On Thu, 16 Sep 1999, Dr.Joerg Bergmann wrote:
> (high systematic error, bad difference plot, but low e.s.d.'s)
> (low systematic error, good difference plot, but high e.s.d.'s).
Apologies for diving off onto the general case, but does this make sense?
You improve your model and the quality of your parameter estimation gets
worse? The esd's in the first case seem underestimated (to me).
I assume your esd's are coming directly from the LSQ matrix.
Conventionally people seem to multiply them by a chi^2 factor to degrade
the ones with the high systematic error (Giacovazzo's book has a nifty
explanation of this in terms of assuming the systematic errors are really
an underestimation of experimental errors). If a procedure like that is
carried out then I would (naively) expect you to find your fit *and*
your parameter estimation to improve with the use of a better model.
That's if the extra parameters improve your fit, if not then you'd be
introducing extra degrees of freedom and correlations which might degrade
the esd's.
Try thinking of using a nice peakshape versus a crappy peakshape as
opposed to prefered orientation corrections. As usual one should only
introduce parameters if they *significantly* improve the fit (Hamilton's
test etc).
Hope I'm making sense,
Jon
============================================================================
Dept. of Chemistry, Lensfield Road, Cambridge, CB2 1EW
