aic = compute_model_selection_criteria(result_list=res_list, objective=obj, ms_criteria='AIC') # AICc not possible # compute bic along penalization strength bic = compute_model_selection_criteria(result_list=res_list, objective=obj, ms_criteria='BIC', n_data=21) # compute converted points conv_points = compute_converged_points(result_list=res_list) file_name_ms = str(n_lambda) + ',' + str(n_starts) save_modelSelection(reg_path=reg_path, lambda_range=lambda_range, n_lambda=n_lambda, n_sigma=0, par_names=model.getParameterIds()[:22], conv_points=conv_points, n_starts=n_starts, aic=aic, aicc=None, bic=bic, options=options, path=path_ms, file_name=file_name_ms)
# compute bic along penalization strength aicc = compute_model_selection_criteria(result_list=res_list, objective=obj, ms_criteria='AICC', n_data=6) # compute bic along penalization strength bic = compute_model_selection_criteria(result_list=res_list, objective=obj, ms_criteria='BIC', n_data=6) # compute converted points conv_points = compute_converged_points(result_list=res_list) file_name_ms = str(n_lambda) + ',' + str(n_starts) save_modelSelection(reg_path=reg_path, lambda_range=lambda_range, n_lambda=n_lambda, n_sigma=1, par_names=['$\\xi_1$', '$\\xi_2$', '$\\xi_3$', '$\sigma$'], conv_points=conv_points, n_starts=n_starts, aic=aic, aicc=aicc, bic=bic, options=options, path=path_ms, file_name=file_name_ms)