Hi Mark, Do you have this issue with all your data? The residuals are quite small but probably statistically significant and there seems to be a pattern to it, but these can sometimes be due to incorrect error estimates and biases respectively. What techniques did you use for temperature control and calibration (https://www.nmr-relax.com/manual/Temperature_control_and_calibration.html)? Improper control can lead to bias and "patterns" in residuals. And how did you estimate the errors for each data point? If these are out, the non-linear least squares fitting algorithms can fail. The errors influence the curvature of the optimization space (rather than topology) and incorrect errors can sometimes squeeze valleys in this space creating false minima.
Regards, Edward On Fri, 21 Jan 2022 at 00:06, Mark Bostock <[email protected]> wrote: > > Dear relax-users, > > I'm trying to fit some methyl-13C SQ CPMG data. I have a number of residues, > which appear to have an exchange contribution, but result in poor fits e.g. > > I've tried a variety of different relaxation dispersion models (CR72 full, > B14 full, NS CPMG 2-site expanded, IT99, TSMFK01) but the fit doesn't > improve. I've also tried increasing the grid increment parameter from 11 to > 21, but again this doesn't improve the fit. Very occasionaly when I have been > testing conditions, a model has accurately fitted the data (in the following > NS CPMG 2-site expanded) but I am unable to replicate this consistently. > > > Any suggestions to improve the reliability of this fitting would be very much > appreciated. > > Many thanks, > > Mark > > > > _______________________________________________ > nmr-relax-users mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/nmr-relax-users _______________________________________________ nmr-relax-users mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/nmr-relax-users
