The diagram only serves as a rough example to give you an idea. To be more precise I would like to give more detail: The data represents movements from two types of pointing device (e.g. mouse, pointer, ) along an axis. The data has diffreent parameters -- such as different pointing devices, different axis, split by different experiment conditions etc. but the problem is always the same: I would like find out if their distributions correlate and would like to have some kind of 'objective' (Yes, I know -- nothing is objective -- but eye-balling isn't either.) measure, test, etc. These would be accompanied by Q-Q plots and density plots to get a general feeling of what is going on and become part of the discussion. I don't expect a solution from here, but perhaps a general direction where I could find my kind of problem being understood.
Ralf On Wed, Jun 23, 2010 at 10:07 PM, Robert A LaBudde <r...@lcfltd.com> wrote: > Your "*" curve apparently dominates your "+" curve. > > If they have the same total number of data each, as you say, they both > cannot sum to the same value (e.g., N = 10000 or 1.000). > > So there is something going on that you aren't mentioning. > > Try comparing CDFs instead of pdfs. > > At 03:33 PM 6/23/2010, Ralf B 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. > > ================================================================ > Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: r...@lcfltd.com > Least Cost Formulations, Ltd. URL: http://lcfltd.com/ > 824 Timberlake Drive Tel: 757-467-0954 > Virginia Beach, VA 23464-3239 Fax: 757-467-2947 > > "Vere scire est per causas scire" > ================================================================ > > ______________________________________________ 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.
