Re: r27203 - /trunk/specific_analyses/relax_disp/optimisation.py -- January 19, 2015

Hi,

Maybe we should discuss on the original thread the problem in detail
and see if there is a solution.  I wonder why the kex errors are so
different?

Regards,

Edward



On 19 January 2015 at 09:51, Troels Emtekær Linnet
<tlinnet@xxxxxxxxxxxxx> wrote:

Hi Edward.

I was through sor (sum of residuals), sos(sum of squares), and now sse(sum
of squared errors).

I agree with sse being the best, but I have reverted all my commits, and
found a solution through the API.

Just using the chi2 value, and finding degrees of freedom with the API.

If one wants .sse, one can just quickly do

value.set(val=1.0, param="r2eff", error=True)
minimise.calculate(verbosity=1)

Anyway, in the end, the new method did not solve my problem.
STD_fit = sqrt(chi2 / dof)

Since dof is so big (many datapoints, small amounts of parameters for
clustered fitting), STD_fit becomes close to 1.


Best
Troels


2015-01-19 9:35 GMT+01:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>:


Hi Troels,

Could you rename spin.sos to spin.sse?  This is the acronym used in
the field and by other software - the sum of squared errors
(https://en.wikipedia.org/wiki/Residual_sum_of_squares,
http://www.palmer.hs.columbia.edu/software/modelfree_manual.pdf).  If
the individual SSE elements are divided by the experimental error
sigma_i, then this is the chi2 value.  The SSE and chi2 statistics are
related, and are identical in the case of unit errors.  Other
acronyms, much less used in the NMR field, are SSR or RSS.  I don't
think I've ever encountered SOS before, outside of emergencies
(https://en.wikipedia.org/wiki/SOS).

Cheers,

Edward

On 16 January 2015 at 23:19,  <tlinnet@xxxxxxxxxxxxx> wrote:

Author: tlinnet
Date: Fri Jan 16 23:19:50 2015
New Revision: 27203

URL: http://svn.gna.org/viewcvs/relax?rev=27203&view=rev
Log:
Implemented storing of sum of squares and the standard deviation of
these for relaxation dispersion, when doing a point calculation.

Task #7882 (https://gna.org/task/?7882): Implement Monte-Carlo
simulation, where errors are generated with width of standard deviation 
or
residuals.

Modified:
    trunk/specific_analyses/relax_disp/optimisation.py

Modified: trunk/specific_analyses/relax_disp/optimisation.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/optimisation.py?rev=27203&r1=27202&r2=27203&view=diff

==============================================================================
--- trunk/specific_analyses/relax_disp/optimisation.py  (original)
+++ trunk/specific_analyses/relax_disp/optimisation.py  Fri Jan 16
23:19:50 2015
@@ -119,7 +119,7 @@
     @type spin_lock_nu1:        list of lists of numpy rank-1 float
arrays
     @keyword relax_times_new:   The interpolated experiment specific
fixed time period for relaxation (in seconds).  The dimensions are {Ei, 
Mi,
Oi, Di, Ti}.
     @type relax_times_new:      rank-4 list of floats
-    @keyword store_chi2:        A flag which if True will cause the
spin specific chi-squared value to be stored in the spin container.
+    @keyword store_chi2:        A flag which if True will cause the
spin specific chi-squared value to be stored in the spin container 
together
with the sum of squares of the residuals and the standard deviation of 
the
sum of squares of the residuals.
     @type store_chi2:           bool
     @return:                    The back-calculated R2eff/R1rho value
for the given spin.
     @rtype:                     numpy rank-1 float array
@@ -215,10 +215,15 @@
     # Make a single function call.  This will cause back calculation
and the data will be stored in the class instance.
     chi2 = model.func(param_vector)

-    # Store the chi-squared value.
+    # Get the sum of squares 'sos' of the residuals between the fitted
values and the measured values. Get the std deviation of these, std_sos.
+    sos, sos_std = model.get_sum_of_squares()
+
+    # Store the chi-squared value, sums of squares of residual and the
standard deviation of sums of squares of residual.
     if store_chi2:
         for spin in spins:
             spin.chi2 = chi2
+            spin.sos = sos
+            spin.sos_std = sos_std

     # Return the structure.
     return model.get_back_calc()


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Re: r27203 - /trunk/specific_analyses/relax_disp/optimisation.py

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