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<div class="moz-cite-prefix">Hi Keitaro,<br>
<br>
Peter Zwart wrote the code so I hope he comments on this more. My
understanding that this is essentially based on Randy Read's
paper:<br>
<br>
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Read, R. J. (1999). Acta Cryst. D55, 1759-1764.<br>
<br>
Pavel<br>
<br>
On 9/5/13 2:31 AM, Keitaro Yamashita wrote:<br>
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<blockquote
cite="mid:CAJUzew7iiwUd00=t0vrALKq_Ds_MvxFfhSR5gw7Bt2HpHbdezg@mail.gmail.com"
type="cite">
<pre wrap="">Dear cctbx developers,
I am interested in the implementation of model-based reflection
outlier rejection. As I read the code
mmtbx/scaling/outlier_rejection.py (lines 244-351), I noticed that
maybe there was a discrepancy between what log_message explained and
the actual code. The log_message in the code says:
</pre>
<blockquote type="cite">
<pre wrap="">Outliers are rejected on the basis of the assumption that a scaled
log likelihood differnce 2(log[P(Fobs)]-log[P(Fmode)])/Q\" is distributed
according to a Chi-square distribution (Q\" is equal to the second
derivative of the log likelihood function of the mode of the
distribution).
The outlier threshold of the p-value relates to the p-value of the
extreme value distribution of the chi-square distribution.
</pre>
</blockquote>
<pre wrap="">
while actual p_value is calculated for each hkl as
p_value = 1 - erf(sqrt(LLG))**N,
where
LLG = log p(F=Fbar | Fmodel) - log p(F=Fobs | Fmodel),
and N is the number of reflections. Here, Fbar is F which
gives the maximum value of p(F | Fmodel). At least, Q (the second
derivative of p(F=Fbar | Fmodel)) is not used in the actual
calculation.
Could someone please explain the meaning of the actual calculation?
Why taking square-root and raising erf() result to the power of N?
Thank you very much,
Keitaro
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</pre>
</blockquote>
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