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 <

[hidden email]> 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]]

>

> ______________________________________________

>

[hidden email] 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

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