model_selection(self,
method=None,
modsel_pipe=None,
pipes=None)
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Function for model selection.
Keyword arguments
~~~~~~~~~~~~~~~~~
method: The model selection technique (see below).
modsel_pipe: The name of the new data pipe which will be created by this user function by
the copying of the selected data pipe.
pipes: An array containing the names of all data pipes 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 executed with the type argument set to the appropriate value to modify its
behaviour.
If the pipes argument is not supplied then all data pipes will be used for model selection.
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 to place the selected
results into the 'mixed' data pipe, type one of:
relax> model_selection('AIC', 'mixed')
relax> model_selection(method='AIC', modsel_pipe='mixed')
relax> model_selection('AIC', 'mixed', ['m1', 'm2', 'm3', 'm4', 'm5'])
relax> model_selection(method='AIC', modsel_pipe='mixed', pipes=['m1', 'm2', 'm3', 'm4', 'm5'])
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