Hi all, I am completely new to R and my knowledge of statistics is quite small so I hope you can help my. I have three dimensional point data which represents (and this is what I do not know for sure) a normal distribution. Now I want to test if this is true or not and as I can remember from statistics lessons I can use Chi-Square test for distribution test. BUT: I have realy no idea how to do this with R and additionally if my assumptions are correct and if this is possible with R at all. Thank you very much in advance for any answer. Markus
Markus Mehrwald wrote:> Hi all, > > I am completely new to R and my knowledge of statistics is quite small > so I hope you can help my. > I have three dimensional point data which represents (and this is what I > do not know for sure) a normal distribution. Now I want to test if this > is true or not and as I can remember from statistics lessons I can use > Chi-Square test for distribution test. BUT: I have realy no idea how to > do this with R and additionally if my assumptions are correct and if > this is possible with R at all. > > Thank you very much in advance for any answer. > MarkusSee ?shapiro.test or ?ks.test HTH, Joh
On Tue, Nov 17, 2009 at 11:17 AM, Markus Mehrwald <mehrwald at ira.uka.de> wrote:> Hi all, > > I am completely new to R and my knowledge of statistics is quite small so I > hope you can help my. > I have three dimensional point data which represents (and this is what I do > not know for sure) a normal distribution. Now I want to test if this is trueI suppose you want to say you have a sample of three-dim data, say represented be vectors x1,x2,x3, and your question is if this data (x1|_1,x2_1, x3_1),...,(x1_n,x2_n, x3_n) are generated by a three-dim multinormal distribution. That is very simple, a very good test is to simply say "reject". I have never seen three-dim data which are truly multinormal. So a better question is to ask if amultinormal distribution can be an acceptable approximation, but then we need to know what is your purpose of analysis! If you are interested in extremes or extrere quantiles, then a normal approx is never safe. If you want a statistical test, then a multivariate extension of shapiro-wilk is in install.packages("mvnormtest", dep=TRUE) library(mvnormtest) ?mshapiro.test kjetil> or not and as I can remember from statistics lessons I can use Chi-Square > test for distribution test. BUT: I have realy no idea how to do this with R > and additionally if my assumptions are correct and if this is possible with > R at all. > > Thank you very much in advance for any answer. > Markus > > ______________________________________________ > R-help at 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. >
This is probably an even more basic question but shapiro.test return both the statistic (w) and the significance (pw) of the statistic. For this test the null-hypothesis is that the distirbution is not normal so very small values of pw would mean that there is very little chance that the distiribution is not normal. Correct? Thank you. Kevin ---- Johannes Graumann <johannes_graumann at web.de> wrote:> Markus Mehrwald wrote: > > > Hi all, > > > > I am completely new to R and my knowledge of statistics is quite small > > so I hope you can help my. > > I have three dimensional point data which represents (and this is what I > > do not know for sure) a normal distribution. Now I want to test if this > > is true or not and as I can remember from statistics lessons I can use > > Chi-Square test for distribution test. BUT: I have realy no idea how to > > do this with R and additionally if my assumptions are correct and if > > this is possible with R at all. > > > > Thank you very much in advance for any answer. > > Markus > > See > ?shapiro.test > or > ?ks.test > > HTH, Joh > > ______________________________________________ > R-help at 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.
Thank you for all the answers! Kjetil, I am not sure if we are talking about the same thing. I only have a two dimensional normal distribution which leads to three dimensional data. You mean with "reject" I should not do such a test? My data files contain about 260000 points which I can reduce to the half. The data is created through a sum of two dim. Gaussian profiles (or just one). It is easy to fit a 2 dim. Gaussian function but this does not take material properties into account so I cannot be sure that the points are realy normal distributed. What I want to do with that is to proof that the model (at least one 2D Gaussian function) is working correctly or if I have to think of a different one. Regards, Markus Kjetil Halvorsen schrieb:> On Tue, Nov 17, 2009 at 11:17 AM, Markus Mehrwald <mehrwald at ira.uka.de> wrote: >> Hi all, >> >> I am completely new to R and my knowledge of statistics is quite small so I >> hope you can help my. >> I have three dimensional point data which represents (and this is what I do >> not know for sure) a normal distribution. Now I want to test if this is true > > I suppose you want to say you have a sample of three-dim data, say > represented be vectors x1,x2,x3, > and your question is if this data (x1|_1,x2_1, x3_1),...,(x1_n,x2_n, x3_n) > are generated by a three-dim multinormal distribution. That is very > simple, a very good > test is to simply say "reject". I have never seen three-dim data > which are truly > multinormal. So a better question is to ask if amultinormal > distribution can be an acceptable > approximation, but then we need to know what is your purpose of > analysis! If you are interested in > extremes or extrere quantiles, then a normal approx is never safe. > > If you want a statistical test, then a multivariate extension of > shapiro-wilk is in > install.packages("mvnormtest", dep=TRUE) > library(mvnormtest) > ?mshapiro.test > > kjetil > > >> or not and as I can remember from statistics lessons I can use Chi-Square >> test for distribution test. BUT: I have realy no idea how to do this with R >> and additionally if my assumptions are correct and if this is possible with >> R at all. >> >> Thank you very much in advance for any answer. >> Markus >> >> ______________________________________________ >> R-help at 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. >> >-- Dipl.-Inform. Med. Markus Mehrwald Institut f?r Prozessrechentechnik, Automation und Robotik Medizin-Gruppe Universit?t Karlsruhe (TH) Geb?ude 40.28, Zimmer 110 Engler-Bunte-Ring 8 76131 Karlsruhe Fon: +49 (721) 608-7113 Fax: +49 (721) 608-7141