Dear All,
How meaningful are the second derivative based estimates obtained via full matrix inversion when the gradient is not 0 (i.e. when not in the minimum)? I can understand that when you are working with high-resolution data and your R-value is close to 0, things could work, but what happens when around a more challenging 2A?
If you are interested in the uncertainty of the occupancy, I recommend not doing any refinement, but just generate a list of occupancies and B-values for the atom of interest and compute the (free) likelihood for each model. Subsequent normalisation of the neg-exponent of these values, should provide you with an answer that could be just as believable as any other method around. A little bit of python scripting should do the trick quite easily.
Both the full matrix inversion and the suggestion above probe the steepness of the data-agreement hole the structure is sitting in. Pavels suggestion explores the spread of local minima around the starting configuration. I am not sure what method is more appropriate, perhaps it is instructive to know what problem you are trying to solve.
HTH
P