net.new_row_covariate('r', np.int)[:] = r net.new_col_covariate('c', np.int)[:] = c c_model.fit = c_model.base_model.fit_conditional for cov_name in cov_names: c_model.base_model.beta[cov_name] = None c_model.fit(net, verbose=True) print 'NLL: %.2f' % c_model.nll(net) for cov_name in cov_names: print '%s: %.2f' % (cov_name, c_model.base_model.beta[cov_name]) print for rep in range(params['n_samples']): c_samples[rep, :, :] = c_model.generate(net, coverage=0.1) c_model.confidence_boot(net, n_bootstrap=params['n_bootstrap']) c_model.confidence_wald(net) for cov_name in cov_names: c_model.confidence_cons(net, cov_name, L=121, test='score') c_model.confidence_cons(net, cov_name, L=121, test='lr') display_cis(c_model) # Offset extreme substructure only for Nonstationary model net.offset_extremes() print 'Fitting nonstationary model' ns_model = NonstationaryLogistic() for cov_name in cov_names: ns_model.beta[cov_name] = None ns_model.fit(net) print 'NLL: %.2f' % ns_model.nll(net) print 'kappa: %.2f' % ns_model.kappa for cov_name in cov_names: print '%s: %.2f' % (cov_name, ns_model.beta[cov_name])
net.new_row_covariate('r', np.int)[:] = r net.new_col_covariate('c', np.int)[:] = c c_model.fit = c_model.base_model.fit_conditional for cov_name in cov_names: c_model.base_model.beta[cov_name] = None c_model.fit(net) print 'NLL: %.2f' % c_model.nll(net) for cov_name in cov_names: print '%s: %.2f' % (cov_name, c_model.base_model.beta[cov_name]) print for rep in range(params['n_samples']): c_samples[rep,:,:] = c_model.generate(net, coverage = 0.1) c_model.confidence_boot(net, n_bootstrap = params['n_bootstrap']) c_model.confidence_wald(net) for cov_name in cov_names: c_model.confidence_cons(net, cov_name, L = 121, test = 'score') c_model.confidence_cons(net, cov_name, L = 121, test = 'lr') display_cis(c_model) # Offset extreme substructure only for Nonstationary model net.offset_extremes() print 'Fitting nonstationary model' ns_model = NonstationaryLogistic() for cov_name in cov_names: ns_model.beta[cov_name] = None ns_model.fit(net) print 'NLL: %.2f' % ns_model.nll(net) print 'kappa: %.2f' % ns_model.kappa for cov_name in cov_names: print '%s: %.2f' % (cov_name, ns_model.beta[cov_name])
new.new_row_covariate('r', np.int)[:] = r new.new_col_covariate('c', np.int)[:] = c c_fit.fit = c_fit.base_model.fit_conditional c_fit.reset_confidence() c_fit.confidence_wald(new) wc_ci_l, wc_ci_u = safe_ci(c_fit, 'x_0', 'wald') if wc_ci_l < theta < wc_ci_u: wc_covered += 1 c_fit.confidence_boot(new, n_bootstrap = n_boot, alpha_level = alpha_level) bc_ci_l, bc_ci_u = c_fit.conf['x_0']['pivotal'] if bc_ci_l < theta < bc_ci_u: bc_covered += 1 c_fit.confidence_cons(new, 'x_0', alpha_level = alpha_level, L = 61, test = 'score') cs_ci_l, cs_ci_u = c_fit.conf['x_0']['conservative-score'] if cs_ci_l < theta < cs_ci_u: cs_covered += 1 c_fit.confidence_cons(new, 'x_0', alpha_level = alpha_level, L = 61, test = 'lr') cl_ci_l, cl_ci_u = c_fit.conf['x_0']['conservative-lr'] if cl_ci_l < theta < cl_ci_u: cl_covered += 1 print 'Wald (stationary): %.2f' % (1.0 * ws_covered / n_rep) print 'Wald (nonstationary): %.2f' % (1.0 * wn_covered / n_rep) print 'Wald (conditional): %.2f' % (1.0 * wc_covered / n_rep) print 'Bootstrap (stationary): %.2f' % (1.0 * bs_covered / n_rep) print 'Bootstrap (nonstationary): %.2f' % (1.0 * bn_covered / n_rep)