Hi Doug,
I've done similar comparisons and come to similar results.
There are a few things to keep in mind when trying to rationalise these
differences. First, the approach coded in full_analysis.py makes a
serious attempt to optimise both the rotational diffusion tensor, as
well as the local dynamic parameters. Modelfree, on the other hand,
relies on you having a good estimate of the tensor before you start. So
the first thing to check is whether the diffusion tensor relax gets
agrees with the one you gave Modelfree - if not, all bets are off with
respect to the dynamic parameters. Second, the model selection used by
relax is different to that used by Modelfree, so relax will in some
cases pick different models, even with everything else being equal.
Edward can elaborate on why the relax approach is superior, I'm sure...
Third, the optimisation code in relax is much more up-to-date, so is
better at finding the true best fit for any given model to your data.
Finally, its worth keeping in mind that in many cases, dynamic
parameters are poorly defined, even by good data. Even very big
differences in tau_e, eg. are not always significant.
The difference that would concern me is if there are dramatic
differences in order parameters - S2 is generally fairly robust to the
above issues, within reason.
Cheers,
Chris
On Wed, 2006-12-20 at 16:18 -0500, Douglas Kojetin wrote:
Hi All,
Has anyone compared runs of relax (m1 through m5; full_analysis.py
script) vs. a traditional fastmodelfree/modelfree run using the
binary provided by the Palmer group? I have ... I think I'm using
similar parameters for both runs, and I'm seeing a drastic difference
in results (models chosen).
Thanks in advance for the input,
Doug
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Re: relax vs. 'traditional' modelfree