A qqplot would indeed help. ?ks.test for more formal testing, but be aware: You should also think about what you call similar distributions. See following example :
set.seed(12345) x1 <- c(rnorm(100),rnorm(150,3.3,0.7)) x2 <- c(rnorm(140,1,1.2),rnorm(110,3.3,0.6)) x3 <- c(rnorm(140,2,1.2),rnorm(110,4.3,0.6)) d1 <-density(x1) d2 <- density(x2) d3 <- density(x3) xlim <- 1.2*c(min(x1,x2,x3),max(x1,x2,x3)) ylim <- 1.2*c(0,max(d1$y,d2$y,d3$y)) op <- par(mfrow=c(1,3)) plot(d1,xlim=xlim,ylim=ylim) lines(d2,col="red") lines(d3,col="blue") qqplot(x1,x2) qqplot(x2,x3) par(op) # formal testing ks.test(x1,x2) ks.test(x2,x3) # relocate x3 x3b <- x3 - mean(x3-x2) x3c <- x3 - mean(x3-x1) # formal testing ks.test(x2,x3b) ks.test(x1,x3c) # test location t.test(x2-x1) t.test(x3-x2) t.test(x3-x1) Cheers Joris On Wed, Jun 23, 2010 at 9:33 PM, Ralf B <ralf.bie...@gmail.com> wrote: > I am trying to do something in R and would appreciate a push into the > right direction. I hope some of you experts can help. > > I have two distributions obtrained from 10000 datapoints each (about > 10000 datapoints each, non-normal with multi-model shape (when > eye-balling densities) but other then that I know little about its > distribution). When plotting the two distributions together I can see > that the two densities are alike with a certain distance to each other > (e.g. 50 units on the X axis). I tried to plot a simplified picture of > the density plot below: > > > > > | > | * > | * * > | * + * > | * + + * > | * + * + + * > | * +* + * + + * > | * + * + +* > | * + +* > | * + +* > | * + + * > | * + + * > |___________________________________________________________________ > > > What I would like to do is to formally test their similarity or > otherwise measure it more reliably than just showing and discussing a > plot. Is there a general approach other then using a Mann-Whitney test > which is very strict and seems to assume a perfect match. Is there a > test that takes in a certain 'band' (e.g. 50,100, 150 units on X) or > are there any other similarity measures that could give me a statistic > about how close these two distributions are to each other ? All I can > say from eye-balling is that they seem to follow each other and it > appears that one distribution is shifted by a amount from the other. > Any ideas? > > Ralf > > ______________________________________________ > 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. > -- 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.
