calculate_bic#

pharmpy.modeling.calculate_bic(model, likelihood, type='mixed')[source]#

Calculate BIC

Different variations of the BIC can be calculated:

  • mixed (default)
    BIC = -2LL + n_random_parameters * log(n_individuals) +
    n_fixed_parameters * log(n_observations)
  • fixed
    BIC = -2LL + n_estimated_parameters * log(n_observations)
  • random
    BIC = -2LL + n_estimated_parameters * log(n_individuals)
  • iiv
    BIC = -2LL + n_estimated_iiv_omega_parameters * log(n_individuals)
Parameters:

  • model (Model) – Pharmpy model object

  • likelihood (float) – -2LL to use

  • type ({‘mixed’, ‘fixed’, ‘random’, ‘iiv’}) – Type of BIC to calculate. Default is the mixed effects.

Returns:

float – BIC of model fit

Examples

>>> from pharmpy.modeling import *
>>> from pharmpy.tools import load_example_modelfit_results
>>> model = load_example_model("pheno")
>>> results = load_example_modelfit_results("pheno")
>>> ofv = results.ofv
>>> calculate_bic(model, ofv)
611.7071686216575
>>> calculate_bic(model, ofv, type='fixed')
616.5366069867251
>>> calculate_bic(model, ofv, type='random')
610.741280948644
>>> calculate_bic(model, ofv, type='iiv')
594.4311311730211
On this page