Jeremy, I'm curious to understand this approach better. Just to confirm, as I understand it you do something like this: 1) Fit the first shell 2) Set the first-shell parameters to their best-fit values, expand the R-range and paths to include the second shell, and guess parameters related to the second shell (possibly using constraints to take advantage of discoveries from the first shell fit). 3) Once things look OK, go back to guessing some of the parameters so that correlations can be explored. What's the advantage to fixing the first-shell parameters in step 2? Is it to avoid nonsensical fits as the parameter space increases? If so, why not use restraints (particularly, say, penalty-type restraints) to make sure the first-shell parameters remain reasonable without actually forcing them to the values from the first shell fit or doing confusing things to the degrees of freedom? I suspect there's some very practical reason why people use this stepwise approach, but I'm not yet seeing it... --Scott Calvin Sarah Lawrence College On Apr 10, 2009, at 11:24 AM, Kropf, Arthur Jeremy wrote:
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