Sorry, missed the two variable thing. Go with the lm solution then, and you can tweak the plot yourself (the confidence intervals are easily obtained via predict(lm.object, interval="prediction") ). The function qq.plot uses robust regression, but in your case normal regression will do.
Regarding the shapes : this just indicates both tails are shorter than expected, so you have a kurtosis greater than 3 (or positive, depending whether you do the correction or not) Cheers Joris On Fri, Jun 25, 2010 at 4:10 AM, Ralf B <ralf.bie...@gmail.com> wrote: > Short rep: I have two distributions, data and data2; each build from > about 3 million data points; they appear similar when looking at > densities and histograms. I plotted qqplots for further eye-balling: > > qqplot(data, data2, xlab = "1", ylab = "2") > > and get an almost perfect diagonal line which means they are in fact > very alike. Now I tried to check normality using qqnorm -- and I think > I am doing something wrong here: > > qqnorm(data, main = "Q-Q normality plot for 1") > qqnorm(data2, main = "Q-Q normality plot for 2") > > I am getting perfect S-shaped curves (??) for both distributions. Am I > something missing here? > > | > | * * * * > | * > | * > | * > | * > | * > | * > | * * * > |--------------------------------------------- > > Thanks, Ralf > -- Joris Meys Statistical consultant Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control tel : +32 9 264 59 87 joris.m...@ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php ______________________________________________ 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.