RJAGS simulation for Poisson regression

In the previous video we engineered a Poisson regression model of volume \(Y\)_i by weekday status \(X\)_i and temperature \(Z\)_i:

likelihood: \(Y\)_i \(\sim Pois(l\)_i) where \(log(l\)_i\() = a + b X\)_i \(+ c Z\)_i
priors: \(a \sim N(0, 200^2)\), \(b \sim N(0, 2^2)\), and \(c \sim N(0, 2^2)\)

Combining your insights from the observed RailTrail data and the priors stated here, you will define, compile, and simulate a posterior model of this relationship using RJAGS. To challenge yourself in this last RJAGS simulation of the course, you'll be provided with less helpful code than usual!

The RailTrail data are in your work space.

DEFINE your Bayesian model:

Use dpois() to define the likelihood model of Y[i] given l[i].
Define the prior models for a, b, c.
Store the model string as poisson_model.

Exercise

RJAGS simulation for Poisson regression

Instructions 1/3