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())
