# R-factors

From CCP4 wiki

Historically, R-factors were introduced by ...

## Definitions

### Data quality indicators

- R
_{sym}and R_{merge}: the formula is

[math]\displaystyle{
R_{merge} = \frac{\sum_{hkl}\vert I_{hkl}-\langle I_{hkl}\rangle\vert}{\sum_{hkl}I_{hkl}}
}[/math]

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

- Redundancy-independant version of the above: R
_{meas} - measuring quality of averaged intensities/amplitudes: R
_{p.i.m.}and R_{mrgd-F}

### Model quality indicators

- R and R
_{free}: the formula is (LaTex please )

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

- relative deviation of

### Data quality

- typical values: ...

### Model quality

## what kind of problems exist with these indicators?

- (R_{sym} / R_{merge} ) should not be used, R_{meas} should be used instead (explain why ?)

- R/R_{free} and NCS: reflections in work and test set are not independant