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)