[Ifeffit] running ifeffit under 64-bit windows7

Matt Newville newville at cars.uchicago.edu
Sat Mar 10 14:34:48 CST 2012


Hi Kicaj,


OK, sorry I misunderstood then.   And, despite my laziness, I think
that Sameh is right that it's probably time for a more complete
update.... Such a thing should probably feature Bruce's newer codes of
course.

--Matt

On Sat, Mar 10, 2012 at 10:16 AM, Matt Newville
<newville at cars.uchicago.edu> wrote:
> Hi Kicaj,
>
> 2012/3/10 "Dr. Dariusz A. Zając" <kicaj at ifj.edu.pl>:
>> Hi,
>> maybe these below clarify a little bit the problem, but the problem sounds
>> very intriguing
>> http://millenia.cars.aps.anl.gov/pipermail/ifeffit/2004-July/005729.html
>> http://millenia.cars.aps.anl.gov/pipermail/ifeffit/2005-October/006613.html
>> http://cars9.uchicago.edu/ifeffit/FAQ/FeffitModeling
>>
>> I am waiting also for the answer from authors
>
> I would have said these questions have been answered, but maybe I
> misunderstand... What is the question you are waiting to be answered?
>
> All of chi-square, reduced chi-square, and R factor express the sum of
> squares of the residual (data-model) after a fit has finished.  The
> difference between these statistics is how they are scaled.
>
> In particular, chi-square is scaled by the estimated error in the
> data. If you look at a (naive?) introduction to statistics, you will
> see it stated that this should be approximately the number of degrees
> of freedom in the fit.  Reduced chi-square is then defined to be
> chi-squared / (the number of degrees of freedom in the fit), so that
> it should be 1 (according to statistics 101).   This presupposes a
> couple of things that aren't very true for us:
>  a) it assumes we actually know the uncertainty in the data -- the
> automated estimate in ifefit is pretty simplistic.
>  b) it assumes our model of the data is much better than that data
> uncertainty. Many people describe these as "systematic errors" and
> include alll sorts of data processing artifacts as well as errors in
> the Feff calculations.
>
> For us, reduced chi-square is almost always >> 1, unless the data is very noisy.
>
> R-factor scales the fit residual by the magnitude of the data itself,
> for some estimate of "fractional misfit".   This gives a convenient
> measure that is independent of the scale of the data (and so also
> independent of data k-range and k-weight for fits in R-space), and can
> more easily be made into a "rule of thumb", say "If R-factor > 0.05,
> then you should  be wary of the results".
>
> Hope that helps,
>
> --Matt



-- 
--Matt Newville <newville at cars.uchicago.edu> 630-252-0431




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