def setup(dm, keys):
""" Generate the PyMC variables for a multi-region/year/sex generic
disease model.
Parameters
----------
dm : dismod3.DiseaseModel
the object containing all the data, priors, and additional
information (like input and output age-mesh)
Results
-------
vars : dict of PyMC stochs
returns a dictionary of all the relevant PyMC objects for the
multi-region/year/sex generic disease model.
"""
vars = {}
# for each region-year-sex triple among the keys
for r in dismod3.gbd_regions:
for y in dismod3.gbd_years:
for s in dismod3.gbd_sexes:
key = dismod3.utils.gbd_key_for('%s', r, y, s)
if not key % 'prevalence' in keys:
continue
dm.set_units(key % 'prevalence', '(per person)')
dm.set_units(key % 'duration', '(years)')
for t in 'incidence', 'remission', 'excess-mortality':
dm.set_units(key % t, '(per person-year)')
#dm.get_initial_estimate(key%t, [d for d in dm.data if dm.relevant_to(d, t, r, y, s)])
data = [
d for d in dm.data if dm.relevant_to(d, 'all', r, y, s)
]
# Also include data that is not sex specific (i.e. sex == 'total') here
data += [
d for d in dm.data
if dm.relevant_to(d, 'all', r, y, 'total')
] # FIXME: this will double include data with sex == 'all'
sub_vars = generic_disease_model.setup(dm, key, data)
vars.update(sub_vars)
return vars
def setup(dm, keys):
""" Generate the PyMC variables for a multi-region/year/sex generic
disease model.
Parameters
----------
dm : dismod3.DiseaseModel
the object containing all the data, priors, and additional
information (like input and output age-mesh)
Results
-------
vars : dict of PyMC stochs
returns a dictionary of all the relevant PyMC objects for the
multi-region/year/sex generic disease model.
"""
vars = {}
# for each region-year-sex triple among the keys
for r in dismod3.gbd_regions:
for y in dismod3.gbd_years:
for s in dismod3.gbd_sexes:
key = dismod3.utils.gbd_key_for('%s', r, y, s)
if not key%'prevalence' in keys:
continue
dm.set_units(key%'prevalence', '(per person)')
dm.set_units(key%'duration', '(years)')
for t in 'incidence', 'remission', 'excess-mortality':
dm.set_units(key%t, '(per person-year)')
#dm.get_initial_estimate(key%t, [d for d in dm.data if dm.relevant_to(d, t, r, y, s)])
data = [d for d in dm.data if dm.relevant_to(d, 'all', r, y, s)]
# Also include data that is not sex specific (i.e. sex == 'total') here
data += [d for d in dm.data if dm.relevant_to(d, 'all', r, y, 'total')] # FIXME: this will double include data with sex == 'all'
sub_vars = generic_disease_model.setup(dm, key, data)
vars.update(sub_vars)
return vars
