[Ifeffit] Another R-factor question

[Matt Newville]

Hi Wayne, 

> Thanks again for clearing up my confusion over the origin of the R
> factor for fits in R-space.  I now have a different and unrelated
> question about the R factor.
> From looking through the ifeffit code, it appears that the R
> factor is defined as sum(data-fit)/sum(data).  Is this correct?

Yes, that is correct.   It is a fractional misfit.

> The reason that I am asking is that I would like to use Hamilton's
> test (Acta Cryst 1965, V18, p.502) to determine whether adding
> additional shells to a fit actually results in a better fit.  
> Hamilton's test uses the "crystallographic R factor", which is
> sqrt(sum(data-fit)/sum(data)), so I would like to know whether or
> not to take the square root of the R-factor ratios in Hamilton's
> test.  Thanks again for the help!

I'm not familiar with "Hamilton's test", just downloaded the paper,
and glanced at page 2 of it.  I am sure I do not know all the
subtleties of R-factor(s) used in crystallography: I thought there
were a couple different R-factors used, and except for something
called 'R merge' (which seems to be only about data quality???) that
they were all essentially 'sum(data-fit)/sum(data)', differing in
whether they used Intensities, F values, and how they weighted the
different reflections (this would seem similar to the different ways
of treating XAFS data: weighting, k-, R-space, etc).  It looks to me
like Hamilton used F values.

I have no doubt that there are people on this list know a lot more
about this than I do.  Can anybody provide any insight on the
R-factors and tests used in crystallography, and correct all the
mistakes above?

I think you might also be interested in the Joyner tests, from
Joyner et al, J Phyc C 20, p 4005 (1987).  If I recall correctly,
these are very close to standard statistics F-tests on the
chi-square values, with the aim of testing whether adding data
and/or variables improves a fit.  I believe the on-line EXCURVE
manuals might have some discussion of these.  Of course, seeing if
reduced chi-square is improved is the simplest way to compare two
fits with different number of variables or data ranges.

--Matt