[Ifeffit] Question about transform windows and statistical parameters

[Brandon Reese]

Hi Scott and Shelly,

Thanks for the replies.

Scott, I have read a bit about how ifeffit computes the error.   I have a
hard time keeping that information handy when I start seeing stuff like
this, but that was the source of my comment about it being related to
compared chi-square with different k-ranges.  Thanks for the refresher,
hopefully it sinks in a bit better this time!   I tried your suggestion with
epsilon and the chi-square values came out to be very similar values with
the different windows.  Does this mean that reporting reduced chi-square
values in a paper that compared several data sets would not be necessary
and/or appropriate?  Would setting a value for epsilon allow comparisons
across different k-ranges, different (but similar) data sets, or a
combination of the two using the chi-square parameter?

Shelly, thanks for the tip on appropriate dk values for the two windows.  I
would appreciate a copy of your book chapter.  Changing the dk value to 2
using the K-B window produced similar chi-square values compared to a
Hanning window with a dk=1.

In playing around with different windows and dk values my fit variables
generally stayed within the error bars, but the size of the error bars could
change more than a factor 2.  Does this mean that it would make sense to
find a window/dk that seems to "work" for a given group of data and stay
consistent when analyzing that data group?

Best Regards,
Brandon



On Wed, May 11, 2011 at 2:41 PM, Shelly Kelly <dr.sdkelly at gmail.com> wrote:

> Hi Brandon:
>
> I have a different idea on what might be happening. Take a look at the
> window function and the data in k-space for both window functions. The
> kaiser-bessel window works in a different way from the Hanning window. They
> emphasize different regions of the data differently. The sill part of the
> window is defined differently.  Usually use a dk = 1 or 2 A-1 with a Hanning
> window. Take a look at the kq data showing the signal q that is used in R
> for the fit.  The Kaiser-Bessel window works best with dk values of 3 to 4
> A-1. This results in a slowly giving more importance to data in the k-range.
> Again look at the kq data to see this effect.
>
> If you use a Kaiser-Bessel window with a small dk then the data used in the
> fit will extend more (as compared to Hanning) beyond the k-range set by kmin
> and kmax.  Same for the Hanning window, but with a large dk value as
> compared to K-B.
> So by simply changing the window without changing dk, you are changing the
> information that is Fourier transformed and that results in slightly
> different values and all the rest.
>
> If you need an example: I can send you my book chapter :).  It has an
> excellent section on FT and all the buttons that go with them.
>
> Kelly, S. D., Hesterberg, D. and Ravel, B. (2008). Analysis of soils and
> minerals using X-ray absorption spectroscopy. Methods of soil analysis, Part
> 5 -Mineralogical methods. Ulery, A. L. and Drees, L. R. Madison, WI, USA,
> Soil Science Society of America: 367-463.
>
> Cheers,
> Shelly
>
> On Wed, May 11, 2011 at 2:46 PM, Brandon Reese <bjreese at gmail.com> wrote:
>
>> Hello everybody,
>>
>> I am working on fitting some EXAFS of amorphous materials and have noticed
>> an odd (in my mind) behavior when changing transform windows.  I settled on
>> a fit using all three k-weights and the Hanning transform window obtaining
>> statistical parameters of R=0.0018 and chi_R=361.  I decided to change the
>> transform window to a Kaiser-Bessel to see what would happen.  The refined
>> parameters came out more or less the same, well within the error bars, with
>> the Hanning windows having slightly smaller error bars.  But my statistical
>> parameters changed significantly to R=0.0022 and chi_R=89.354.  It seems
>> that this large change may be related to why we can't use the chi_R
>> parameter to compare fits over different k-ranges, but I am not sure about
>> that.  Have other people seen this?  I would guess it means that when
>> looking for trends in different data sets, it is more important to be
>> consistent, rather than which specific window type is used.
>>
>> Thanks,
>> Brandon
>>
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>
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