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, <tlinnet@xxxxxxxxxxxxx> 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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Re: r25364 - /trunk/auto_analyses/relax_disp.py