[R] lm and R-squared (newbie)
ggrothendieck at gmail.com
Thu Dec 15 15:20:34 CET 2011
On Thu, Dec 15, 2011 at 8:35 AM, PtitBleu <ptit_bleu at yahoo.fr> wrote:
> I've two data.frames (data1 and data4), dec="." and sep=";".
> http://r.789695.n4.nabble.com/file/n4199964/data1.txt data1.txt
> http://r.789695.n4.nabble.com/file/n4199964/data4.txt data4.txt
> When I do
> plot(data1$nx,data1$ny, col="red")
> points(data4$nx,data4$ny, col="blue")
> , results seem very similar (at least to me) but the R-squared of
> summary(lm(data1$ny ~ data1$nx))
> summary(lm(data4$ny ~ data4$nx))
> are very different (0.48 against 0.89).
> Could someone explain me the reason?
> To be complete, I am looking for an simple indicator telling me if it is
> worthwhile to keep the values provided by lm. I thought that R-squared could
> do the job. For me, if R-squared is far from 1, the data are not good enough
> to perform a linear fit.
> It seems that I'm wrong.
The problem is the outliers. Try using a robust measure instead. If
we replace Pearson correlations with Spearman (rank) correlations they
are much closer:
> # R^2 based on Pearson correlations
> cor(fitted(lm(ny ~ nx, data4)), data4$ny)^2
> cor(fitted(lm(ny ~ nx, data1)), data1$ny)^2
> # R^2 based on Spearman (rank) correlations
> cor(fitted(lm(ny ~ nx, data4)), data4$ny, method = "spearman")^2
> cor(fitted(lm(ny ~ nx, data1)), data1$ny, method = "spearman")^2
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email: ggrothendieck at gmail.com
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