[Ifeffit] doubt on number of variables

[Jesús Eduardo Vega Castillo]

Thanks Bruce for your answer,

Just for clarifying, this is not what I have been doing. I just was asked
to add new independent variables (individual DW factors for each path)
while I am already at the limit of independent points.

I have been using a number of parameter always lower than the number of
independent points. I have also been managing high correlations by not
varying two strongly correlated parameters at the time. But at the end I do
a final fit setting to guess all the parameters, no matter the
correlations, in order to obtain a "true" reduced Chi2 to report which
includes all parameters. Do you consider this procedure right?








2016-05-26 10:36 GMT-03:00 Bruce Ravel <bravel en bnl.gov>:

> On 05/26/2016 09:17 AM, Jesús Eduardo Vega Castillo wrote:
>
>> As far as I understand the number of variables refers to those
>> parameters which have been "guessed" in the fit and it is limited by the
>> number of independent points.
>>
>
> Correct.
>
> If I have a group of variables but only set to guess a few of them while
>> fixing the others, then fix some of the obtained values and guess other
>> variables; I might include a large number of parameters in the resulting
>> fit (even more than the independent points) without increasing the
>> Number of variables of each individual fit. This also would keep reduced
>> chi2 from increasing too much.
>>
>> Would this be cheating?
>>
>
> The answer, almost certainly, is "yes".
>
> The total number of parameters included is always restricted by the
>> Number of independent points?
>>
>
> I'm going with this one ...
>
> or
>> Can I include all the parameters I want as long as I set to guess no
>> more that the number of independent points at the time?
>>
>
> ... and I really don't like this one.
>
>
> The thing you seem not to be considering is that parameters have
> correlations.  The correlations between parameters are an important part of
> the assessment of uncertainty.
>
> If you artificially suppress correlations between parameters in the way
> that you are describing, it is unlikely that the fitted results and their
> uncertainties would be defensible.
>
> While it is possible to probe correlations between parameters in a
> defensible manner by selectively setting and guessing parameters in a
> lengthy sequence of fits, your brief description does not give me
> confidence that that is what you are doing.
>
> The trick to EXAFS analysis (and any non-linear minimization analysis) is
> to defensibly get what information you can from the data.  Rarely do we get
> everything we want.  In those situations where we aspire to  more than our
> data can provide, it's important to remember that something is better than
> nothing.
>
> B
>
> --
>  Bruce Ravel  ------------------------------------ bravel en 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/
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