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.
