I've just added the 2D Grace plots for this to the repository (r25444, http://article.gmane.org/gmane.science.nmr.relax.scm/23194). They are also attached to the task for easier access (https://gna.org/task/index.php?7822#comment107). From these plots I see that the I0 error appears to be reasonable, but that the R2eff errors are overestimated by 1.9555.
The plots are very, very different. It is clear that chi2_jacobian=True just gives rubbish. It is also clear that there is a perfect correlation in R2eff when chi2_jacobian=False. The plot shows absolutely no scattering, therefore this indicates a crystal clear mathematical error somewhere. I wonder where that could be. It may not be a factor of 2, as the correlation is 1.9555. So it might be a bug that is more complicated. In any case, overestimating the errors by ~2 and performing a dispersion analysis is not possible. This will significantly change the curvature of the optimisation space and will also have a huge effect on statistical comparisons between models. Regards, Edward On 29 August 2014 16:56, Troels Emtekær Linnet <tlin...@nmr-relax.com> wrote: > The default is now chi2_jacobian=False. > > You will hopefully see, that the errors are double. > > Best > Troels > > 2014-08-29 16:43 GMT+02:00 Edward d'Auvergne <edw...@nmr-relax.com>: >> Terrible ;) For R2eff, the correlation is 2.748 and the points are >> spread out all over the place. For I0, the correlation is 3.5 and the >> points are also spread out everywhere. Maybe I should try with the >> change from: >> >> relax_disp.r2eff_err_estimate(chi2_jacobian=True) >> >> to: >> >> relax_disp.r2eff_err_estimate(chi2_jacobian=False) >> >> How should this be used? >> >> Cheers, >> >> Edward >> >> >> >> On 29 August 2014 16:33, Troels Emtekær Linnet <tlin...@nmr-relax.com> wrote: >>> Do you mean terrible or double? >>> >>> Best >>> Troels >>> >>> 2014-08-29 16:15 GMT+02:00 Edward d'Auvergne <edw...@nmr-relax.com>: >>>> Hi Troels, >>>> >>>> I really cannot follow and judge how the techniques compare. I must >>>> be getting old. So to remedy this, I have created the >>>> test_suite/shared_data/dispersion/Kjaergaard_et_al_2013/exp_error_analysis/ >>>> directory (r25437, >>>> http://article.gmane.org/gmane.science.nmr.relax.scm/23187). This >>>> contains 3 scripts for comparing R2eff and I0 parameters for the 2 >>>> parameter exponential curve-fitting: >>>> >>>> 1) A simple script to perform Monte Carlo simulation error analysis. >>>> This is run with 10,000 simulations to act as the gold standard. >>>> >>>> 2) A simple script to perform covariance matrix error analysis. >>>> >>>> 3) A simple script to generate 2D Grace plots to visualise the >>>> differences. Now I can see how good the covariance matrix technique >>>> is :) >>>> >>>> Could you please check and see if I have used the >>>> relax_disp.r2eff_err_estimate user function correctly? The Grace >>>> plots show that the error estimates are currently terrible. >>>> >>>> Cheers, >>>> >>>> Edward >>>> >>>> _______________________________________________ >>>> relax (http://www.nmr-relax.com) >>>> >>>> This is the relax-devel mailing list >>>> relax-devel@gna.org >>>> >>>> To unsubscribe from this list, get a password >>>> reminder, or change your subscription options, >>>> visit the list information page at >>>> https://mail.gna.org/listinfo/relax-devel _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list relax-devel@gna.org To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
