r6032 - /1.3/docs/latex/curvefit.tex -- May 01, 2008 - 16:35

Author: bugman
Date: Thu May  1 16:35:25 2008
New Revision: 6032

URL: http://svn.gna.org/viewcvs/relax?rev=6032&view=rev
Log:
Completed the relaxation curve fitting chapter of the relax manual.


Modified:
    1.3/docs/latex/curvefit.tex

Modified: 1.3/docs/latex/curvefit.tex
URL: 
http://svn.gna.org/viewcvs/relax/1.3/docs/latex/curvefit.tex?rev=6032&r1=6031&r2=6032&view=diff
==============================================================================
--- 1.3/docs/latex/curvefit.tex (original)
+++ 1.3/docs/latex/curvefit.tex Thu May  1 16:35:25 2008
@@ -161,6 +161,43 @@
 
 \section{Error analysis}
 
-Only one technique adequately estimates parameter errors when the parameter 
values where found by optimisation -- Monte Carlo simulations\index{Monte 
Carlo simulation}.
-
-\textbf{\textit{Please write me!}}
+Only one technique adequately estimates parameter errors when the parameter 
values where found by optimisation -- Monte Carlo simulations\index{Monte 
Carlo simulation}.  In relax this can be implemented by using a series of 
functions from the \texttt{monte\_carlo} user function class.  Firstly the 
number of simulations needs to be set
+
+\example{monte\_carlo.setup(number=500)}
+
+For each simulation, randomised relaxation curves will be fit using exactly 
the same methodology as the original exponential curves.  These randomised 
curves are created by back calculation from the fitted model parameter values 
and then each point on the curve randomised using the error values set 
earlier in the script
+
+\example{monte\_carlo.create\_data()}
+
+As a grid search for each simulation would be too computationally expensive, 
the starting point for optimisation for each simulation can be set to the 
position of the optimised parameter values of the model
+
+\example{monte\_carlo.initial\_values()}
+
+Then exactly the same optimisation as was used for the model can be performed
+
+\example{minimise(`simplex', constraints=False)}
+
+The parameter errors are then determined as the standard deviation of the 
optimised parameter values of the simulations
+
+\example{monte\_carlo.error\_analysis()}
+
+
+
+% Finishing off.
+%%%%%%%%%%%%%%%%
+
+\section{Finishing off}
+
+To finish off, the script first saves the relaxation rates together with 
their errors in a simple text file
+
+\example{value.write(param=`rx', file=`rx.out', force=True)}
+
+Grace plots are created and viewed
+
+\example{grace.write(y\_data\_type=`rx', file=`rx.agr', force=True)}
+
+\example{grace.view(file=`rx.agr')}
+
+and finally the program state is saved for future reference
+
+\example{state.save(file=`rx.save', force=True)}