[RMCProfile-users] Weighting of data sets

[King, Graham M]

The simplest solution to your problem would be to simply not include the Bragg data at all and only fit to G(r) and F(Q).  RMCProfile does not require you to fit Bragg data, you can choose whatever data types you want to fit.  In fact, you can run it using no data at all if you wanted to do something like randomly swap atoms or disorder them.  Just take the entire Bragg section out of your .dat file.  

A higher value for weight allows more moves that make the fit worse to be accepted.  The weights are supposed to be related to the actual errors in your data, to help prevent you from over-fitting the data.  So you could also make the weight value high for your Bragg data and then no moves would be rejected because of it.  From my experience weight numbers between 0.02-0.06 seem to work best, but it also seems to depend on the exact situation.  The weights are the sigma values used to calculate the total X^2 for the fit, as described in appendix C of the manual.  You should read this section for a better understanding how the weights you set affect the refinement procedure.  The section describes the mathematics of it.  The X^2 is the number the RMC is trying to reduce, and it is inversely proportional to sigma^2.  Since the probably of accepting a move that makes the fit worse is lower for larger X^2, using lower weights allows more atom moves to be accepted.

-Graham King

________________________________________
From: Henrik Mauroy [hmauroy at gmail.com]
Sent: Tuesday, June 03, 2014 12:24 PM
To: rmcprofile-users at rmcprofile.org
Subject: [RMCProfile-users] Weighting of data sets

Hi everyone.
This might be a dumb question but I wonder how the weighting of the data
sets are done. So far I've used equal weighting for G(r), F(Q) and Bragg
and haven't been troubled by this yet.
I have some data collected at GEM where the Bragg data is completely
impossible to fit due to a unusually weak Bragg signal (due to
cancelling negative scattering lengths), and need to turn the weighting
of the Bragg data to such a low number that it is not being part of the
fitting.

1) What effect does a higher number for the weight do?
2) In what range should the weight-number be? In most examples it is
smaller than 0.1, but can you use bigger or smaller numbers?
3) I see from experimenting with high and low numbers that the chi^2
value calculated for the data set in question becomes larger for a lower
weighting. Is this the number RMCProfile is trying to reduce?

Thanks for any answer!

Cheers,
Henrik
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