On Dec 15, 2011, at 8:35 AM, PtitBleu wrote:
Hello,
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))
and
summary(lm(data4$ny ~ data4$nx))
are very different (0.48 against 0.89).
Could someone explain me the reason?
Because you failed to do an adequate assessment of your data. Try this
ploting exercsie and I think you will see the reason for the
differences:
plot(data1$nx,data1$ny, col="red", xlim=range(c(data1$nx,data4$nx)),
ylim=range(c(data1$ny,data4$ny)) )
--
David.
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.
Thanks for your explainations.
Ptit Bleu.
--
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