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Sure, but in P6422 with 2.8A I'd say that 5% of reflections are more
likely near 40-50 than 'a few hundreds' if the cell too small. And
most people simply flag 5% of their reflections without checking how
many these really are. Splitting these up into resolution ranges makes
the situation even worse, and as far as I understand this is what most
ML-programs do for proper Maximum Likelihood refinement. A proper
k-fold cross validation (eg. 50-fold) would, in my point of view, give
a more realistic R value.
Best, Tim
On 06/26/2013 01:30 PM, Bernhard Rupp wrote:
>> you may have only a few hundred and thus not get a reliable Rfree
>> value.
>
> The estimate for the error in R free as a function of the number
> of reflections is as follows:
>
> Brunger initially estimated^35 that the uncertainty in R-free is
> proportional to (Nref )^-1/2, which is reasonable to assume because
> this is how uncertainties vary with sample size. Tickle et al.
> finally showed^38 that the relative uncertainty in Rfree is exactly
> equal to (2Nref )^-1/2 confirming Brunger's initial estimate, with
> constant of proportionality as 2^-1/2.
>
> Following this proportionality, ~1000 reflections are sufficient to
> obtain a better than 1% precision for an overall R-free in the
> 20-30% range, i.e. 'a few hundred' is still not too bad.
>
> Best, BR
>
- --
- --
Dr Tim Gruene
Institut fuer anorganische Chemie
Tammannstr. 4
D-37077 Goettingen
GPG Key ID = A46BEE1A
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