I certainly expect Chi-squared and the dynamic parameters to be highly dependent on the value of tm. This is why I recomend the extensive iteration of steps 2-4 below, in an attempt to find the optimal fit. I would expect if you start the procedure below from subtly different tm values and iterate to convergence you should reach the same result in the end (ie. tm, chi2 and all other parameters should be nearly identical). If that is not the case, you could use the model-selection functions in relax to test which result is the best fit to your data. This is a little dangerous, however, because you have no way of knowing that the two possible solutions you are considering are the only possibilities (ie. there might always be another, better, solution that you havn't found yet).
One alternative is to use the analysis protocol implimented in the full_analysis.py sample script. This is a new and quite different approach, that does not rely on having an initial tm estimate. It has been discussed in the thread Edward pointed you to, and elsewhere on this list. One final point to keep in mind is that all modelfree analysis protocols can be effected by bad data. It is well worth looking carefully for apparent outliers, residues that appear to be strongly affected by Rex, etc. and excluding them from the early stages of the analysis. They can always be reintroduced after you have settled on a final diffusion tensor. Chris On Mon, 2007-01-08 at 10:04 +0800, Hongyan Li wrote: > Dear Chris, > Thanks for the helpful suggestion. > I have tried as you suggested to repeat steps 2-4 from estimated tm and then > from best-fit tm. Since estimated tm I used is from modelfree (which is very > good) I actually got converged results immediately. However, I noticed that a > subtle difference in tm caused Chi-square significantly different. Of cause, > other parameters are also different. The question is how to judge which set of > data is more accurate (based on Chi-square??). > > Best wishes, > > Hongyan > > Quoting Chris MacRaild <[EMAIL PROTECTED]>: > > > Hi Hongyan, > > > > relax is designed to be completely flexible in the way you perform your > > analysis, allowing for the procedure to be tailored to the system at > > hand, or for new proceedures to be developed. One procedure that I can > > recomend which is somewhat similar to the one you outline is as follows: > > > > 1. estimate tm > > 2. fit each residue to dynamic models > > 3. select best model > > 4. fit tm and dynamic parameters simultaneously > > 5. repeat steps 2-4 starting from best-fit tm value. Continue until > > results converge > > 6. repeat steps 2-5 for each diffusion model (isotropic, axially > > symetric and anisotropic) > > 7. select best diffusion model > > 8. Monte Carlo simulations (error analysis) > > > > As you note, Monte Carlo simulations over all parameters will be very > > slow. This is why I recommend only performing the error analysis at the > > end of the whole proceedure. I some cases it may be necessary to perform > > the Monte Carlo simulations over only the dynamic parameters (ie. with > > diffusion tensor fixed) in order to improve efficiency. > > > > There has been some discussion of this and other analysis proceedures on > > this list before. The thread that starts here: > > > > https://mail.gna.org/public/relax-users/2006-10/msg00007.html > > > > is worth a look. > > > > Chris > > > Dr. Hongyan Li > Department of Chemistry > The University of Hong Kong > Pokfulam Road > Hong Kong > > _______________________________________________ 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
