R-factors: Difference between revisions

From CCP4 wiki
Jump to navigation Jump to search
mNo edit summary
Line 1: Line 1:
Historically, R-factors were introduced by ...  
Historically, R-factors were introduced by ... ???


== Definitions ==
== Definitions ==
=== Data quality indicators ===
=== Data quality indicators ===
In the following, all sums over hkl extend only over unique reflections with more than one observation!
* R<sub>sym</sub> and R<sub>merge</sub> : the formula for both is
* R<sub>sym</sub> and R<sub>merge</sub> : the formula for both is
<math>
<math>
Line 9: Line 10:
<br>
<br>
<br>
<br>
where <math>\langle I_{hkl}\rangle</math> is the average of symmetry- (or Friedel-) related observations of a unique reflection, and the first summation is over all unique reflections with more than one observation.
where <math>\langle I_{hkl}\rangle</math> is the average of symmetry- (or Friedel-) related observations of a unique reflection
* Redundancy-independant version of the above: R<sub>meas</sub>
* Redundancy-independant version of the above:  
* measuring quality of averaged intensities/amplitudes: R<sub>p.i.m.</sub> and R<sub>mrgd-F</sub>
<math>
R_{meas} = \frac{\sum_{hkl} \sqrt \frac{n}{n-1} \sum_{j=1}^{n} \vert I_{hkl,j}-\langle I_{hkl}\rangle\vert}{\sum_{hkl} \sum_{j}I_{hkl,j}}
</math>
<br>
<br>
* measuring quality of averaged intensities/amplitudes:
 
for intensities use
<math>
R_{p.i.m} (or R_{mrgd-I}) = \frac{\sum_{hkl} \sqrt \frac{1}{n} \sum_{j=1}^{n} \vert I_{hkl,j}-\langle I_{hkl}\rangle\vert}{\sum_{hkl} \sum_{j}I_{hkl,j}}
</math>
<br>
<br>
 
and similarly for amplitudes:
<math>
R_{mrgd-F} = \frac{\sum_{hkl} \sqrt \frac{1}{n} \sum_{j=1}^{n} \vert F_{hkl,j}-\langle F_{hkl}\rangle\vert}{\sum_{hkl} \sum_{j}F_{hkl,j}}
</math>
<br>
<br>
 


=== Model quality indicators ===
=== Model quality indicators ===
* R and R<sub>free</sub> : the formula for both is  
* R and R<sub>free</sub> : the formula for both is  
<math>
<math>
R=\frac{\sum_{hkl_{unique}}\vert F_{hkl}^{(obs)}-F_{hkl}^{(calc)}\vert}{\sum_{hkl_{unique}} F_{hkl}^{(obs)}}
R=\frac{\sum_{hkl}\vert F_{hkl}^{obs}-F_{hkl}^{calc}\vert}{\sum_{hkl} F_{hkl}^{obs}}
</math>
</math>
<br>
<br>
<br>
<br>
where <math>F_{hkl}^{(obs)}</math> and <math>F_{hkl}^{(calc)}</math> have to be scaled w.r.t. each other. R and R<sub>free</sub> differ in the set of reflections they are calculated from: R is calculated for the [[working set]], whereas R<sub>free</sub> is calculated for the [[test set]].
where <math>F_{hkl}^{obs}</math> and <math>F_{hkl}^{calc}</math> have to be scaled w.r.t. each other. R and R<sub>free</sub> differ in the set of reflections they are calculated from: R is calculated for the [[working set]], whereas R<sub>free</sub> is calculated for the [[test set]].
== what do R-factors try to measure, and how to interpret their values? ==
== what do R-factors try to measure, and how to interpret their values? ==
* relative deviation of
* relative deviation of

Revision as of 12:42, 15 February 2008

Historically, R-factors were introduced by ... ???

Definitions

Data quality indicators

In the following, all sums over hkl extend only over unique reflections with more than one observation!

  • Rsym and Rmerge : the formula for both is

[math]\displaystyle{ R = \frac{\sum_{hkl} \sum_{j} \vert I_{hkl,j}-\langle I_{hkl}\rangle\vert}{\sum_{hkl} \sum_{j}I_{hkl,j}} }[/math]

where [math]\displaystyle{ \langle I_{hkl}\rangle }[/math] is the average of symmetry- (or Friedel-) related observations of a unique reflection

  • Redundancy-independant version of the above:

[math]\displaystyle{ R_{meas} = \frac{\sum_{hkl} \sqrt \frac{n}{n-1} \sum_{j=1}^{n} \vert I_{hkl,j}-\langle I_{hkl}\rangle\vert}{\sum_{hkl} \sum_{j}I_{hkl,j}} }[/math]

  • measuring quality of averaged intensities/amplitudes:

for intensities use [math]\displaystyle{ R_{p.i.m} (or R_{mrgd-I}) = \frac{\sum_{hkl} \sqrt \frac{1}{n} \sum_{j=1}^{n} \vert I_{hkl,j}-\langle I_{hkl}\rangle\vert}{\sum_{hkl} \sum_{j}I_{hkl,j}} }[/math]

and similarly for amplitudes: [math]\displaystyle{ R_{mrgd-F} = \frac{\sum_{hkl} \sqrt \frac{1}{n} \sum_{j=1}^{n} \vert F_{hkl,j}-\langle F_{hkl}\rangle\vert}{\sum_{hkl} \sum_{j}F_{hkl,j}} }[/math]


Model quality indicators

  • R and Rfree : the formula for both is

[math]\displaystyle{ R=\frac{\sum_{hkl}\vert F_{hkl}^{obs}-F_{hkl}^{calc}\vert}{\sum_{hkl} F_{hkl}^{obs}} }[/math]

where [math]\displaystyle{ F_{hkl}^{obs} }[/math] and [math]\displaystyle{ F_{hkl}^{calc} }[/math] have to be scaled w.r.t. each other. R and Rfree differ in the set of reflections they are calculated from: R is calculated for the working set, whereas Rfree is calculated for the test set.

what do R-factors try to measure, and how to interpret their values?

  • relative deviation of

Data quality

  • typical values: ...

Model quality

what kinds of problems exist with these indicators?

- (Rsym / Rmerge ) should not be used, Rmeas should be used instead (explain why ?)

- R/Rfree and NCS: reflections in work and test set are not independant