Hi, The first part of your post is simple to respond to as I have used exactly this approach in my PhD thesis. This is described in Chapter 7 of the thesis which can be downloaded from http://eprints.infodiv.unimelb.edu.au/archive/00002799/ . The hybridisation function docstring 'help(run.hybridise)' will explain the rest (otherwise if you could help me in expanding this documentation so that it is fully comprehensible, that would be of great use (those coding are often blind to deficiencies in the code's documentation)).
The second part is a little more worrying. The results should have converged well before 30 iterations unless there is something seriously wrong with the model or data. The inclusion of residues for which there is only 3 data sets may be the problem but, from memory, as you are using models with many more parameters than this these should be automatically deselected by relax. There is one scenario that I can theoretically conceive of and this has to do with the full_analysis.py script's attempt at finding the solution within the universal set by incorporating mathematics through the optimisation of the chi-squared function while also optimising the statistical quantity known as the Kullback-Leibler discrepancy. If this makes no sense, my publications (when they are all out) or thesis should explain everything. The scenario is that the dual optimisations are feeding off each other and causing the results to flip-flop between two continually interchanging models. There could simply be one parameter, being close to insignificance, that is appearing and disappearing causing the chi-squared and AIC values to be repeated every two iterations. Simply tabulating the chi-squared value for a number of these iterations should clearly demonstrate this problem. All that being said, this scenario is quite unlikely and something else is more likely to be the problem. Again a list of the chi-squared values for a large number of iterations would be very useful in tracking down the issue. Regards, Edward On 6/24/07, Douglas Kojetin <[EMAIL PROTECTED]> wrote: > Hi All, > > I have two separate but related questions. I am using relax 1.2 (svn > version 3301). I have relaxation data collected at two fields -- 500 > and 600 MHz. However, I have data for 9 additional residues at 600 > MHz that were unresolved at 500 MHz. > > (1) The protein I am studying has two domains, with considerable > interactions between them, connected by a flexible linker. When all > data (domains + linker) was included in the calculations, the > full_analysis.py protocol picked local_tm for the AIC selection of > the diffusion tensor. I would like to analyze my data using a hybrid > model: (a) the two domains together (using the same diffusion > tensor) and (b) the flexible linker using a separate diffusion tensor > (likely local_tm). My guess is that a prolate or oblate tensor will > be selected for the domains when analyzed without data from the > linker region (the quadric_diffusion program from Art Palmer suggests > an axially symmetric tensor is a good approximation). > > Can anyone provide an example of a script where relax is used to > analyze a hybrid model, or briefly outline the steps? For example, > should I run a local_tm optimization using all residues, then > unselect the flexible linker residues in the unresolved file (as > specified in the full_analysis.py script) and continue the > optimization of the other tensors (sphere, prolate, oblate and > ellipsoid)? > > > (2) I am currently running the full_analysis.py protocol, without > the data for the linker region. The optimization of the prolate > tensor is taking much longer than the other tensors for this > calculation (currently on round_30), as well as the prolate > calculation using all data including the linker region (it converged > in 14 rounds). The differences in the parameters between rounds are > very small: > > """ > ##################### > # Convergence tests # > ##################### > > > Chi-squared test: > chi2 (k-1): 785.88714033105236 > chi2 (k): 785.88714033128417 > The chi-squared value has not converged. > > Identical model-free models test: > The model-free models have converged. > > Identical parameter test: > Parameter: tm > Value (k-1): 6.794068350295769e-09 > Value (k): 6.7940683502957698e-09 > The diffusion parameters have not converged. > > Parameter: Da > Value (k-1): 6337661.7164024841 > Value (k): 6337661.7164041474 > The diffusion parameters have not converged. > > Parameter: theta > Value (k-1): 1.6904048161417038 > Value (k): 1.6904048161417222 > The diffusion parameters have not converged. > > Parameter: phi > Value (k-1): 0.30710640562938446 > Value (k): 0.30710640562950142 > The diffusion parameters have not converged. > > """ > https://mail.gna.org/public/relax-devel/2007-06/msg00012.html > relax does not report a problem for a specific residue, as was > reported in the following post (https://mail.gna.org/public/relax- > users/2006-12/msg00002.html). Could this be a result of having data > at only one field for the 9 residues? > > Thanks in advance, > > Doug > > > _______________________________________________ > 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 > _______________________________________________ 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
