You could fit a linear model to original/predicted y values and get rsquared from that.
Chris On Mar 13, 2014 5:26 PM, Greg Snow <538...@gmail.com> wrote: > > Well if I had it and you asked nicely, then I would be happy to give > it to you. Oh, you mean the gls function, not GLS as my initials (my > parents are OLS and WLS, perhaps I was destined to regress), sorry. > > The gls function in the nlme package (is that the one that you are > asking about? or is there another gls function?) fits using maximum > likelihood (or restricted maximum likelihood) rather than looking at > sums of squares, so an adjusted r-squared is not a direct result like > in ordinary least squares. The idea of r-squared does not really > translate well to models beyond ordinary least squares (see > fortune(252), fortune(253), and fortune(254)), so adjusted r-squared > would not either. > > There are other measures of overall model fit that penalize or adjust > for the number of terms in the model, e.g. AIC and BIC, perhaps one of > those would be better for what you are trying to accomplish. > > One possibility would be to square the correlation between the > original y-values and the predicted y-values (y-hats) as an estimate > of r-squared, then apply the same adjustment > (http://en.wikipedia.org/wiki/Adjusted_R-squared#Adjusted_R2), but > there is no guarantee that it has the same effect for the generalized > model (might be an interesting project for a student to look at this > by simulation). I would suggest looking into AIC or BIC instead. > > On Thu, Mar 13, 2014 at 9:59 AM, Yuan, Rebecca > <rebecca.y...@bankofamerica.com> wrote: > > Hello, > > > > Although lm() gives a way to get the adjusted R squared by > > > > adjr2 <- summary(mdl)$adj.r.squared > > > > > > I cannot find a way to extract the adjusted R squared from gls(), any hint? > > > > Thanks, > > > > Rebecca > > > > ---------------------------------------------------------------------- > > This message, and any attachments, is for the intended...{{dropped:14}} > > ______________________________________________ > 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. ______________________________________________ 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.
