Dear Richard, I *think* it works like this, don't know if it's detailed or rigourous enough for you!
If I(l,h,i) is the intensity of the ith observation of reflection h on frame l, with error sig(l,h,i), and S(l) is the (inverse) scale factor to be applied to frame l, then the error in the scaled intensity I(l,h,l)/S(l) is parameterised in terms of the error scale factor (E1) and estimated error (E2) as sqrt( (E1*sig(l,h,i))**2 + (E2*I(h)*S(l))**2 ) where I(h) is the weighted mean of the scaled intensity values for index h (i.e. the merged, scaled intensity). The reasoning behind this that the errors in the intensities of strong and weak reflections generally arise from different sources. Weak data are noisy, whilst very strong data can often be systematically badly measured (especially in DENZO, which assumes that all your spots are the same size and shape) or overloaded. E1 and E2 thus tend to dominate the error model at high and low resolutions, respectively. Hope that helps, Joe > Does anyone know of a detailed rigorous discussion of how the > scalepack error model/Bayesian reasoning works? The scalepack manual > has no equations for this. > > Richard Gillilan > MacCHESS >
