > Dear, > > I want to compute coefficient of determination (R-squared) to complement AIC > for model selection of > multivariable GLM. > > However, I found this is not a built-in function in glm. neither is it > available through reviewing the question in the R-help archive. > Please kindly help and thanks a lot. > > Elaine > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > -- Joris Meys Statistical consultant Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control tel : +32 9 264 59 87 joris.m...@ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php ______________________________________________ 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.
