mailRe: model selection


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Posted by Clare-Louise Evans on April 30, 2007 - 16:28:
Dear Douglas,

I kept getting this same error when I first tried using relax. after checking all the input formats, it turned out to be that it wasn't finding the NH vectors because of the H atoms in my pdb. I found simply changing the HN atoms to H rectified this. Whether this was the true reason I'm unsure, but the error message stopped after I did this.

Hope this helps.

Clare

Douglas Kojetin wrote:
While we are on the topic of model selection/optimization, I am having some trouble with relax 1.2's full_analysis.py script. I have optimized the local_tm run, and I ran the initial run on the oblate tensor (init). When I attempt to optimize the oblate run (round_1), I see the following error a:

relax> results.write(run='final', file='results', dir='oblate/round_1/ aic', force=1, format='columnar', compress_type=1)Opening the file 'oblate/round_1/aic/results.bz2' for writing.RelaxError: The sequence data for the run 'final' does not exist.

Any ideas?

Thanks,
Doug


On Apr 30, 2007, at 10:11 AM, Clare-Louise Evans wrote:

Dear Edward,

Sorry for the thread hijack, but I noticed in the PS section of the
quote Hongyan has included below your comment regarding convergence.

I am having problems with convergence at the moment when running the
full_analysis.py script.  I have run the optimisation of the diffusion
models on the same PC.  For the oblate, prolate and ellipsoid models I
have failed to reach convergence after 30+ rounds.  The sphere model
converged within 5 rounds. However, when I look at the output the only difference between the chi-squared and other parameter values is in the
10th and later decimal places.  Surely, with having such small
differences between the values they can be considered to have
converged.  However, in your comment you state they have to be
identical. If this really is the case then I'm not sure how to proceed
given that I'm failing to reach convergence on these 3 models?

Apologies if this ties in to the question I asked yesterday regarding
full_analysis.py.

Kind regards
Clare

Hongyan Li wrote:
Dear Edward,
Thanks for your early suggestion regarding different model selections. I have been busy on other stuff and only recently got time to focus on this subject
again.
I have run all the diffusion models e.g. isotropic, prolate, oblate and ellipsoid and model selections were made within each model (in aic directory).
I am not sure how to write a script to select different models using
'model_selection()'. Could you please do me a favor?
Your help is highly appreciated!
Best wishes,
Hongyan

Quoting Edward d'Auvergne <edward.dauvergne@xxxxxxxxx>:


Hi,

To compare the results what you need to employ is a technique from the
statistical field of model selection.  The spherical diffusion
(isotropic) + all model-free models of all selected residues is one
single mathematical model. The prolate and oblate spheroids (prolate
and oblate axially symmetric anisotropic diffusion tensors) + all
model-free models, and the ellipsoid (fully anisotropic or three
different eigenvalues) + all model-free models, are three additional
mathematical models. Therefore to compare these four different models
you need to select the model which best represents your relaxation
data.  These models are, however, not nested and therefore cannot be
compared using ANOVA F-tests!  Firstly the three types of diffusion
tensor are not nested (there is a reference from Dominique Marion's
group in which they say ANOVA statistics cannot be used but I can't
find it at the moment (although it shouldn't be too hard to track
down, it's related to Tensor)).  Secondly the model-free models
selected will be different between the four models. Hence chi- squared
and F-tests cannot be used.

A useful reference (I'm not at all biased ;) for this problem is my
paper d'Auvergne, E. J. and Gooley, P. R. (2003), (see
http://www.nmr-relax.com/refs.html for the full reference).  On page
37 at the end of that paper I discuss how AIC model selection is
perfect for selecting between these non-nested models.  The AIC
criterion is still

AIC = chi2 + 2k,

however chi2 is the minimised chi-squared value for the complete model
and k is the sum of the number of diffusion parameters and number of
model-free parameters for all spin systems.  BIC model selection is
likely to work quite well as well.  If you have four runs, one for
each of the diffusion models, then the relax user function
'model_selection()' is designed to select between these models.  I
hope this helps and hasn't been too biased.

Cheers,

Edward


P.S.  Prior to model selection between the diffusion models, the
diffusion models must have fully converged.  Multiple iterations of
optimisation of the model-free models, AIC model selection, and
optimisation of all parameters together (diffusion tensor + model- free parameter of all residues) must be executed. Convergence is when two iterations possess identical chi-squared values, identical model- free
models, and identical parameter values.


On 3/2/07, Hongyan Li <hylichem@xxxxxxxxxxxx> wrote:

Dear relax users,
I have managed to use Relax to run my dynamics data by both isotropic and
axial-oblate models. My qestion is how to compare the results, by

chi-square??

what is the criteria to make judgment that which residues is beter

simulated

with which model?

Thanks for your kind help!

Best wishes,

Hongyan

Dr. Hongyan Li
Department of Chemistry
The University of Hong Kong
Pokfulam Road
Hong Kong


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Dr. Hongyan Li
Department of Chemistry
The University of Hong Kong
Pokfulam Road
Hong Kong


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