# use_flag = False
# else:
# use_flag = ii # ii = 0 -> no covariates
# dm.params['covariates']['Study_level'][cv]['rate']['value'] = use_flag
# clear any fit and priors
dm.clear_fit()
dm.clear_empirical_prior()
dismod3.neg_binom_model.covariate_hash = {}
import fit_posterior
data = dm.data
fit_posterior.fit_posterior(dm,
region,
sex,
year,
map_only=False,
store_results=False)
dm.data = data # put data back in
results[ii] = dm
pl.figure(**book_graphics.quarter_page_params)
r = pl.array([
dm.value_per_1(d) for d in data
if dm.relevant_to(d, 'prevalence', region, year, sex)
])
n = [
d['effective_sample_size'] for d in data
if dm.relevant_to(d, 'prevalence', region, year, sex)
]
sorted_indices = r.argsort().argsort()
dm.clear_fit()
dm.clear_empirical_prior()
dismod3.neg_binom_model.covariate_hash = {}
return dm
models = []
dm = initialize_model()
dm.description = 'As loaded, but only GBR data, no covariates'
dm.data = [d for d in dm.data if dm.relevant_to(d, 'all-cause_mortality', region, year, sex)] + \
[d for d in dm.data if dm.relevant_to(d, 'prevalence_x_excess-mortality', region, year, sex)] + \
[d for d in dm.data if d.get('country_iso3_code') == 'GBR'] + \
[d for d in dm.data if dm.relevant_to(d, 'excess-mortality', 'all', 'all', 'all')]
dm.params['global_priors']['increasing']['excess_mortality'] = dict(age_start=25, age_end=100)
fit_posterior.fit_posterior(dm, region, sex, year, map_only=True, store_results=False)
models.append(dm)
dm = initialize_model()
dm.description = 'Without increasing prior on excess-mortality'
dm.data = [d for d in dm.data if dm.relevant_to(d, 'all-cause_mortality', region, year, sex)] + \
[d for d in dm.data if dm.relevant_to(d, 'prevalence_x_excess-mortality', region, year, sex)] + \
[d for d in dm.data if d.get('country_iso3_code') == 'GBR'] + \
[d for d in dm.data if dm.relevant_to(d, 'excess-mortality', 'all', 'all', 'all')]
fit_posterior.fit_posterior(dm, region, sex, year, map_only=True, store_results=False)
models.append(dm)
dm = initialize_model()
dm.description = 'With lower bound of .2 on excess-mortality (to encourage good convergence)'
dm.data = [d for d in dm.data if dm.relevant_to(d, 'all-cause_mortality', region, year, sex)] + \
[d for d in dm.data if dm.relevant_to(d, 'prevalence_x_excess-mortality', region, year, sex)] + \
pl.semilogy([1], [1])
Z = Y[Y["Rate type"] == "prevalence"].groupby("Age").apply(weighted_age)
pl.plot(Z.mean(1).__array__(), color="red", linewidth=3, alpha=0.5, label="Inconsistent NA/ME")
pl.legend()
pl.axis([-5, 130, 1e-6, 2])
import dismod3
dm = dismod3.load_disease_model(19807)
import fit_posterior
fit_posterior.fit_posterior(dm, "north_africa_middle_east", "male", "2005", map_only=True)
X = pandas.read_csv(
"/var/tmp/dismod_working/test/dm-19807/posterior/dm-19807-north_africa_middle_east-male-2005.csv", index_col=None
)
pl.figure()
for iso in list(pl.unique(X["Iso3"])):
pl.plot(X[(X["Iso3"] == iso)].filter(like="Draw").mean(1).__array__(), label=iso)
pl.semilogy([1], [1])
Z = X.groupby("Age").apply(weighted_age)
plot(Z.mean(1).__array__(), color="red", linewidth=3, alpha=0.5, label="Inconsistent NA/ME")
plot(
