[Ifeffit] Chi in arthemis

Matthew mamarcus at lbl.gov
Tue Aug 18 23:06:40 CDT 2009

OK, I feel I have to weigh in.  I'm on a microprobe line where sample motions contribute to noise and I rarely find that the noise 
quality  of
the EXAFS signal, as measured by running a high-order polynomial through the data and looking at the residuals, matches the number 
of counts per point.
Also, if you are using an analog counter like an ion chamber, then you can't measure the true number of detected quanta, so you 
can't get the shot-noise
limit.  Further, there will be systematics like background-subtraction artifacts which will act as other than white noise.  For all 
these reasons, I think
that an attempt to use a literal chi-squared isn't going to succeed.  I don't think I've ever seen anyone report the true noise 
quality of their data, anyway.
Occasionally, someone might report the number of counts/point, but as I said, that's an upper limit to the noise quality.  What is 
more intuitive, though
less rigorous to report, is the R value.
----- Original Message ----- 
From: "Scott Calvin" <SCalvin at slc.edu>
To: "XAFS Analysis using Ifeffit" <ifeffit at millenia.cars.aps.anl.gov>
Sent: Tuesday, August 18, 2009 8:30 PM
Subject: Re: [Ifeffit] Chi in arthemis

> Matt,
> Is this the most recent IXAS report on error reporting standards?
> http://www.i-x-s.org/OLD/subcommittee_reports/sc/err-rep.pdf
> It uses a rather expansive definition of epsilon, which explicitly
> includes "imperfect" ab initio standards such as FEFF calculations. It
> indicates that statistical methods such as that used by ifeffit for
> estimating measurement error yields a lower limit for epsilon, and
> thus an overestimate of chi square.
> So I think my statement and yours are entirely compatible.
> As far as what should be reported, I do deviate from the IXAS
> recommendations by not reporting chi-square. Of course, I tend to work
> in circumstances where the signal-to-noise ratio is very high, and
> thus the statistical uncertainties make a very small contribution to
> the overall measurement error. In such cases I have become convinced
> that the R-factor alone provides as much meaningful information as the
> chi-square values, and that in fact the chi-square values can be
> confusing when listed for fits on different data. For those working
> with dilute samples, on the other hand, I can see that chi-square
> might be a meaningful quantity.
> At any rate, I strongly agree that the decision of which measurements
> of quality of fit to produce should not be dependent on what "looks
> good"! That would be bad science. The decision of what figures of
> merit to present should be made a priori.
> --Scott Calvin
> Sarah Lawrence College
> On Aug 18, 2009, at 10:40 PM, Matt Newville wrote:
>> Having a "reasonable R-factor" of a few percent misfit and a reduced
>> chi-square of  ~100 means the misfit is much larger than the estimated
>> uncertainty in the data.  This is not at all unusual.   It does not
>> necessarily  mean (as Scott implies) that this is because the
>> uncertainty in data is unreasonably low, but can also mean that there
>> are systematic problems with the FEFF calculations that do not account
>> for the data as accurately as it can be measured.   For most "real"
>> data, it is likely that both errors FEFF and a slightly low estimate
>> for the uncertainty in the data contribute to making reduced
>> chi-square much larger than 1.
>> And, yes, the community-endorsed recommendation is to report either
>> chi-square or reduced chi-square as well as an R-factor.  I think some
>> referees might find it a little deceptive to report  R-factor because
>> it is "acceptably small" but not reduced chi-square because it is "too
>> big".
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