Some time ago, I computed the mean value of Rcryst(F) / Rmerge(F) across the whole PDB. This average was 4.5, and I take this as a rough estimate of |Fcalc - Fobs| / sigma(Fobs). More recently, I have been looking in more detail at deposited data, but so far the few cases where this ratio is close to 1 are all cases where sigma(Fobs) is unusually high!
I think the "answer" is that we can believe structures in the PDB to "within 20% error". This is "close enough" for a few things (such as government work), but not for traditional statistics like "confidence tests". For me, it is just really bothersome that we can measure structure factors to better than 5% accuracy, but still don't know how to model them. Ethan does make a good point that sig(Fobs) is the error in the measurement, and that the model-data error is not the weight one should use in refinement, etc. However, when you are comparing one PDB entry (yours) to others (published), I still don't think that sigma(Fobs) plays any significant role. -James Holton MAD Scientist On Tue, Oct 26, 2010 at 4:45 PM, Jacob Keller < [email protected]> wrote: > ----- Original Message ----- > *From:* James Holton <[email protected]> > *To:* [email protected] > *Sent:* Tuesday, October 26, 2010 6:31 PM > *Subject:* Re: [ccp4bb] Against Method (R) > > Yes, but what I think Frank is trying to point out is that the difference > between Fobs and Fcalc in any given PDB entry is generally about 4-5 times > larger than sigma(Fobs). In such situations, pretty much any standard > statistical test will tell you that the model is "highly unlikely to be > correct". > > Wow, so what is the answer to this? Is that figure "|Fcalc - Fobs| = 4-5x > sigma" really true? How, then, do we believe structures? Are there really > good structures where this is discrepancy is not there, to "stake our > claim," so to speak? >
