Abhijeet, I'll add to Scott's comments. I typically perform the fitting as you suggest, at least in complicated systems, one shell at a time. However, at some point you must go back and explore the correlations between the fit parameters. The simplest way is, after you've added a new shell and satisfied yourself the fit is reasonable in a new region of R- or k- space, to allow more or even all of the parameters to be "guess"es. As Scott suggests, often you can reduce uncertainties by tying together in some manner the parameter from different scattering paths. If there is some reason to believe you know the structure, using a common amplitude for the first and seconds shells is one way to do this. Jeremy -----Original Message----- From: ifeffit-bounces@millenia.cars.aps.anl.gov on behalf of Scott Calvin Sent: Fri 4/10/2009 8:26 AM To: XAFS Analysis using Ifeffit Subject: Re: [Ifeffit] High SO2 Hi Abhijeet, Well, I do know that many people do it that way, so it's not "wrong." But there are a number of problems with it: * You're locking in errors, rather than using additional data to improve your fit. After all, the fit to the first shell has uncertainty, given by the error bars. But you're choosing the center point of the range and locking it in. By doing that, you're then distorting the fit to the next shell. * You're ignoring "leakage" from the second shell when you fit the first shell. The second-shell paths have some width (look at them in Artemis to see this). Inevitably they have some tail that extends into the first shell. A fit to the first shell therefore has a systematic bias. If all you're doing is fitting one shell, then it may be small enough to live with it. But if you're adding a second shell anyway, why not let it inform the fit? * It becomes difficult to keep track of degrees of freedom. By doing fits in layers, you of course aren't actually reducing the number of parameters you're fitting, but ifeffit doesn't know that. The reduced chi-square statistic is no longer reduced by the right number of degrees of freedom, so you lose the ability to use it to compare fits. And if you decide during the fitting of the second shell to change the k-range, degrees of freedom can get quite confusing. Maybe someone who uses this strategy should speak up for it; I'd like to understand what the advantages are. Just to be clear, what I'm suggesting instead is that you fit the first shell alone first, but when you add the second shell you continue to allow the first shell parameters to vary. In any case, either way that you do it, you should be using the S02 from the first shell for the subsequent shells. Otherwise you're not taking advantage of your understanding of the physical meaning of the parameters. --Scott Calvin Sarah Lawrence College On Apr 10, 2009, at 5:52 AM, abhijeet gaur wrote:
Hello Scott Sir, I got that ifeffit basically works on the principle of making the theoretical data look similar to experimental data. But what I know about fitting is that we have to first fit the first shell and then keeping the first shell parameter constant , we go for the next shell. Is it not right. Please tell me Abhijeet Gaur _______________________________________________ Ifeffit mailing list Ifeffit@millenia.cars.aps.anl.gov http://millenia.cars.aps.anl.gov/mailman/listinfo/ifeffit
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