On Wed, Oct 15, 2008 at 3:53 PM, Tyler Reddy <[EMAIL PROTECTED]> 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 _______________________________________________ relax (http://nmr-relax.com) This is the relax-users mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-users
