On Tuesday, October 26, 2010 01:16:58 pm Frank von Delft wrote:
> Um...
>
> * Given that the weighted Rfactor is weighted by the measurement errors
> (1/sig^2)
>
> * and given that the errors in our measurements apparently have no
> bearing whatsoever on the errors in our models (for macromolecular
> crystals, certainly - the "R-vfactor gap")
You are overlooking causality :-)
Yes, the errors in state-of-the-art models are only weakly limited by the
errors in our measurements. But that is exactly _because_ we can now weight
properly by the measurement errors (1/sig^2). In my salad days,
weighting by 1/sig^2 was a mug's game. Refinement only produced
a reasonable model if you applied empirical corrections rather than
statistical weights. Things have improved a bit since then,
both on the equipment side (detectors, cryo, ...) and on the processing
side (Maximum Likelihood, error propagation, ...).
Now the sigmas actually mean something!
> is the weighted Rfactor even vaguely relevant for anything at all?
Yes, it is. It is the thing you are minimizing during refinement,
at least to first approximation. Also, as just mentioned, it is a
well-defined value that you can do use for statistical significance
tests.
Ethan
>
> phx.
>
>
>
> On 26/10/2010 20:44, Ian Tickle wrote:
> > Indeed, see: http://scripts.iucr.org/cgi-bin/paper?a07175 .
> >
> > The Rfree/Rwork ratio that I referred to does strictly use the
> > weighted ('Hamilton') R-factors, but because only the unweighted
> > values are given in the PDB we were forced to approximate (against our
> > better judgment!).
> >
> > The problem of course is that all refinement software AFAIK writes the
> > unweighted Rwork& Rfree to the PDB header; there are no slots for the
> > weighted values, which does indeed make doing serious statistics on
> > the PDB entries difficult if not impossible!
> >
> > The unweighted crystallographic R-factor was only ever intended as a
> > "rule of thumb", i.e. to give a rough idea of the relative quality of
> > related structures; I hardly think the crystallographers of yesteryear
> > ever imagined that we would be taking it so seriously now!
> >
> > In particular IMO it should never be used for something as critical as
> > validation (either global or local), or for guiding refinement
> > strategy: use the likelihood instead.
> >
> > Cheers
> >
> > -- Ian
> >
> > PS I've always known it as an 'R-factor', e.g. see paper referenced
> > above, but then during my crystallographic training I used extensively
> > software developed by both authors of the paper (i.e. Geoff Ford& the
> > late John Rollett) in Oxford (which eventually became the 'Crystals'
> > small-molecule package). Maybe it's a transatlantic thing ...
> >
> > Cheers
> >
> > -- Ian
> >
> > On Tue, Oct 26, 2010 at 7:28 PM, Ethan Merritt<[log in to unmask]> wrote:
> >> On Tuesday, October 26, 2010 09:46:46 am Bernhard Rupp (Hofkristallrat a.D.) wrote:
> >>> Hi Folks,
> >>>
> >>> Please allow me a few biased reflections/opinions on the numeRology of the
> >>> R-value (not R-factor, because it is neither a factor itself nor does it
> >>> factor in anything but ill-posed reviewer's critique. Historically the term
> >>> originated from small molecule crystallography, but it is only a
> >>> 'Residual-value')
> >>>
> >>> a) The R-value itself - based on the linear residuals and of apparent
> >>> intuitive meaning - is statistically peculiar to say the least. I could not
> >>> find it in any common statistics text. So doing proper statistics with R
> >>> becomes difficult.
> >> As WC Hamilton pointed out originally, two [properly weighted] R factors can
> >> be compared by taking their ratio. Significance levels can then be evaluated
> >> using the standard F distribution. A concise summary is given in chapter 9
> >> of Prince's book, which I highly recommend to all crystallographers.
> >>
> >> W C Hamilton "Significance tests on the crystallographic R factor"
> >> Acta Cryst. (1965). 18, 502-510
> >>
> >> Edward Prince "Mathematical Techniques in Crystallography and Materials
> >> Science". Springer-Verlag, 1982.
> >>
> >> It is true that we normally indulge in the sloppy habit of paying attention
> >> only to the unweighted R factor even though refinement programs report
> >> both the weighted and unweighted versions. (shelx users excepted :-)
> >> But the weighted form is there also if you want to do statistical tests.
> >>
> >> You are of course correct that this remains a global test, and as such
> >> is of limited use in evaluating local properties of the model.
> >>
> >> cheers,
> >>
> >> Ethan
> >>
> >>
> >>
> >>
> >>> b) rules of thumb (as much as they conveniently obviate the need for
> >>> detailed explanations, satisfy student's desire for quick answers, and
> >>> allow superficial review of manuscripts) become less valuable if they have a
> >>> case-dependent large variance, topped with an unknown parent distribution.
> >>> Combined with an odd statistic, that has great potential for misguidance and
> >>> unnecessarily lost sleep.
> >>>
> >>> c) Ian has (once again) explained that for example the Rf-R depends on the
> >>> exact knowledge of the restraints and their individual weighting, which we
> >>> generally do not have. Caution is advised.
> >>>
> >>> d) The answer which model is better - which is actually what you want to
> >>> know - becomes a question of model selection or hypothesis testing, which,
> >>> given the obscurity of R cannot be derived with some nice plug-in method. As
> >>> Ian said the models to be compared must also be based on the same and
> >>> identical data.
> >>>
> >>> e) One measure available that is statistically at least defensible is the
> >>> log-likelihood. So what you can do is form a log-likelihood ratio (or Bayes
> >>> factor (there is the darn factor again, it’s a ratio)) and see where this
> >>> falls - and the answers are pretty soft and, probably because of that,
> >>> correspondingly realistic. This also makes - based on statistics alone -
> >>> deciding between different overall parameterizations difficult.
> >>>
> >>> http://en.wikipedia.org/wiki/Bayes_factor
> >>>
> >>> f) so having said that, what really remains is that the model that fits the
> >>> primary evidence (minimally biased electron density) best and is at the same
> >>> time physically meaningful, is the best model, i. e., all plausibly
> >>> accountable electron density (and not more) is modeled. You can convince
> >>> yourself of this by taking the most interesting part of the model out (say a
> >>> ligand or a binding pocket) and look at the R-values or do a model selection
> >>> test - the result will be indecisive. Poof goes the global rule of thumb.
> >>>
> >>> g) in other words: global measures in general are entirely inadequate to
> >>> judge local model quality (noted many times over already by Jones, Kleywegt,
> >>> others, in the dark ages of crystallography when poorly restrained
> >>> crystallographers used to passionately whack each other over the head with
> >>> unfree R-values).
> >>>
> >>> Best, BR
> >>> -----------------------------------------------------------------
> >>> Bernhard Rupp, Hofkristallrat a.D.
> >>> 001 (925) 209-7429
> >>> +43 (676) 571-0536
> >>> [log in to unmask]
> >>> [log in to unmask]
> >>> http://www.ruppweb.org/
> >>> ------------------------------------------------------------------
> >>> Und wieder ein chillout-mix aus der Hofkristall-lounge
> >>> ------------------------------------------------------------------
> >>>
> >> --
> >> Ethan A Merritt
> >> Biomolecular Structure Center, K-428 Health Sciences Bldg
> >> University of Washington, Seattle 98195-7742
> >>
>
--
Ethan A Merritt
Biomolecular Structure Center, K-428 Health Sciences Bldg
University of Washington, Seattle 98195-7742
|