Re: Comparison of Monte Carlo simulations vs. covariance matrix. -- August 29, 2014 - 16:57

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 <edward@xxxxxxxxxxxxx>:
> 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 <tlinnet@xxxxxxxxxxxxx> 
> wrote:
> > Do you mean terrible or double?
> >
> > Best
> > Troels
> >
> > 2014-08-29 16:15 GMT+02:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>:
> > > 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
> > >
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