[phenixbb] phenix and weak data
epozh001 at umaryland.edu
Wed Dec 12 07:56:16 PST 2012
On Wed, 2012-12-12 at 09:00 +0000, Randy Read wrote:
> In the statistics you give below, the key statistic is probably the
> standard deviation of sigf/sqrt(beta), which is actually quite small.
> So after absorbing the average effect of measurement error into the
> beta values, the residual variation is even less important to the total
> variance than you would think from the total value of sigf.
You are absolutely right - this is one of the possible reasons (perhaps
the main reason) why the effect on model upon incorporating sigf is not
obvious. Indeed, in the example dataset that I used relative standard
deviation of sigf in resolution shells ranges from 0.3 at high to 0.6 at
low resolution. The incorporation of the shell-average sigf into beta
is obscured by the fact that the two anti-correlate.
Another issue is that average value of beta does not matter that much
and is only weakly controlled by data. This is obvious (to me but I may
be wrong) from eq. (6-7) in Lunin (2002). It points out that near
minimum (and we are talking here about effects on the *final model*)
target may be approximated quadratically and the applied weight is
essentially inversely proportional to beta (not exactly, of course, but
it is the major effect beta has on the target). Thus, some inflation of
beta over what it is expected to be (model variance in reciprocal space)
will do very little to the target other than scaling it.
I think my main issue is with the idea that beta may be used as an
estimate of model variance. Mathematically it probably does not matter,
but we all tend to attach "physical" interpretations to model
parameters, and here it does not work as it seems to suggest that
crystallographic models are grossly overfitted.
> I would still argue that it's relatively easy to incorporate the
> experimental error into the likelihood variances so it's worth doing
> even if we haven't found the circumstances where it turns out to
I am not sure it is that easy. As I mentioned previously, the
analytical expressions for alpha/beta break down when sigf is
incorporated. Also, it is possible that this has already been done by
Kevin Cowtan in his 2005 paper. My observation was that the spline
coefficients return more reasonable estimates of model variance.
There are also very strange consequences in alpha/beta approach.
Equations (6-7) in Lunin (2002) essentially set up the quadratic
approximation. I already mentioned that target value Fs* oddly reduces
to exact zero for "weak reflections", and if beta is overestimated those
are not so weak anymore. In the example dataset that I used, in the low
resolution shells average I/sigma of such zeroed reflections is as high
as ~5-6. I can identify a reflection that according to eq. (6-7) will
have a target value of zero and has I/sigma=22.5! I understand that
this still has only minor effect on the final model because only ~12% of
reflections are hit (and nobody minds that 5-10% of experimental data is
routinely tossed for the sake of Rfree calculation). Still, it is
puzzling and unexpected.
Also, the weights applied to individual reflections are approximated by
ws* which has some peculiar properties, namely that it dips to zero
around f/sqrt(beta)~1. Curiously, it recovers back to full weight for
reflections that are weaker (Fig.3 in Lunin 2002). Again, I see no flaw
in the math, but it is rather counterintuitive that reflections that
roughly match model error are weighted down, while even weaker
reflections are not. Given that these weaker reflections have their
respective target Fs* reset to zero, there will be potential during
minimization to simply run weak reflections down to zero across the
Oh, and by the way, phaser rocks :)
Edwin Pozharski, PhD, Assistant Professor
University of Maryland, Baltimore
When the Way is forgotten duty and justice appear;
Then knowledge and wisdom are born along with hypocrisy.
When harmonious relationships dissolve then respect and devotion arise;
When a nation falls to chaos then loyalty and patriotism are born.
------------------------------ / Lao Tse /
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