pharmpy.modeling.results module¶
- pharmpy.modeling.results.calculate_individual_parameter_statistics(model, exprs, seed=None)[source]¶
Calculate statistics for individual parameters
Calculate the mean (expected value of the distribution), variance (variance of the distribution) and standard error for individual parameters described by arbitrary expressions. Any dataset column or variable used in the model can be used in the expression. The exception being that variables that depends on the solution of the ODE system cannot be used. If covariates are used in the expression the statistics of the parameter is calculated at the median value of each covariate as well as at the 5:th and 95:th percentiles.
- Parameters
model (Model) – A previously estimated model
exprs (str, sympy expression or iterable of str or sympy expressions) – Expressions or equations for parameters of interest. If equations are used the names of the left hand sides will be used as the names of the parameters.
seed (int or numpy rng)
- Returns
pd.DataFrame – A DataFrame of statistics indexed on parameter and covariate value.
- pharmpy.modeling.results.calculate_individual_shrinkage(model)[source]¶
Calculate the individual eta-shrinkage
Definition: ieta_shr = (var(eta) / omega)
- pharmpy.modeling.results.calculate_pk_parameters_statistics(model, seed=None)[source]¶
Calculate statistics for common pharmacokinetic parameters
Calculate the mean (expected value of the distribution), variance (variance of the distribution) and standard error for some individual pre-defined pharmacokinetic parameters.
- Parameters
model (Model) – A previously estimated model
seed (int or numpy rng)
- Returns
pd.DataFrame – A DataFrame of statistics indexed on parameter and covariate value.
See also
calculate_individual_parameter_statisticsCalculation of statistics for arbitrary parameters