On 15 November 2016 at 14:33, Jeremy Anderson <jande118@xxxxxxx> wrote:
Hi Edward,
Thanks for the follow up. I totally understand the reasons for not having
fixed values within a relax analysis, it seems like a special case relative
to what I've seen in the literature and increases ones ability to skew the
results to their liking, I'm doing my best to safeguard against that myself.
No problems. You do have to be quite careful, as it is far too easy
to fall into an alternative reality that can nicely explain some of
the biology. Mapping the optimisation space is an essential tool when
constraining certain parameters to see if you are at a well defined
minimum. For example it can show you if you've just chopped across a
valley in the space and the optimiser is landing at the bottom of that
valley where it has been cut. If you have access to the ancient, yet
very powerful OpenDX software, you can use the dx.map and dx.execute
relax user functions for this.
I was able to implement such an analysis in python using the RD models from
relax as well as the scipy and lmfit packages to both hold the dw parameters
constant while performing the usual grid search then nonlinear least squares
minimization and perform a cluster analysis holding the rate constant and/or
the major population constant amongst all residues. The code is a bit of a
mess at the moment but I'm hoping to clean it up and make a repository on
github, so I can better document what I did and so other folks can check it
out if they want.
Note that I originally looked at the scipy optimisation packages for
relax. However I found fatal bugs in all three of the algorithms
implemented at the time (Levenberg-Marquardt being one of them). The
algorithms appeared to minimise the results, but they were nothing
like what Art Palmer's Modelfree4 found. I was comparing relax to
Modelfree4 for debugging at the time when implementing the model-free
analysis component. I don't know if anything has changed since then,
but you really need to be wary and double check whenever you use any
part of scipy. Anyway, because scipy's optimisation was so terrible,
I decided to write the minfx optimisation library
(https://gna.org/projects/minfx/). You'll see a record of all of this
in my publication history ;)
Thanks for your assistance.
You're welcome!
Regards,
Edward
Re: Dispersion Back Calculation