DismodGroups Class¶

class cascade.model.DismodGroups¶

A DismodGroups contains Var instances or contains SmoothGrid instances. It gives them the shape of the whole model, so it expresses what rates are nonzero, what random effects are defined, and on which of these there are covariate multipliers.

The DismodGroups structure will appear in lots of places. The fit returned by Dismod will be a DismodGroups containing Var objects. The Model itself is a DismodGroups containing SmoothGrid objects.

A classic use of this is to create a new DismodGroups of Var. The first loop is over the rate, random effect, and covariate group names. The inner loop is over particular sets of keys, which are composed of tuples of the primary rate, covariate name, and location IDs.

var_groups = DismodGroups()
for group_name, group in var_ids.items():
    for key, var_id_mapping in group.items():
        var_groups[group_name][key] = var_builder(table, var_id_mapping)

rate[primary_rate]

This is a dictionary of rates. They are always one of the five underlying rates: iota, chi, omega, rho, pini:

dg = DismodGroups()
dg.rate["iota"] = Var([0, 1, 50], [2000])

random_effect[(primary_rate, child_location)]

A dictionary of random effects on the rates, so the keys are a rate and the ID of the child for which this is a random effect. When constructing a Model, we typically want to make one SmoothGrid of priors for all child random effects on a particular rate. In that case, specify the child ID as None:

model = Model()  # A Model is a DismodGroups object, too.
model.random_effect[("iota", None)] = SmoothGrid([0, 100], [1990])

scale = DismodGroups()
scale.random_effect[("omega", 2)] = Var([0, 100], [1990, 2000])
scale.random_effect[("omega", 3)] = Var([0, 100], [1990, 2000])

alpha[(covariate_name, rate_name)]: Alpha are covariate multipliers on the rates. The key is the name of the Covariate, which should match the name in the class given as an argument to the Session object. The rate name is one of the five underlying rates.

beta[(covariate_name, integrand_name)]: Beta are covariate multipliers on the measured value of the integrands. The integrand name is one of the canonical values.

gamma[(covariate_name, integrand_name)]: Gamma are covariate multipliers on the measured standard deviation of the integrands.