Dear All,
I would argue that there are very few situations where it is
necessary to use R-free as a criterion for optimization (as opposed
to R-work). In any case where the parameterization is fixed and we
are simply looking for the best set, R-work will do just fine. It is
only situations such as deciding what type of NCS restraints to
use, and in which the parameterization changes and R might not be
comparable in the various cases, where Rfree might be useful. As
long as only a very few decisions of this kind are made on the basis
of R-free, it will remain essentially unbiased.
The case that Gerard Kleywegt mentions of generating many models in
many ways and using R-sleep for evaluation is an interesting
possibility. I would agree that if a very large set of structures is
to be created and ranked based on R-free, then the R-sleep idea is
useful. However I would argue that it would be simpler and perhaps
just as effective to only use R-work and real-space criteria to
evaluate the structures and reserve R-free as we do now.
At 12:27 PM 10/1/2007, William Scott wrote:
>If R-sleep is to be the "real" validation R-factor, why not just sequester
>each of R-sleep and the current R-free, each as a randomly-chosen (but
>mutually exclusive) set of reflections, and then proceed as normally with
>the other (eg) 80% of the data until the very end of the refinement, using
>the R-free set to optimize weightings for geometries, NCS symmetry
>averaging, and so forth, and then simply add those back in at the
>penultimate step of refinement. In the end, you have R-sleep and the
>Rfactor corresponding to the rest of the data, just like before, and you
>can have the additional statistic reporting the difference between R-sleep
>and and R-free, which we could call something like the R-i-didn't-peak.
>
>
>Peter Adrian Meyer wrote:
> > This raises a slightly tangential question though - how do we know how
> > what obs/param ratio is good enough?
Thomas C. Terwilliger
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Los Alamos National Laboratory
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