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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