On 1/8/07, Chris MacRaild <c.a.macraild@xxxxxxxxxxx> wrote:
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).
I would add that iterating the data analysis chain (model-free
optimisation, model elimination, model selection, and finally
optimisation of the global model) is absolutely essential for finding
the global solution. The iterations should be terminated only when,
between iterations, all model-free models are identical, the
chi-squared value is identical, and the model-free and diffusion
parameter values are identical. Otherwise you haven't found the
solution yet, and interpreting results from the unconverged results is
hazardous.
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.
The protocol embedded in the 'full_analysis.py' sample script is a new
protocol that I will soon have published. This protocol is quite
different from the currently used model-free data analysis chains, yet
it could quite easily be implemented in Dasha and Modelfree as well.
How you do model-free analysis (i.e. the specifics of the data
analysis chain) is not coupled to the program you are using (Tensor is
the exception).
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.
This is quite important if you are to implement a data analysis chain
whereby you start with and initial estimate of the diffusion tensor.
For the new model-free optimisation protocol in the 'full_analysis.py'
script, the model-free description comes first and then the diffusion
tensor is obtained. In this protocol no relaxation data should be
excluded.
Edward
Re: scripts