The repository for the development of the extension to PEtab for model selection, including the additional file formats and Python 3 package.
The Python 3 package provides both the Python 3 and command-line (CLI)
interfaces, and can be installed from PyPI, with pip3 install petab-select.
Further documentation is available at http://petab-select.readthedocs.io/.
There are example Jupyter notebooks for usage of PEtab Select with
- the command-line interface, and
- the Python 3 interface,
in the doc/examples directory.
AIC: https://en.wikipedia.org/wiki/Akaike_information_criterion#DefinitionAICc: https://en.wikipedia.org/wiki/Akaike_information_criterion#Modification_for_small_sample_sizeBIC: https://en.wikipedia.org/wiki/Bayesian_information_criterion#Definition
forward: https://en.wikipedia.org/wiki/Stepwise_regression#Main_approachesbackward: https://en.wikipedia.org/wiki/Stepwise_regression#Main_approachesbrute_force: Optimize all possible model candidates, then return the model with the best criterion value.famos: https://doi.org/10.1371/journal.pcbi.1007230
Note that the directional methods (forward, backward) find models with the smallest step size (in terms of number of estimated parameters). For example, given the forward method and a predecessor model with 2 estimated parameters, if there are no models with 3 estimated parameters, but some models with 4 estimated parameters, then the search may return candidate models with 4 estimated parameters.