compute_covariance_matrix

(function from pyomo.contrib.parmest.parmest)

pyomo.contrib.parmest.parmest.compute_covariance_matrix(experiment_list, method, obj_function, theta_vals, step, solver, tee, estimated_var=None)[source]

Computes the covariance matrix of the estimated parameters using ‘finite_difference’ or ‘automatic_differentiation_kaug’ methods

Parameters:
  • experiment_list (list) – List of Experiment class objects containing the Pyomo model for the different experimental conditions

  • method (str) – Covariance calculation method specified by the user, e.g., ‘finite_difference’

  • obj_function (callable) – Built-in objective function selected by the user, e.g., SSE

  • theta_vals (dict) – Dictionary containing the estimates of the unknown parameters

  • step (float) – Float used for relative perturbation of the parameters, e.g., step=0.02 is a 2% perturbation

  • solver (str) – Solver name specified by the user, e.g., ‘ipopt’

  • tee (bool) – Boolean solver option to be passed for verbose output

  • estimated_var (float, optional) – Value of the estimated variance of the measurement error in cases where the user does not supply the measurement error standard deviation

Returns:

cov – Covariance matrix of the estimated parameters

Return type:

pd.DataFrame