[Ifeffit] Different R-factor values

Fri Jan 25 15:24:42 CST 2013

Thanks again everyone,

Thanks for the clarity Bruce.  Yes guess it was a poor question. The 
purely numerical aspect of R in a single data set fit is certainly not 
lost on me but reviewing the analyses I want to have some concrete 
arguments against the fits instead of just, 'your method and fits are 
indefensible'.  There are several problems with the fits outside any 
statistical metrics...    in fact I would guess that this was the unique 
case where the user depended exclusively upon R instead of the "red line 
matching the blue line".   (see attached)

- Chris

********************************
Christopher J. Patridge, PhD
NRC Post Doctoral Research Associate
Naval Research Laboratory
Washington, DC 20375
Cell: 315-529-0501

On 1/25/2013 3:32 PM, Bruce Ravel wrote:
> Chris et al,
>
> Sorry I didn't pipe up earlier.  I haven't had a good chance to sit
> down and follow this discussion until this afternoon.
>
> I'll start with the practical issue.  Recently someone requested
> per-data-set R factors in Artemis.  Being a perfectly fine request, I
> sat down to implement it.  Since fits in Artemis are usually done with
> multiple k-weights, it wasn't clear to me how to display the
> information in the clearest manner.
>
> The "overall" R factor, the one that Ifeffit reports after the fit
> finishes, includes all the data and all the k-weights used in the fit.
> That is certainly a useful number in that it summarizes the closeness
> of the fit in the aggregate.
>
> As long as I was breaking down the R-factors by data set, I figured it
> would be useful to do so by k-weight also.  I could imagine a scenario
> where knowing how a particular data set and a particular k-weight
> contributed to the overall closeness of the fit.  That should explain
> the why of what you find in Artemis' log file.
>
> My intent is to use the same formula for R-factor as in the Ifeffit
> reference manual.  If you do a single data set, single k-weight fit,
> theoverall R-factor and the per data set R-factor at the k-weight (all
> three are reported regardless) should be the same.  It is possible
> that is not well enought tested.
>
> Matt's point about Larch being the superior tool for user-specified
> R-factors is certainly true, although few GUI users would avail
> themselves of that.
>
> If some R-factor other than one reported by Ifeffit (or, soon, the one
> reported by default by Larch) is needed, that would be a legitamate
> request.  If something sophisticated or flexible is needed, that too
> can be put into the GUI.
>
> As for the actual question -- how to "decide" between the R-factors --
> well, my take is that that's not a well posed question.  The R factor
> is not reduced chi-square.  It does not measure *goodness*, it only
> measures *closeness* of fit.  The term "goodness" means something in a
> statistical context.  An R-factor is some kind of percentage misfit
> without any consideration of how the information content of the actual
> data ensemble was used.  In short, the R-factor is a numerical value
> expressing how closely the red line overplots the blue line in the
> plot made after Artemis finishes her fit.  Thus, the overall R-factor
> expresses how closely all the red lines together overplot all the blue
> lines.  The R-factors broken out by data set and k-weight express how
> closely a particular red line overplots a particular blue line.
>
> HTH,
> B
>
>
> On Friday, January 25, 2013 01:11:22 PM Christopher Patridge wrote:
>> Thank you for the discussion Matt and Jason,
>>
>> My main objective was to decide between the two different reported
>> R-factors in some older Artemis fit file logs.  I suspect that the
>> analysis was prematurely completed because the user found small R-factor
>> values printed out along with the other fit statistics near the
>> beginning of the fit log.  Scrolling down the log file to the area which
>> gives;
>>
>> R-factor for this data set = ?
>> k1,k2,k3 weightings R-factors = ?
>>
>> This R-factor is the average R-factor of the k-weights and much larger
>> say,  0.01 above vs. 0.07-0.08 making a typical "good fit" to a single
>> data set into a rather questionable one.
>>
>> Looking at more current fit logs from Demeter (attached, just a quick
>> example), the R-factor which is printed near the beginning of the fit
>> file is equal to the average R-factor for the k-weightings.  Therefore
