r25467 - /trunk/user_functions/relax_disp.py -- August 30, 2014 - 01:03

Author: tlinnet
Date: Sat Aug 30 01:03:34 2014
New Revision: 25467

URL: http://svn.gna.org/viewcvs/relax?rev=25467&view=rev
Log:
Improved documentation to user function relax_disp.r2eff_err_estimate, and 
removed the possibility to use the chi2 Jacobian, as this is rubbish.

But the back-end still have this possibility, should one desire to try this.

task #7822(https://gna.org/task/index.php?7822): Implement user function to 
estimate R2eff and associated errors for exponential curve fitting.

Modified:
    trunk/user_functions/relax_disp.py

Modified: trunk/user_functions/relax_disp.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/user_functions/relax_disp.py?rev=25467&r1=25466&r2=25467&view=diff
==============================================================================
--- trunk/user_functions/relax_disp.py  (original)
+++ trunk/user_functions/relax_disp.py  Sat Aug 30 01:03:34 2014
@@ -635,13 +635,6 @@
 uf.title = "Estimate R2eff errors by the Jacobian matrix."
 uf.title_short = "Estimate R2eff errors."
 uf.add_keyarg(
-    name = "chi2_jacobian",
-    default = False,
-    py_type = "bool",
-    desc_short = "use of chi2 Jacobian",
-    desc = "If the Jacobian derived from the chi2 function, should be used 
instead of the Jacobian from the exponential function."
-)
-uf.add_keyarg(
     name = "spin_id",
     py_type = "str",
     arg_type = "spin ID",
@@ -671,9 +664,12 @@
 uf.desc[-1].add_paragraph("This will estimate R2eff errors by using the 
exponential decay Jacobian matrix 'J' to compute the covariance matrix of the 
best-fit parameters.")
 uf.desc[-1].add_paragraph("This can be an huge time saving step, when 
performing model fitting in R1rho.  Errors of R2eff values, are normally 
estimated by time-consuming Monte-Carlo simulations.")
 uf.desc[-1].add_paragraph("This method is inspired from the GNU Scientific 
Library (GSL).")
-uf.desc[-1].add_paragraph("The covariance matrix is given by: covar = Qxx = 
(J^T J)^{-1}.")
-uf.desc[-1].add_paragraph("Qxx is computed by QR decomposition, Qxx=QR, 
Qxx^-1=R^-1 Q^T.  The columns of R which satisfy: |R_{kk}| <= epsrel |R_{11}| 
are considered linearly-dependent and are excluded from the covariance matrix 
(the corresponding rows and columns of the covariance matrix are set to 
zero).")
+uf.desc[-1].add_paragraph("The covariance matrix is given by: covar = Qxx = 
(J^T.W.J)^-1, where the weight matrix W is constructed by the multiplication 
of an Identity matrix I and a weight array w.  The weight array is 
1/errors^2, which then gives W = I.w = I x 1/errors^2.")
+uf.desc[-1].add_paragraph("Qxx is computed by QR decomposition, J^T.W.J=QR, 
Qxx=R^-1. Q^T.  The columns of R which satisfy: |R_{kk}| <= epsrel |R_{11}| 
are considered linearly-dependent and are excluded from the covariance matrix 
(the corresponding rows and columns of the covariance matrix are set to 
zero).")
 uf.desc[-1].add_paragraph("The parameter 'epsrel' is used to remove 
linear-dependent columns when J is rank deficient.")
+uf.desc[-1].add_paragraph("The errors estimated from the co-variance is 
exactly equal to the errors reported from the co-variance matrix from 
scipy.optimize.leastsq.")
+uf.desc[-1].add_paragraph("scipy.optimize.leastsq uses a numerical Jacobian 
to estimate the co-variance.")
+uf.desc[-1].add_paragraph("Initial tests shows that the errors are twice as 
high than compared to Monte-Carlo simulations.  Therefore expect 4 times 
lower chi2 values.")
 uf.backend = estimate_r2eff_err
 uf.menu_text = "&r2eff_err_estimate"
 uf.gui_icon = "relax.relax_fit"