Calculate parameter errors from the Monte Carlo simulations.
Parameter errors are calculated as the standard deviation of the distribution of parameter values. This function should never be used if parameter values are obtained by minimisation and the simulation data are generated using the method `direct'. The reason is because only true Monte Carlo simulations can give the true parameter errors.
For proper error analysis using Monte Carlo simulations, a sequence of function calls is required for running the various simulation components. The steps necessary for implementing Monte Carlo simulations are:
Monte Carlo simulations can be turned on or off using functions within this class. Once the function for setting up simulations has been called, simulations will be turned on. The effect of having simulations turned on is that the functions used for minimisation (grid search, minimise, etc) or calculation will only affect the simulation parameters and not the model parameters. By subsequently turning simulations off using the appropriate function, the functions used in minimisation will affect the model parameters and not the simulation parameters.
An example for model-free analysis using the prompt UI mode which includes only the functions required for implementing the above steps is:
[numbers=none] relax> minimise.grid_search(inc=11) # Step 2.
[numbers=none] relax> minimise.execute('newton') # Step 2.
[numbers=none] relax> monte_carlo.setup(number=500) # Step 3.
[numbers=none] relax> monte_carlo.create_data(method='back_calc') # Step 4.
[numbers=none] relax> monte_carlo.initial_values() # Step 5.
[numbers=none] relax> minimise.execute('newton') # Step 6.
[numbers=none] relax> eliminate() # Step 7.
[numbers=none] relax> monte_carlo.error_analysis() # Step 8.
An example for reduced spectral density mapping is:
[numbers=none] relax> minimise.calculate() # Step 2.
[numbers=none] relax> monte_carlo.setup(number=500) # Step 3.
[numbers=none] relax> monte_carlo.create_data(method='back_calc') # Step 4.
[numbers=none] relax> minimise.calculate() # Step 6.
[numbers=none] relax> monte_carlo.error_analysis() # Step 8.