run_iovsearch#

pharmpy.tools.run_iovsearch(column='OCC', list_of_parameters=None, rank_type='bic', cutoff=None, distribution='same-as-iiv', results=None, model=None, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', E=None, **kwargs)#

Run IOVsearch tool. For more details, see IOVsearch.

Parameters:

  • column (str) – Name of column in dataset to use as occasion column (default is ‘OCC’)

  • list_of_parameters (None or list) – List of parameters to test IOV on, if none all parameters with IIV will be tested (default)

  • rank_type ({‘ofv’, ‘lrt’, ‘aic’, ‘bic’, ‘mbic’}) – Which ranking type should be used. Default is BIC.

  • cutoff (None or float) – Cutoff for which value of the ranking type that is considered significant. Default is None (all models will be ranked)

  • distribution ({‘disjoint’, ‘joint’, ‘explicit’, ‘same-as-iiv’}) – Which distribution added IOVs should have (default is same-as-iiv)

  • results (ModelfitResults) – Results for model

  • model (Model) – Pharmpy model

  • strictness (str or None) – Strictness criteria

  • E (float) – Expected number of predictors (used for mBIC). Must be set when using mBI

  • kwargs – Arguments to pass to tool

Returns:

IOVSearchResults – IOVSearch tool result object

Examples

>>> from pharmpy.modeling import load_example_model
>>> from pharmpy.tools import run_iovsearch, load_example_modelfit_results
>>> model = load_example_model("pheno")
>>> results = load_example_modelfit_results("pheno")
>>> run_iovsearch('OCC', results=results, model=model)
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