dm.params['global_priors']['level_bounds']['remission'] = dict(lower=0., upper =.01) dm.params['global_priors']['level_bounds']['excess_mortality'] = dict(lower=0., upper =.01) dm.params['global_priors']['level_bounds']['relative_risk'] = dict(lower=1., upper=1000.) ### @export 'initialize model data' region = 'north_america_high_income' year = 1990 dm.data = [d for d in dm.data if dm.relevant_to(d, 'all', region, year, 'all')] # fit model dm.clear_fit() dm.clear_empirical_prior() dismod3.neg_binom_model.covariate_hash = {} import fit_world fit_world.fit_world(dm, generate_diagnostic_plots=False, store_results=False, map_only=False) models[ii] = dm #results[ii] = dict(rate_stoch=dm.vars['prevalence+world+all+all']['rate_stoch'].stats(), dispersion=dm.vars['prevalence+world+all+all']['dispersion'].stats()) ### @export 'save' for d in dm.data: d['sex'] = 'male' # otherwise tile plot shows it twice reload(book_graphics) book_graphics.plot_age_patterns(dm, region='world', year='all', sex='all', rate_types='remission incidence prevalence'.split()) pl.subplot(1,3,1) pl.yticks([0, .04, .08], [0,4,8]) pl.ylabel('Rate (Per 100 PY)')
dm.params['covariates']['Study_level']['bias']['rate']['value'] = 0 for cv in dm.params['covariates']['Country_level']: dm.params['covariates']['Country_level'][cv]['rate']['value'] = 0 # TODO: set bounds on remission and excess-mortality in the second time through ### @export 'initialize model data' region = 'north_america_high_income' year = 1990 dm.data = [d for d in dm.data if dm.relevant_to(d, 'prevalence', region, year, 'all')] # fit model dm.clear_fit() dm.clear_empirical_prior() dismod3.neg_binom_model.covariate_hash = {} import fit_world fit_world.fit_world(dm) models[ii] = dm results[ii] = dict(rate_stoch=dm.vars['prevalence+world+all+all']['rate_stoch'].stats(), dispersion=dm.vars['prevalence+world+all+all']['dispersion'].stats()) ### @export 'save' for d in dm.data: d['sex'] = 'male' # otherwise tile plot shows it twice dismod3.plotting.tile_plot_disease_model(dm, dismod3.utils.gbd_keys(['incidence', 'remission', 'excess-mortality', 'prevalence'], ['world'], ['all'], ['all']), plot_prior=False, print_sample_size=False) pl.show() book_graphics.save_json('hep_c.json', vars())
# set expert priors and other model parameters dm.params['global_priors']['level_value']['incidence']['age_before'] = 10 dm.params['global_priors']['level_value']['incidence']['age_after'] = 99 #dm.params['global_priors']['smoothness']['incidence']['age_start'] = 10 dm.params['global_priors']['level_bounds']['remission']['upper'] = .05 dm.params['global_priors']['level_value']['prevalence']['age_before'] = 10 dm.params['global_priors']['smoothness']['relative_risk']['amount'] = 'Very' dm.params['covariates']['Country_level']['LDI_id_Updated_7July2011']['rate'][ 'value'] = 0 dm.params['covariates']['Study_level']['cv_past_year']['rate']['value'] = 1 # clear any fit and priors dm.clear_fit() dm.clear_empirical_prior() dismod3.neg_binom_model.covariate_hash = {} # initialize model data #dismod3.neg_binom_model.fit_emp_prior(dm, 'prevalence') import fit_world fit_world.fit_world(dm) import fit_posterior fit_posterior.fit_posterior(dm, 'north_america_high_income', 'female', '2005') ### @export 'save' book_graphics.save_json('bipolar.json', vars())
### @export 'initialize model data' region = 'north_america_high_income' year = 1990 dm.data = [ d for d in dm.data if dm.relevant_to(d, 'all', region, year, 'all') ] # fit model dm.clear_fit() dm.clear_empirical_prior() dismod3.neg_binom_model.covariate_hash = {} import fit_world fit_world.fit_world(dm, generate_diagnostic_plots=False, store_results=False, map_only=False) models[ii] = dm #results[ii] = dict(rate_stoch=dm.vars['prevalence+world+all+all']['rate_stoch'].stats(), dispersion=dm.vars['prevalence+world+all+all']['dispersion'].stats()) ### @export 'save' for d in dm.data: d['sex'] = 'male' # otherwise tile plot shows it twice reload(book_graphics) book_graphics.plot_age_patterns( dm, region='world', year='all', sex='all', rate_types='remission incidence prevalence'.split())