Let’s load the necessary packages:
We will use the “broken stick” approach to simulate data from the Dirichlet - trinomial model. This model assumes that the group proportions for each observation are Dirichlet, but the observed values are either 0, the total sample size (N) or a number between 0 and N.
Our broken_stick
function can be called as follows,
The object y
is a list with 2 elements, (1) the true
underlying compositions (p) and the realized data (X_obs). They can be
accessed as
By default, the simulation function assumes a uniform prior for the
Dirichlet, with hyperparameters = 1. We can change this by specifying
our own values of hyperparameters. Using the argument p
, we
can simulate new values with a slightly larger effective sample size,
and pass that into broken_stick