Hi,

The covariance matrix error estimate is not so bad and it should be
close to the MC simulations in most cases.  But MC simulations are
almost always of higher quality and do not suffer from artefacts.

Regards,

Edward



On 28 August 2014 09:46,  <tlin...@nmr-relax.com> wrote:
> Author: tlinnet
> Date: Thu Aug 28 09:46:45 2014
> New Revision: 25364
>
> URL: http://svn.gna.org/viewcvs/relax?rev=25364&view=rev
> Log:
> Added a warning to the auto analyses about error estimation from the 
> Co-variance.
>
> task #7822(https://gna.org/task/index.php?7822): Implement user function to 
> estimate R2eff and associated errors for exponential curve fitting.
>
> Modified:
>     trunk/auto_analyses/relax_disp.py
>
> Modified: trunk/auto_analyses/relax_disp.py
> URL: 
> http://svn.gna.org/viewcvs/relax/trunk/auto_analyses/relax_disp.py?rev=25364&r1=25363&r2=25364&view=diff
> ==============================================================================
> --- trunk/auto_analyses/relax_disp.py   (original)
> +++ trunk/auto_analyses/relax_disp.py   Thu Aug 28 09:46:45 2014
> @@ -512,6 +512,10 @@
>                  # Print
>                  subsection(file=sys.stdout, text="Estimating errors from 
> Covariance matrix", prespace=1)
>
> +                # Raise warning.
> +                text = 'Estimating errors from the Covariance matrix is 
> highly likely to "quite" wrong.  Use only with extreme care, and for initial 
> rapid testing of your data.'
> +                warn(RelaxWarning(text))
> +
>                  # Estimate errors
>                  self.interpreter.relax_disp.r2eff_err_estimate()
>              else:
>
>
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