model_selection(self,
method=None,
modsel_run=None,
runs=None)
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Function for model selection.
Keyword arguments
~~~~~~~~~~~~~~~~~
method: The model selection technique (see below).
modsel_run: The run name to assign to the results of model selection.
runs: An array containing the names of all runs to include in model selection.
Description
~~~~~~~~~~~
The following model selection methods are supported:
AIC: Akaike's Information Criteria.
AICc: Small sample size corrected AIC.
BIC: Bayesian or Schwarz Information Criteria.
Bootstrap: Bootstrap model selection.
CV: Single-item-out cross-validation.
Expect: The expected overall discrepancy (the true values of the parameters are required).
Farrow: Old model-free method by Farrow et al., 1994.
Palmer: Old model-free method by Mandel et al., 1995.
Overall: The realised overall discrepancy (the true values of the parameters are required).
For the methods 'Bootstrap', 'Expect', and 'Overall', the function 'monte_carlo' should have
previously been run with the type argument set to the appropriate value to modify its
behaviour.
If the runs argument is not supplied then all runs currently set or loaded will be used for
model selection, although this could cause problems.
Example
~~~~~~~
For model-free analysis, if the preset models 1 to 5 are minimised and loaded into the
program, the following commands will carry out AIC model selection and assign the results
to the run name 'mixed':
relax> model_selection('AIC', 'mixed')
relax> model_selection(method='AIC', modsel_run='mixed')
relax> model_selection('AIC', 'mixed', ['m1', 'm2', 'm3', 'm4', 'm5'])
relax> model_selection(method='AIC', modsel_run='mixed', runs=['m1', 'm2', 'm3', 'm4', 'm5'])
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