That's a very nice explanation of the utility of restraints. I suspect that many out there in mailing-list-land will appreciate your comments quite a bit.
Perhaps you could discuss more explicitly on how the error bar guides your choice of weight, maybe even with an example...
B
Thanks, Bruce. In case people aren't familiar with restraints, here's a brief paragraph on how they work: Ifeffit determines the "best" fit by minimizing chi-square which is given by the sum of the squares of the misfits between fit and data at each point as scaled by an estimated error epsilon (so that the result is dimensionless). By default, ifeffit uses high-R noise to estimate epsilon, but that can be overridden (this is implemented in Artemis as well). A "restraint" simply gives an expression which is squared and then added to chi-square, thus giving ifeffit an additional variable to minimize. So one way in which I've used restraints is to fit a standard compound in the usual way and then move on to a related compound. The fit for the related compound involved more unknown parameters, and tended to yield high uncertainties. I expected certain values to be the same (or at least very close) for the sample as compared to the standard: S02 and E0 for example. But I was not comfortable simply setting the values for the sample equal to the fit from the standard, both because the fit has an uncertainty associated with it and because there could be small differences with, e.g., normalization, and I'd like to let ifeffit evaluate uncertainties for the sample parameters. So I used restraints with the uncertainty in the standard's parameter as the epsilon for the restraint. For example, in one case the fit of the standard yielded an E0 of 3.66 +/- 1.04 eV. I therefore used the following in the sample's fit: guess E0 = 3.66 resE0 = (E0 - 3.66)/1.04 One problem with this scheme is that it makes the estimate of epsilon for the data quite important. One of the beamlines I use used to have high-frequency oscillations in the signal which made the ifeffit method of estimating epsilon a poor choice. But it's probably a good idea to think about the epsilon generated by ifeffit anyway, and it's crucial to do so for multi-edge fits. In any case, it seems to me that the restraint method I described here maintains the proper statistical meaning of chi-square, with the difficulty being where it always was; i.e. in estimating the epsilon for the data. --Scott Calvin Sarah Lawrence College