Hi Hongyan, I've just come back from summer holidays at the beach which is why I didn't respond to your earlier queries. Chris answered your questions exactly as I would have. relax has been designed to be flexible. The user functions and the scripting abilities allow you to implement almost all of the different model-free data analysis chains present in the literature - and there are many! The only limitation is your imagination.
The chi-squared and parameter value differences you are experiencing are due to a multitude of factors. Breaking these into the different aspects of the data analysis chain, these are: model selection, model elimination, and optimisation (or minimisation). By carefully redesigning your relax script, you should be able to replicate very similar behaviour to Art's Modelfree (or Dasha if you wish). I wouldn't recommend that at all though (see below). Douglas Kojetin has asked the same question (https://mail.gna.org/public/relax-users/2006-12/msg00008.html, Message-id: <[EMAIL PROTECTED]>) and Chris MacRaild's response (https://mail.gna.org/public/relax-users/2006-12/msg00011.html, Message-id: <[EMAIL PROTECTED]>) as well as my response in that thread explains all the differences in fine detail (https://mail.gna.org/public/relax-users/2006-12/msg00017.html, Message-id: <[EMAIL PROTECTED]>). I hope these archived posts help (I'd recommend reading them all). Cheers, Edward On 1/8/07, Hongyan Li <[EMAIL PROTECTED]> 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
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