With Ifeffit-like, you can test these assumptions with an explicit chi^2_r, and indeed the answer does and must depend upon your actual individual error bars. The other factor here is how correlated your choice of parameters are which of course reaches that limit much faster.
:)
------------------------------------------------------------
Christopher Chantler, Professor, FAIP
Editor-in-Chief, Radiation Physics and Chemistry
Chair, International IUCr Commission on XAFS
President, International Radiation Physics Society
School of Physics, University of Melbourne
Parkville Victoria 3010 Australia
+61-3-83445437 FAX +61-3-93474783
chantler@unimelb.edu.au chantler@me.com
http://optics.ph.unimelb.edu.au/~chantler/xrayopt/xrayopt.html
http://optics.ph.unimelb.edu.au/~chantler/home.html
________________________________________
From: ifeffit-bounces@millenia.cars.aps.anl.gov [ifeffit-bounces@millenia.cars.aps.anl.gov] on behalf of ifeffit-request@millenia.cars.aps.anl.gov [ifeffit-request@millenia.cars.aps.anl.gov]
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Subject: Ifeffit Digest, Vol 153, Issue 30
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Today's Topics:
1. Re: Problem with Perl interpreter (Bruce Ravel)
2. Re: Hello (Matt Newville)
----------------------------------------------------------------------
Message: 1
Date: Mon, 16 Nov 2015 07:48:54 -0500
From: Bruce Ravel
The timeline of this problem was as follows: 1) Demeter was working fine; 2) One day I couldn't run any of the Demeter software - error message "Per interpreter has stopped working; 3) Reinstallation of Demether - the problem is still there; 4) Installation of perl (in other folder, not the same as Demeter) - the problem is still there; 5) Mail to mailing list; 6) Deinstallation of Demether and Perl; 7) Installation of Demeter. Unfortunately the problem is still there. I receive the same error message.
Wojciech,
Step 2 really suggests that this is a problem local to your computer.
In recent weeks, there have been a few reports of anti-virus software
blocking some component of perl or Demeter.
In the time period between steps 1 and 2, was AV updated on this
computer? Could you disable AV (as a test of course)? Or configure AV
to explicitly allow Demeter to run?
B
--
Bruce Ravel ------------------------------------ bravel@bnl.gov
National Institute of Standards and Technology
Synchrotron Science Group at NSLS-II
Building 535A
Upton NY, 11973
Homepage: http://bruceravel.github.io/home/
Software: https://github.com/bruceravel
Demeter: http://bruceravel.github.io/demeter/
------------------------------
Message: 2
Date: Mon, 16 Nov 2015 11:18:56 -0600
From: Matt Newville
Hi Bruce and Matt, Are there any obvious examples of where Sterns criteria is shown to be incorrect? Meaning that 2delkdelR/pi +2 has been shown to over predict the number of available independent parameters? I have often been asked this by students/users and failed to explain it well, except for taking of talking sources or error such as systematic, spline, etc. Thanks! -Riti
That's an excellent question. To be honest, I don't think this has been tested in great detail, with the main issue being "when can you truly tell that you've used too many parameters". We typically rely on "when adding a variable doesn't improve reduced chi-square" as that measure. But, the definition of reduced chi-square that we use includes a value for N_idp already, so we're more or less *imposing* the Stern rule. I think lots of us have observed cases where the Stern rule is definitely optimistic. And that might be understandable because the Stern rule doesn't include a separate measure of the noise in the data, or what the distribution of "the effect" of the parameters would be on the signal. That is, one can always increase the R range to 20 Angstroms, and so increase N_idp. But if you're trying to fit 25 variables for the part of the signal between 1.5 and 2.5 Ang, there is probably going to be very hard to get good measures of all 25 variables. The Stern rule sort of doesn't account for this, except in the sense of giving guidance. It would be interesting the revisit this with a better statistical treatments of information. There are, for example, statistical values like the Akaike Information criterion ( https://en.wikipedia.org/wiki/Akaike_information_criterion) and related values that might be useful for better understanding when one is using too many parameters. --Matt