Hi Bruce,

thanks a lot for the very  fast response.
Maybe you are right and I did not well on normalization but I tried to be consistent as much as possible. Find attached a demo Athena project. The fit the box "to weight to sum to 1" was checked. But you are right it did still not sum up to 1. But why? Or did I something wrong?

Thanks,
PJ


Am 18.08.2014 16:48, wrote Bruce Ravel:
On 08/18/2014 10:12 AM, Jens Kruse wrote:
I doing LCF on my XANES data. I wonder if someone can tell me the
meaning of the values in parentheses following the weight value in the
Fit results window?. I looked into the respective document section of
the Manuel but could not find the answer, yet.

If you are asking the simplest interpretation of your question: they
are the evaluated uncertainties, also known as error bars.

If you are asking what kind of uncertainties: they are the diagonal
elements of the covariance matrix scaled by the square root of reduced
chi-square.  Thus they are 1sigma uncertainties, presuming that you
trust that the only mistake made in evaluation of statistic is
evaluation of epsilon (which Athena does not attempt for an linear
combination fit).  The scaling of the diagonal elements yields
defensible 1sigma error bars with the assumption that each fit is a
good fit -- an assumption that it is up to you to defend.

As I will discuss below, you should not trust that evaluation of
epsilon is your only problem.

I am asking because I did a Fitting with over 20 standards (STD) to find
all combination of max 4 standards which describe my unknown sample
spectra. This results in a lot of combination with similar
chi-square(reduced) and R-values. I know a relative changes between
these values are more probably meaningful than absolute values.
A lot of the possible combinations provided by after the fit yieled
combinations with at lest on STD having a weight of 0.000. (Fit 1). I
thought that this would mean, that 3 standards are sufficient to
describe the system. However, repeating the fit with only the three
standards with weight > 0.0 yielded a complete different proportions
(Fit 2).  How can this be explain and how should I deal with
combinations or standards having  a weight of 0.000?

You seem to have allowed the fit to run without the constraint that
the weights add up to 1.  The reason Athena allows you to lift this
constraint is to accommodate the situation where you have some
systematic uncertainty in how your spectra are normalized.

To say that another way, if you could somehow know that you have done
a perfect job normalizing your data and all your standards, it would
not be necessary -- indeed, it would be a mistake -- to lift the
constraint that the weights add to 1.

In you case, it is clear (or at least as clear as it could be, given
that I have not seen your data) that you have issues with
normalization.  Fit #1 has weights that sum to 1.137.  Fit #2 has
weights that sum to 1.093.  Thus, your inconsistencies in
normalization have introduced systematic error into your analysis at
the level of about 10%.

In fit #1, standard 20 accounts for about 10% of the spectral weight.
That is, it's contribution to the fit is at the level of the
systematic uncertainty in your model.  When you remove standard 27,
the fit finds a different way to resolve the model.  I would guess --
although I have no way of proving this in the absence of data -- that
the contours of the surface in the space of your fitting parameters is
altered by the presence of a second standard whose contribution is
hard to distinguish from the effect of error in normalization.

So my conclusiona (again, not authoritative since I have not seen the
data) are:

  1. your data likely contains only standards 3 and 7, it likely does
     not contain any of 20 or 27

  2. you seem not to have done a very good job normalizing your data
     in a consistent manner

  3. your uncertainties are at about the 10% level, when you include
     the systematic effect of inconsistent normalization -- the purely
     statistical uncertainties represented by the reported error bars
     are much smaller, thus your error budget is dominated by
     systematics


HTH,
B



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