relax SVN archive

To systemtest Relax_disp.test_task_7882_monte_carlo_std_residual(), adding test for raise of errors, if the R2eff model is selected.

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

Raising an error, if the R2eff model is used, and drawing errors from the fit.

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

Change to systemtest Relax_disp.x_test_task_7882_kex_conf()

This is just a temporary systemtest, to check for local minima.

This is method in regression book of Graphpad: http://www.graphpad.com/faq/file/Prism4RegressionBook.pdf
Page: 109-111.

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

Temporary test of making a confidence interval as described in fitting guide.

This is systemtest Relax_disp.x_test_task_7882_kex_conf, which is not activated by default.

Running the test, interestingely shows, there is a possibility for a lower global kex.
But the value only differ from kex=1826 to kex=1813.

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

Added API function in relaxation dispersion to return error structure from the reduced chi2 distribution.

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

Adding empty API method to return errors from the reduced chi2 distribution.

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

Adding to back-end of pipe_control.error_analysis(), to modify datapoint as error drawn from the reduced chi2 gauss distribution.

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

Extension of the userfunction monte_carlo.create_data, to draw errors from the reduced chi2 gauss distribution as found by best fit.

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

Simplification of systemtest Relax_disp.test_task_7882_monte_carlo_std_residual, to just test the creation of Monte-Carlo data where errors are drawn from the reduced chi2 distribution.

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

Reverted range of commits r27213:r27202 as this was a bad implementation.

To get the distribution, one should look at the sum of squares.
But this should be from the weighted sum of squares, which is stored as chi2.
The API provides a method of model_statistics(), to get:
k - number of parameters.
n - number of data points.
chi2 - the chi-squared value.

Then reduced chi2 can be computed. chi2_red = chi2/(n-k)
And the error distribution be drawn from this.

The command used was:
svn merge -r27213:r27201 .

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