Hi Rongjin
Model based outliers are observations reflections that disagree
disproportionate with the model, where what is disproportionate is
determined along the lines summarized below:
Calculated amplitudes and estimated values of alpha and beta
are used to compute the log-likelihood of the observed amplitude.
The method is inspired by Read, Acta Cryst. (1999). D55, 1759-1764.
Outliers are rejected on the basis of the assumption that a scaled
log likelihood difference 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).
Note however that these reflections are not thrown out of your dataset.
If you do improve your model and it turns out that these reflections
are ok anyway, they will be included later on during refinement.
Why these particular reflections are flagged as bad is unclear at the
moment. Contact me off list for more help and we can send a summary
when done.
HTH
Peter
On 8 December 2010 11:45, [email protected]
Hi All
I have a question regarding reflection outliers detection in phenix.refine: in my input reflection file, there are 24432 reflections, but only 22769 of them were used in refinement. I checked the log file and saw 1663 outliers.
(below if from the log) basic_wilson_outliers = 14 extreme_wilson_outliers = 18 beamstop_shadow_outliers = 0 model_based_outliers = 1657 total = 1663
What is the "model_based_outliers"? What causes so many reflections rejected and not used in the refinement?
Thanks
Rongjin
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