run_covsearch#

pharmpy.tools.run_covsearch(model, results, search_space, p_forward=0.01, p_backward=0.001, max_steps=-1, algorithm='scm-forward-then-backward', max_eval=False, adaptive_scope_reduction=False, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', naming_index_offset=0, nsamples=10, _samba_max_covariates=3, _samba_selection_criterion='bic', _samba_linreg_method='ols', _samba_stepwise_lcs=None, **kwargs)#

Run COVsearch tool. For more details, see COVsearch.

Parameters:

  • model (Model) – Pharmpy model

  • results (ModelfitResults) – Results of model

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

  • max_eval (bool) – Limit the number of function evaluations to 3.1 times that of the base model. Default is False.

  • adaptive_scope_reduction (bool) – Stash all non-significant parameter-covariate effects to be tested after all significant effects have been tested. Once all these have been tested, try adding the stashed effects once more with a regular forward approach. Default is False

  • strictness (str) – Strictness criteria

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

  • nsamples (int) – Number of samples from individual parameter conditional distribution for linear covariate model selection. Default is 10, i.e. generating 10 samples per subject

  • _samba_max_covariates (int or None) – Maximum number of covariate inclusion allowed in linear covariate screening for each parameter.

  • _samba_linreg_method (str) – Method used to fit linear covariate models. Currently, Ordinary Least Squares (ols), Weighted Least Squares (wls), and Linear Mixed-Effects (lme) are supported.

  • _samba_selection_criterion (str) – Method used for linear and nonlinear model selection in SAMBA methods. Currently, BIC and LRT are supported.

  • _samba_stepwise_lcs (bool or None) – Use stepwise linear covariate screening or not. By default, SAMBA methods use stepwise LCS whereas SCM-LCS uses non-stepwise LCS

  • 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(model=model, results=results, search_space=search_space)
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