Hi Alison, I agree entirely with Victor and Anatoly. In addition, I'd say that in my work an R-factor of above 0.1 is pretty sketchy. How sketchy depends on the complexity of the fit, of course; you're going over a big r-range, so if it takes you only two or three parameters to get an R-factor of 0.11, I guess that's not too bad. But if you have more parameters than that and it's still not fitting so well, I'd be concerned. As a referee, for example, that would be a much larger warning flag to me than the little discrepancy between the R-factors for the k- and q-space fits. Just to echo Victor and Anatoly again: it's quite easy for me to believe that the fitting algorithm just happened to find a somewhat different minimum in the fitting space in the two cases. They both suggested ways to test if that is the case. Although I've never fit in q-space, I've seen something that on the face of it is equally baffling: sometimes I've added an additional fitting parameter, changing nothing else, and seen the R-factor <italic>increase </italic>slightly. In some ideal sense, that shouldn't be--the fitting routine (this one happened to be Ifeffit) "should" at worst get the identical fit as to when the new parameter was constrained. But it is impossible to write a fitting routine that is guaranteed to find the closest possible fit, so all routines will occasionally generate those kinds of results. --Scott Calvin Sarah Lawrence College
-------------- Original message --------------
Bruce,
I've looked, and my R-factors for the back-transformed
space are not necessarily twice the value of the R-factors
for the k-space fits. In fact, they are often quite
close. For example, just recently I had a k-space
R-factor of 0.102 and a q-space R-factor of 0.113. Now, I
realize those numbers are very close, but I'm afraid if I
try to publish this, then I will get criticism for the
q-space R-factors being larger. If I can explain it, then
maybe it won't be a problem.