Hm. The last idea I have, is the division by number of degree of freedom.
So either 5-2, or 4-2. That should be verified by a script with many different time points. But then the errors for intensity gets very different. Hm. On 30 Aug 2014 01:45, "Troels Emtekær Linnet" <[email protected]> wrote: > The sentence: > > "then the covariance matrix above gives the statistical error on the > best-fit parameters resulting from the Gaussian errors 'sigma_i' on > the underlying data 'y_i'." > > And here I note the wording: > "statistical error" > "Gaussian errors" > > Best > Troels > > > 2014-08-29 21:21 GMT+02:00 Troels Emtekær Linnet <[email protected]>: > > Hi Edward. > > > > I also think it is some math some where. > > > > I have a feeling, that it is the creating of Monte Carlo data with 2 > sigma? > > and then some log/exp calculation of R2eff. > > > > If the errors are 2 x times over estimated, the chi2 values are 4 as > > small, and the > > space should be the same? > > > > best > > Troels > > > > 2014-08-29 17:06 GMT+02:00 Edward d'Auvergne <[email protected]>: > >> 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 <[email protected]> > 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 <[email protected]>: > >>>> 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 <[email protected]> > wrote: > >>>>> Do you mean terrible or double? > >>>>> > >>>>> Best > >>>>> Troels > >>>>> > >>>>> 2014-08-29 16:15 GMT+02:00 Edward d'Auvergne <[email protected]>: > >>>>>> 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 > >>>>>> [email protected] > >>>>>> > >>>>>> 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 [email protected] 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
