On Wed, Oct 15, 2008 at 3:53 PM, Tyler Reddy <TREDDY@xxxxxx> wrote:
Hi,
I'll try to dig up those references. The other thing I find confusing is
that
some groups use the curve fit error for the parameters. So, the errors in R1
and R2 per residue are actually from the nonlinear curve fitting process
itself. In theory, if there is no error in peak height then the fit is
perfect.
So I wonder if there is yet another relationship to think about if you want
to
use those values?!
Well, the Jackknife technique
(http://en.wikipedia.org/wiki/Resampling_(statistics)#Jackknife) does
something like this. It uses the errors present inside the collected
data to estimate the parameter errors. It's not great, but is useful
when errors cannot be measured. You can also use the covariance
matrix from the optimisation space to estimate errors. Both are rough
and approximate, and in convoluted spaces (the diffusion tensor space
and double motion model-free models of Clore et al., 1990) are known
to have problems. Monte Carlo simulations perform much better in
complex spaces.
I have these values already for T1 and T2 parameters and their curve fitting
errors (though I haven't figured out how to propagate these errors to the
reciprocal rate constants, or if that will even be meaningful), but I'm not
sure how they compare to the other 2 'error types' we are talking about.
Certainly, S/N = peak height/RMS baseline noise (From Cavanagh textbook)
And while there are many references that throw around the sqrt(2) in various
equations, I haven't seen a comprehensive explanation yet.
Neither have I ;)
Regards,
Edward
Re: Relax_fit.py problem