SmoothGrid Class¶
A SmoothGrid represents model priors (as opposed to data priors) in
a Dismod-AT model. A Model is a bunch of
SmoothGrids, one for each rate, random effect, and covariate multiplier.
For instance, in order to set priors on underlying incidence rate, iota, create a SmoothGrid, set its priors, and add it to the Model:
smooth = SmoothGrid([0, 5, 10, 50, 100], [1990, 2015])
smooth.value[:, :] = Uniform(mean=0.01, lower=1e-6, upper=5)
smooth.dage[:, :] = Gaussian(mean=0, standard_deviation=10)
smooth.dtime[:, :] = Gaussian(mean=0, standard_deviation=0.1)
All of the priors in a SmoothGrid need to be defined. There is a value prior at each age and time, but the prior for difference in age and time are forward differences, so there is no prior for the largest age point and largest time point. That means you’ll notice examples with no dtime priors when the underlying grid is defined for only one year.
If you want more control over exact priors, iterate over them. The age_time_diff iterator returns the age and time at the age points but also the difference in age and time to the next age point:
for age, time, age_diff, time_diff in smooth.age_time_diff():
if not isinf(age_diff):
smooth.dage[age, time] = \
Gaussian(mean=0, standard_deviation=1 + 5 * age_diff)
This would change the standard deviation as the age interval changes, which could be helpful when age intervals change greatly. The check for isinf catches the last age difference, which returns the value inf because there is no next age point.
It is also possible to see what priors are set. This gets the prior at each age and time. Then it sets a new value for the prior with twice-as-large a standard deviation but the same density:
for age, time in smooth.age_time():
prior = smooth.value[age, time]
print(f"prior mean {prior.mean} std {prior.standard_deviation}")
smooth.value[age, time] = prior.update(standard_deviation=2 * prior.standard_deviation)