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*To*: ccp4bb@dl.ac.uk*Subject*: [ccp4bb]: I to F*From*: Dmitriy Alexeev <dima@holyrood.ed.ac.uk>*Date*: Thu, 16 Nov 2000 09:48:08 +0000*Organization*: University of Edinburgh*Sender*: owner-ccp4bb@dl.ac.uk

What the weak F's are for? For better refinement, OK! What is the major danger of including them? Artificially low sigF's - not to overweight the arbitrary assigned poorly measured F's. The estimate sigF=sqrt(sigI) is as fine as any other, except for few (but very important for ML refinement) cases when sigI is accidently too small - it happens if the data redundancy is low. I suggest to introduce a lower limit for sigF and derive it from the well measured reflections. Indeed, in absence of systematic errors and for well measured reflections sigI->const*sqrt(I) and sigF=0.5*sigI/F. Then: sigF->const*sqrt(I)/F=const, which is the estimate for min(sigF) I compute this constant and top sigF's up to this level. For good data (statistical noise only) this const is really constant throughout all the intensisty range (I tested it). Actually, the variability of min(sigF) might be used as an indicator of systematic errors. Dmitriy Alexeev, Edinburgh.

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**Follow-Ups**:**[ccp4bb]: I to F***From:*Phil Evans <pre@mrc-lmb.cam.ac.uk>

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