For the equations, see the epydoc documentation http://epydoc.sourceforge.net/epytext.html, specifically "Section 2.4. Literal Blocks". The double "::" is needed to allow the documentation to compile. Cheers, Edward On 28 August 2014 10:31, <tlinnet@xxxxxxxxxxxxx> wrote:
Author: tlinnet Date: Thu Aug 28 10:31:22 2014 New Revision: 25370 URL: http://svn.gna.org/viewcvs/relax?rev=25370&view=rev Log: Tried to improve docstring for API documentation. task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting. Modified: trunk/specific_analyses/relax_disp/estimate_r2eff.py Modified: trunk/specific_analyses/relax_disp/estimate_r2eff.py URL: http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/estimate_r2eff.py?rev=25370&r1=25369&r2=25370&view=diff ============================================================================== --- trunk/specific_analyses/relax_disp/estimate_r2eff.py (original) +++ trunk/specific_analyses/relax_disp/estimate_r2eff.py Thu Aug 28 10:31:22 2014 @@ -197,19 +197,19 @@ If the minimisation uses the weighted least-squares function: - f_i = (Y(x, t_i) - y_i) / \sigma_i - - then the covariance matrix above gives the statistical error on the best-fit parameters resulting from the Gaussian errors \sigma_i on the underlying data y_i. - - This can be verified from the relation \delta f = J \delta c and the fact that the fluctuations in f from the data y_i are normalised by \sigma_i - and so satisfy <\delta f \delta f^T> = I. + f_i = (Y(x, t_i) - y_i) / sigma_i + + then the covariance matrix above gives the statistical error on the best-fit parameters resulting from the Gaussian errors 'sigma_i' on the underlying data 'y_i'. + + This can be verified from the relation 'd_f = J d_c' and the fact that the fluctuations in 'f from the data 'y_i' are normalised by 'sigma_i' + and so satisfy <d_f d_f^T> = I. For an unweighted least-squares function f_i = (Y(x, t_i) - y_i) the covariance matrix above should be multiplied by the variance of the residuals about the best-fit - \sigma^2 = \sum (y_i - Y(x, t_i))^2 / (n-p) - - to give the variance-covariance matrix \sigma^2 C. + sigma^2 = sum ( (y_i - Y(x, t_i))^2 / (n-p) ) + + to give the variance-covariance matrix sigma^2 C. This estimates the statistical error on the best-fit parameters from the scatter of the underlying data. See: _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-commits mailing list relax-commits@xxxxxxx To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-commits