Hi Edward,
I'm trying to write a script to calculate the chisq values for each
of the prolate rounds, but I'm having some trouble as I'm not 100%
familiar with the relax subroutines.
#--start
import glob
runs = glob.glob('prolate/round_*')
# Loop over the runs.
for name in runs:
name=name+'/aic'
run.create(name, 'mf')
results.read(name)
chi2=self.relax.data.chi2[run]
print "%s: %1.20f" % (name, chi2)
#--end
But this does not seem to work. Can someone help me with the proper
code to extract chisq values from multiple runs?
Thanks,
Doug
On Jun 24, 2007, at 5:12 PM, Edward d'Auvergne wrote:
> 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
>>
>>
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