Hi Kicaj,
OK, sorry I misunderstood then. And, despite my laziness, I think that Sameh is right that it's probably time for a more complete update.... Such a thing should probably feature Bruce's newer codes of course. --Matt On Sat, Mar 10, 2012 at 10:16 AM, Matt Newville <[email protected]> wrote: > Hi Kicaj, > > 2012/3/10 "Dr. Dariusz A. Zając" <[email protected]>: >> Hi, >> maybe these below clarify a little bit the problem, but the problem sounds >> very intriguing >> http://millenia.cars.aps.anl.gov/pipermail/ifeffit/2004-July/005729.html >> http://millenia.cars.aps.anl.gov/pipermail/ifeffit/2005-October/006613.html >> http://cars9.uchicago.edu/ifeffit/FAQ/FeffitModeling >> >> I am waiting also for the answer from authors > > I would have said these questions have been answered, but maybe I > misunderstand... What is the question you are waiting to be answered? > > All of chi-square, reduced chi-square, and R factor express the sum of > squares of the residual (data-model) after a fit has finished. The > difference between these statistics is how they are scaled. > > In particular, chi-square is scaled by the estimated error in the > data. If you look at a (naive?) introduction to statistics, you will > see it stated that this should be approximately the number of degrees > of freedom in the fit. Reduced chi-square is then defined to be > chi-squared / (the number of degrees of freedom in the fit), so that > it should be 1 (according to statistics 101). This presupposes a > couple of things that aren't very true for us: > a) it assumes we actually know the uncertainty in the data -- the > automated estimate in ifefit is pretty simplistic. > b) it assumes our model of the data is much better than that data > uncertainty. Many people describe these as "systematic errors" and > include alll sorts of data processing artifacts as well as errors in > the Feff calculations. > > For us, reduced chi-square is almost always >> 1, unless the data is very > noisy. > > R-factor scales the fit residual by the magnitude of the data itself, > for some estimate of "fractional misfit". This gives a convenient > measure that is independent of the scale of the data (and so also > independent of data k-range and k-weight for fits in R-space), and can > more easily be made into a "rule of thumb", say "If R-factor > 0.05, > then you should be wary of the results". > > Hope that helps, > > --Matt -- --Matt Newville <newville at cars.uchicago.edu> 630-252-0431 _______________________________________________ Ifeffit mailing list [email protected] http://millenia.cars.aps.anl.gov/mailman/listinfo/ifeffit
