I would use the vis.test function along with vt.qqnorm (both in TeachingDemos package). This will create several plots, one of which is your data, the rest are simulated normals with the same mean and standard deviation. If you can tell which plot stands out (and it is your real data) then that suggests that the data is not normal. If you cannot tell which plot is the real data then that suggests that your data is close enough to normal.
-- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.s...@imail.org 801.408.8111 > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-bounces@r- > project.org] On Behalf Of Matevž Pavlic > Sent: Saturday, April 30, 2011 11:28 AM > To: r-help@r-project.org > Subject: [R] QQ plot for normality testing > > Hi all, > > > > I am trying to test wheater the distribution of my samples is normal > with QQ plot. > > > > I have a values of water content in clays in around few hundred > samples. Is the code : > > > > qqnorm(w) #w being water content > > qqline(w) > > > > > > sufficient? > > > > How do I know when I get the plots which distribution is normal and > which is not? > > > > Thanks, m > > > [[alternative HTML version deleted]] > > ______________________________________________ > 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.
