From: Greg Snow <Greg.Snow_at_imail.org>

Date: Thu, 24 Jun 2010 10:07:17 -0600

Date: Thu, 24 Jun 2010 10:07:17 -0600

If you want a more objective eye-ball test, look at:

Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,

D.F and Wickham, H. (2009) Statistical Inference for exploratory data analysis and model diagnostics Phil. Trans. R. Soc. A 2009 367, 4361-4383 doi: 10.1098/rsta.2009.0120

One implementation of this procedure is the vis.test function in the TeachingDemos package.

-- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.snow_at_imail.org 801.408.8111Received on Thu 24 Jun 2010 - 16:09:00 GMT

> -----Original Message-----

> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-> project.org] On Behalf Of Ralf B> Sent: Wednesday, June 23, 2010 8:53 PM> To: Robert A LaBudde> Cc: r-help_at_r-project.org> Subject: Re: [R] Comparing distributions>> 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 <ral_at_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_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.> >> > ================================================================> > Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: ral_at_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_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.

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