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run_covsearch#

pharmpy.tools.run_covsearch(search_space, p_forward=0.01, p_backward=0.001, max_steps=-1, algorithm='scm-forward-then-backward', results=None, model=None, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', naming_index_offset=0, **kwargs)#

Run COVsearch tool. For more details, see COVsearch.

Parameters:

  • search_space (str) – MFL of covariate effects to try

  • p_forward (float) – The p-value to use in the likelihood ratio test for forward steps

  • p_backward (float) – The p-value to use in the likelihood ratio test for backward steps

  • max_steps (int) – The maximum number of search steps to make

  • algorithm ({‘scm-forward’, ‘scm-forward-then-backward’}) – The search algorithm to use. Currently, ‘scm-forward’ and ‘scm-forward-then-backward’ are supported.

  • results (ModelfitResults) – Results of model

  • model (Model) – Pharmpy model

  • strictness (str or None) – Strictness criteria

  • naming_index_offset (int) – index offset for naming of runs. Default is 0

  • kwargs – Arguments to pass to tool

Returns:

COVSearchResults – COVsearch tool result object

Examples

>>> from pharmpy.modeling import load_example_model
>>> from pharmpy.tools import run_covsearch, load_example_modelfit_results
>>> model = load_example_model("pheno")
>>> results = load_example_modelfit_results("pheno")
>>> search_space = 'COVARIATE([CL, V], [AGE, WT], EXP)'
>>> res = run_covsearch(search_space, model=model, results=results)
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