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Interesting.. I use this formula to calculate R-factor between two
data sets when I cannot choose which one to call "Fobs" and which
one to call "Fcalc". But clearly, this is not exact what we call
R-factor.<br>
<br>
Pavel<br>
<br>
<div class="moz-cite-prefix">On 10/13/14 2:02 PM, Nathaniel Echols
wrote:<br>
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cite="mid:CALeAa1OLmHL5+ywhzA75xvZ1dN9bstajNnKnWBhLe6NWA0MGuQ@mail.gmail.com"
type="cite">
<div dir="ltr">On Mon, Oct 13, 2014 at 11:54 AM, Nathaniel Echols
<span dir="ltr"><<a moz-do-not-send="true"
href="mailto:nechols@lbl.gov" target="_blank">nechols@lbl.gov</a>></span>
wrote:<br>
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<div class="gmail_quote"><span class="">
<div>In the default phenix.refine output MTZ, the
"F-obs" column will not be scaled to F-model.�
My guess is that your input data have already
been placed on an absolute scale based on the
Wilson statistics, so the results are reasonably
close, but when I tried using the same commands
on an XFEL dataset I got an R-factor of 192.</div>
</span></div>
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<div>Okay, this statement is at least partially incorrect -
your data are clearly on the correct scale in the
phenix.refine output file, but the data in the file I used
are not. �(I'm going to blame this on the weirdness of
certain XFEL data.)</div>
<div><br>
</div>
<div>However, I did eventually figure out the problem:
SFTOOLS is using a different formula for the R-factor.� If
you give it the command "correl help", it will include
this:</div>
<div><br>
</div>
<div>
<div>� RFACT � � �Rfactor in percent</div>
<div>� � � � � � �( 200*Sum|col1-col2|/sum(col1+col2) )</div>
</div>
<div><br>
</div>
<div>Which disagrees with our source code, and the Rupp
textbook, and Kay's wiki, and Wikipedia, all of which use
sum(col1) as the denominator (assuming col1 == F-obs, but
in our code it's written more generally).� In other words:
the R-factors from SFTOOLS cannot be meaningfully compared
to the R-factors from refinement.</div>
<div><br>
</div>
<div>-Nat</div>
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