Hi Manfred
I've been using and thinking Rmeas ever since I first saw it; but
(embarrassingly) I've only just woken up to Rpim -- so thanks for the
prompt. So I trawled the original references (Weiss and Hilgenfeld,
1997) to find out why it has the form it does, but I must have skimmed
too quickly, because I couldn't find the explanation.
Rpim, as I understand it, is trying to do two things (See Eq 3 in link
below):
1) penalise me for bad data
2) reward me for high redundancy
But why that *particular* balance of redundancy vs badness? And how do
we know that it was the best one?
And is this really waterproof? Since the redundancy factor (1/(N-1))
tends to zero for large N, does it not dominate for large redundancy?
For instance, for terrible data (e.g. wrong symmetry) but very high
redundancy, then Rpim will still tend to zero, won't it?
So to counteract that, N might be downweighted it turn by the data
badness. Which could in its turn again be.... I don't think I like where
this is going :)
Cheers
phx.
Manfred S. Weiss wrote:
Dear Deb,
R_meas or R_rim is a merging R-factor which is independent of the
redundancy or multiplicity of the data (hence its name), R_pim
stands for precision indicating merging R-factor. R_pim
gives you the precision of the averaged measurement, which is
the one you are actually using for structure solution and refinement.
SCALA will calculate both R_rim (R_meas) and R_pim, XDS/XSCALE
will calculate R_rim (R_meas) only, and SCALEPACK neither of the
two. However, you may produce a file from SCALEPACK with scaled
but unmerged intensities (option NO MERGE ORIGINAL INDEX)
and then download a program from my site called RMERGE or
RMERGE_4LINUX, which will do the job for you.
If you have further questions, please see the page
http://www.embl-hamburg.de/~msweiss/projects/msw_qual.html
or ask me.
Cheers, Manfred
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On Sat, 6 Dec 2008, Debajyoti Dutta wrote:
�
Dear members,
I have a little query hare about Rpim and Rmeans. How these are used to mark
data quality, and how can one calculate it.
Thak you for your reply in advance.
Sincerely
Deb
