I was referring to the height of the pdf. The 1st distribution is the simple uniform between 0 and 1, the second is also uniform, but on a discontinuous region. The first will generate numbers between 0 and 1 with equal probability, the second will be similar but only up to 0.99 with the very rare value between 999.99 and 1000.
-----Original Message----- From: Bogaso Christofer [mailto:bogaso.christo...@gmail.com] Sent: Sunday, May 29, 2011 12:15 AM To: Greg Snow; R-help@r-project.org Subject: RE: [R] Normality test Hi Greg, please forgive me as I could not understand one part of your wishful reply. You said: "distributions where one is uniform between 0 and 1 with height 1; the other also has height 1 between 0 and 0.99, but is also 1 between 999.99 and 1000, zero elsewhere. " Can you be more specific on this 2nd distribution? And what you mean to say as "height?" Thanks for your time. -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Greg Snow Sent: 29 May 2011 01:52 To: Robert Baer; Salil Sharma; R-help@r-project.org Subject: Re: [R] Normality test To build on Robert's suggestion (which is very good to begin with), you might consider using the vis.test function in the TeachingDemos package with the vt.qqnorm function. This will create the qq plot of your data along with several other qqplots of normal samples of the same size. If you cannot tell which of the plots is your data, then your data is probably close enough to normal for most practical purposes. It will give you a p-value based on your ability to distinguish your data from random normals if you need one. If you need more precision, then the most precise normality test is SnowsPenultimateNormalityTest also in TeachingDemos. However, the documentation for that function tends to be more useful than the function itself. If you really want to choose among the different normality tests in nortest (or elsewhere) then you should really investigate what assumptions they are making and what types of alternatives they are the most powerful for. Also decide on what types of non-normality you really care about, then use that to choose among them. Consider the 2 distributions where one is uniform between 0 and 1 with height 1; the other also has height 1 between 0 and 0.99, but is also 1 between 999.99 and 1000, zero elsewhere. Are these 2 distributions different in a meaningful way? They have very different mean and variance, but for most samples they will look the same (and if you throw out outliers they will look even more similar). The reason that different tests give different results is because they focus on different types of differences. -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Robert Baer Sent: Friday, May 27, 2011 5:28 PM To: Salil Sharma; R-help@r-project.org Subject: Re: [R] Normality test > I am writing to inquire about normality test given in nortest package. > I have a random data set consisting of 300 samples. I am curious about > which normality test in R would give me precise measurement, whether > data sample is following normal distribution. As p value in each test > is different in each test, if you could help me identifying a suitable > test in R for this medium size of data, it will be grateful. I am neither a statistician nor an expert on these types of tests, but I'm guessing that your are unlikely to get a good answer even from people with such qualifications as such judgments can only be made in the context of a specific problem. You have not provided us with such a problem (please read the posting guide). That admonishment aside, I typically start by using qqnorm() and qqline() to plot my data against the expected theoretical quantiles. If your data is perfectly normal, the points will fall right along the line. Skewness and deviations from normal by the tails produce very characteristic patterns in the plots which you can learn about by plotting some simulated data that is left-skewed, right-skewed, long tailed, or short tailed. I personally find this graphical feedback to be a much more useful way to understand my data than doing a single normality test that produces a p-value. based upon assumptions I may not be privy to For more, see the help by typing: ?qqnorm ?qqline Rob ------------------------------------------ Robert W. Baer, Ph.D. Professor of Physiology Kirksville College of Osteopathic Medicine A. T. Still University of Health Sciences 800 W. Jefferson St. Kirksville, MO 63501 660-626-2322 FAX 660-626-2965 ______________________________________________ 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. ______________________________________________ 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.