dm.vars["prevalence+north_africa_middle_east+2005+male"]["rate_stoch"].stats()["mean"],
dm.params['global_priors']['level_value']['prevalence']['age_before'] = 15
dm.params['global_priors']['level_value']['prevalence']['age_after'] = 50
dm.params['global_priors']['smoothness']['prevalence']['age_start'] = 15
dm.params['covariates']['Country_level']['LDI_id_Updated_7July2011']['rate']['value'] = 0
# clear any fit and priors
dm.clear_fit()
dm.clear_empirical_prior()
dismod3.neg_binom_model.covariate_hash = {}
import fit_posterior
data = dm.data
fit_posterior.fit_posterior(dm, region, sex, year, map_only=faster_run_flag, store_results=False)
dm.data = data # put data back in
results[grid] = dm
try:
results['dic_%s'%grid] = dm.mcmc.dic
except Exception, e:
print e
results['dic_%s'%grid] = 'TK'
pl.figure(**book_graphics.quarter_page_params)
for ii, grid in enumerate('abcd'):
dm = results[grid]
pl.subplot(1,4,ii+1)
dismod3.plotting.plot_intervals(dm, [d for d in dm.data if dm.relevant_to(d, 'prevalence', region, year, sex)],
color='black', print_sample_size=False, alpha=1., plot_error_bars=False,
# 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())
r = pl.array([dm.value_per_1(s) for s in prev_data])
min_rate_per_100 = '%d' % round(r.min()*100)
max_rate_per_100 = '%d' % round(r.max()*100)
median_rate_per_100 = '%d' % round(pl.median(r*100))
regions = pl.array([d['gbd_region'] for d in prev_data])
num_regions = len(pl.unique(regions))
import fit_world
#fit_world.fit_world(dm)
#dm.data = prev_data # put data back in
import fit_posterior
region = 'north_america_high_income'
sex = 'female'
year='2005'
fit_posterior.fit_posterior(dm, region, sex, year, map_only=faster_run_flag, store_results=False)
dm.data = prev_data # put data back in
pl.figure(**book_graphics.quarter_page_params)
pl.subplot(1,2,1)
dismod3.plotting.plot_intervals(dm, [d for d in dm.data if dm.relevant_to(d, 'prevalence', 'all', 'all', 'all')],
color='black', print_sample_size=False, alpha=.75, plot_error_bars=False,
linewidth=1)
pl.axis([10,60,-.01,1])
pl.yticks([0,.25,.5,.75])
pl.ylabel('Prevalence (per 1)')
pl.xlabel('Age (years)')
pl.title('a) All data')
pl.subplot(1,2,2)
pl.semilogy([1],[1])
Z = Y[Y['Rate type'] == 'prevalence'].groupby('Age').apply(weighted_age)
pl.plot(Z.mean(1).__array__(), color='red', linewidth=3, alpha=.5, label='Inconsistent NA/ME')
pl.legend()
pl.axis([-5,130,1e-6,2])
import dismod3
dm = dismod3.load_disease_model(19807)
import fit_posterior
fit_posterior.fit_posterior(dm, 'north_africa_middle_east', 'male', '2005', map_only=True)
X = pandas.read_csv('/var/tmp/dismod_working/test/dm-19807/posterior/dm-19807-north_africa_middle_east-male-2005.csv', index_col=None)
pl.figure()
for iso in list(pl.unique(X['Iso3'])):
pl.plot(X[(X['Iso3']==iso)].filter(like='Draw').mean(1).__array__(), label=iso)
pl.semilogy([1],[1])
Z = X.groupby('Age').apply(weighted_age)
plot(Z.mean(1).__array__(), color='red', linewidth=3, alpha=.5, label='Inconsistent NA/ME')
plot(dm.vars['prevalence+north_africa_middle_east+2005+male']['rate_stoch'].stats()['mean'], color='red', linewidth=3, alpha=.5, label='Mean of Consistent NA/ME')
pl.legend()
# 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())
