Hello,
(Cross posted to NMRGEN although that doesn't have many subscribers yet.)
If anyone has a good idea about how to calculate error estimates for the
parameters for this kind of curve fitting then let us know. Rasmus said
he once used a method which involved randomly removing points from the
analysis and then calculating the standard deviation of the resultant
parameter fits. As Tim has mentioned, what we have done in the code is to
sample the (x, y) points around their stated values using the given
deviations. I'm not sure about the theoretical underpinning of either
method.
Wayne
On Thu, 25 Aug 2005, Tim Stevens wrote:
> > I've a question about the Rate Analysis part of analysis. I have loaded in
> > my T2 spectra etc, calculated errors for each and put them in the
> > condition point errors. When I select Group Peaks I get a set of results,
> > however if I select Recalculate All Rates, without changing anything, the
> > TC error changes. When pressed again it changes to something different
> > again.
>
> There is no easy way of calculating the error in the time constant. So
> what we have done is to sample a number of fittings (currently 1000)
> within the error widths of the data points. We estimate the error in the
> time constant from the standard deviation of the many fitting attempts.
>
> Because the sampling fits are chosen randomly when the error calculation
> is repeated the fits are different and hence the time constant error
> alters.
>
> One thing you can do to limit the variation is goto line 390 in
> $CCPNMR_HOME/python/ccpnmr/analysis/DataAnalysisBasic.py and increase the
> number of samples "nIter = 1000" to "nIter = 10000" or larger, but bearing
> in mind that the more samples the slower the fitting will be.
>
>
> > I also found that there usually is an error which is miles out from
> > the rest (i.e. say if most were around 10 then the odd one was maybe 6000,
> > or something ridiculously small). Recalculate and the no. changes again.
>
> If the Fit Error is also large I would suggest having a look at the data
> points in the graph [Show Function Fit] to see if there's anything
> obviously wrong.
>
> There is also the issue that the fit is very sensitive to the X (time)
> errors. I will discuss this further with Wayne.
>
> Tim
>
>
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