Hi Clemens
OK so you're saying use only the reflections that are in common between all
datasets and keep the parameterisation the same. There are clearly two
distinctly different ways in which datasets can differ: either a different
set of indices due to different resolution cut-offs or completeness, and/or
as you say different sets of values of Iobs/Fobs for the same set of
indices. In practice of course it's very likely to be a mixture of the two
cases. Clearly your suggestion could be of help in identifying the "best"
dataset in the latter case, but not in the former case, since it's not even
looking at the reflections that are not in common, so there's no way of
knowing whether they would have improved the map had they been used
(without obviously eye-balling the map!).
I just have an uncomfortable feeling about using a data-derived model to
judge the quality of the data: it sounds like a circular argument to me!
IMO there should be two completely distinct steps: first decide on the
"best" data using data-related criteria alone (Rmeas, CChalf, resolution,
whatever), then with this "best" dataset decide on the "best" model using
the model selection metrics (Rfree, free likelihood and their relatives).
Very interesting discussion!
Cheers
-- Ian
On Thu, 4 Nov 2021 at 16:00, Clemens Vonrhein <vonrh...@globalphasing.com>
wrote:
> On Wed, Nov 03, 2021 at 12:54:00PM +0000, Ian Tickle wrote:
> > Suppose you had to compare two datasets differing only in their
> > high-resolution cut-offs. Now Rwork, Rfree and Rall will inevitably have
> > higher values at the high d* end, which means that if you apply a cut-off
> > at the high d* end all the overall R values will get smaller, so use of
> any
> > R value as a data-quality metric will tend to result in selection of the
> > lowest-resolution dataset from your candidate set, which may well give a
> lower
> > quality map: not what you want!
>
> Couldn't that be avoided by using the common set of reflection data
> present in both the A and B dataset (from David's example - or even
> across a whole series A-Z)? When at the same time we keep the same
> model parametrisation (i.e. not adding altConfs or waters, keeping
> same TLS definitions etc) it might be useful to compare different data
> processing procedures via the R{free,all,work} during refinement.
>
> As far as I can see, the only difference would be the actual values of
> F and sig(F) (or I and sig(I)) attached to the identical set of Miller
> indices ... right?
>
> Cheers
>
> Clemens
>
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