>>(i) We have multiple observations of the same, already integrated h: the 'unmerged' data <- most important data set which SHOULD BE deposited and rarely is.
>yes, fully agree.
Perfect.
> I don't quite understand the difference between (ii) and (iii). As soon as you take the weighted average, you merge the data, because you create one single estimate of the intensity I (and sigma(I)) of a unique reflection from several symmetry-related observations of that unique reflection. So, to me, 'taking the weighted average' and 'merging' are different words for the same procedure.
There is indeed no distinction between (ii) and (iii) form the merging point of view, I just wanted to point out the difference between just 'merged' and 'unique' data.
We return to Kay's original post.
>> Indicators of precision of *unmerged* data are: [Rsym=Rmerge (which should be deprecated),] <- yes, and I want to iterate:
Here is already where the notational confusion starts - 'unmerged' data (i) obviously contain multiple observations of a single reflection h, then how can any measure of their quality logically be called an Rsym (there is no sym in a single reflection) ?
A Rsym is per definition of sym a measure producing merged data of type (iii) , although it is also AN Rmerge.
Historically this seems to come from the original Arndt definition (c.f. Diederichs & Karplus 1997) but it is illogical in the above context. The original definition of Rmerge also includes already the summation over a set of binned hkls.
Along the same line, that the quality indicator for 'unmerged' data is their 'merging' R is also illogical - they have the same quality before they are merged. Not only as a statistic, even as a term Rmerge should be buried (i.e. finally BECOME a statistic).
One primary statistic that is valid universally are the <i/sigI>. None of these Rs are robust statistics. Rmeas is an asymptotic target (penalizing you for small N) , and Rpim some form of standard error of the mean (rewarding you for large N). Choose wisely....
Because of its statistical defensibility (clear definition and the association with a confidence or significance level) CC1/2 is interesting and perhaps the only measure in addition to primary <I/sigi> needed - with the juicy bonus of having via CC*/work/free a traceable relation to the model quality. That, as Kay has pointed out in his papers, is more than you can say about any of these Rs. </anti_R_flame>
> Thus, Rmerge � 0.8/<I/s(I)>
can only hold for unmerged data (i.e. observations), not for merged data (unique reflections, after averaging over symmetry-related observations).
True. I see that. Which is the reason why it is still close for the low redundancy data historically observed, but I think this will change rapidly with the PADs & high redundancy becoming standard - another reason to bury Rmerge & associates.
Happy Easter, BR
best,
Kay
>
>Is that correct? If so, let�s continue the thread (there is more to come...) or adjust the definitions.
>
>Best, BR
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