Glad to hear it now works for you. But speaking more generally, note that R-squared is the squared correlation between the predicted Y and actual Y values. E.g. lmout <- lm(y ~ x) print(cor(lmout$fitted.values,y)^2) One can use this in any regression setting, even machine learning methods. Norm
issue with Rcmdr
2 messages · Norm Matloff, Bert Gunter
... and even more generally, is generally misleading. ;-) (search "problems with R^2" or similar for why). Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, Jan 8, 2020 at 9:37 AM Norm Matloff <nsmatloff at ucdavis.edu> wrote:
Glad to hear it now works for you. But speaking more generally, note that R-squared is the squared correlation between the predicted Y and actual Y values. E.g. lmout <- lm(y ~ x) print(cor(lmout$fitted.values,y)^2) One can use this in any regression setting, even machine learning methods. Norm
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