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
>>>
>>> _______________________________________________
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