>> the value found in the earlier Artemis file logs must have been faulty
>> or buggy as was said so one should not rely on that value to evaluate
>> the fits.  Sorry for any confusion but this is all in the name of
>> weeding out good/bad analysis....
>>
>> Thanks again,
>>
>> Chris
>>
>> ********************************
>> Christopher J. Patridge, PhD
>> NRC Post Doctoral Research Associate
>> Naval Research Laboratory
>> Washington, DC 20375
>> Cell: 315-529-0501
>>
>> On 1/25/2013 12:04 PM, Matt Newville wrote:
>>> Hi Jason, Chris,
>>>
>>> On Fri, Jan 25, 2013 at 10:01 AM, Jason Gaudet <jason.r.gaudet at gmail.com>
> wrote:
>>>> Hi Chris,
>>>>
>>>> Might be helpful also to link to the archived thread you're talking
>>>> about.
>>>>
>>>> http://millenia.cars.aps.anl.gov/pipermail/ifeffit/2006-June/007048.html
>>>>
>>>> Bruce might have to correct me on this, but if I remember right there
>>>> were
>>>> individual-data-set R-factor and chi-square calculations at some point,
>>>> which come not from IFEFFIT but from Bruce's own post-fit calculations,
>>>> and
>>>> these eventually were found to be pretty buggy and were dropped.
>>>>
>>>> I don't understand what "the average over the k weights" R factor is;
>>>> analyzing the same data set with multiple k weights (which is pretty
>>>> typical) still means a single fit result and a single statistical output
>>>> in
>>>> IFEFFIT, as far back as I can remember, anyhow.  The discussion about
>>>> multiple R-factors is for when you're simultaneously fitting multiple
>>>> data
>>>> sets (i.e. trying to fit a couple different data sets to some shared or
>>>> partially shared set of guess variables).
>>>>
>>>> I think the overall residuals and chi-square are the more statistically
>>>> meaningful values, as they are actually calculated by the same algorithm
>>>> used to determine the guess variables - they're the quantities IFEFFIT is
>>>> attempting to reduce.  I don't believe I've reported the per-data-set
>>>> residuals in my final results, as I only treated it as an internal check
>>>> for myself.  (It would be nice to have again, though...)
>>>>
>>>> -Jason
>>> I can understand the desire for "per data set" R-factors.  I think
>>> there are a few reasons why this hasn't been done so far.  First, The
>>> main purpose of chi-square and R-factor are to be simple, well-defined
>>> statistics that can be used to compare different fits.   In the case
>>> of R-factor,  the actual value can also be readily interpreted and so
>>> mapped to "that's a good fit" and "that's a poor fit" more easily
>>> (even if still imperfect).   Second, it would be a slight technical
>>> challenge for Ifeffit to make these different statistics and decide
>>> what to call them.     Third, this is  really asking for information
>>> on different portions of the fit, and it's not necessarily obvious how
>>> to break the whole into parts.  OK, for fitting multiple data sets, it
>>> might *seem* obvious how to break the whole.
>>>
>>> But, well, fitting with multiple k-weights *is* fitting different
>>> data.  Also, multiple-data-set fits can mix fits in different fit
>>> spaces, with different k-weights, and so on.  Should the chi-squared
>>> and R-factors be broken up for different k-weights too?  Perhaps they
>>> should.  You can different weights to different data sets in a fit,
>>> but how to best do this can quickly become a field of study on its
>>> own.  I guess that's not a valid reason to not report these....
>>>
>>> So, again, I think it's reasonable to ask for per-data-set and/or
>>> per-k-weight statistics, but not necessarily obvious what to report
>>> here.  For example, you might also want to use other partial
>>> sums-of-squares (based on k- or R-range, for example) to see where a
>>> fit was better and worse.    Of course, you can calculate any of the
>>> partial sums and R-factors yourself.  This isn't so obvious with
>>> Artemis or DArtemis, but it is possible.  It's  much easier to do
>>> yourself and implement for others with larch than doing it in Ifeffit
>>> or Artemis.  Patches welcome for this and/or any other advanced
>>> statistical analyses.
>>>
>>> Better visualizations of the fit and/or mis-fit might be useful to
>>> think about too.
>>>
>>> --Matt
>>> _______________________________________________
>>> Ifeffit mailing list
>>> Ifeffit at millenia.cars.aps.anl.gov
>>> http://millenia.cars.aps.anl.gov/mailman/listinfo/ifeffit
>

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