Session Ready
Exercise

Chlamydia by age-group and county

The number of cases of infections can change through time and vary across age groups. Reason vary, but can include cultural, social, and policy-related reasons. For small populations, such as counties in rural America, these counts may likely include zero infections and be non-normal. In these cases, distributions such as the Poisson may be more appropriate for modeling than a normal distribution.

For this exercise, you will examine how chlamydia infections in small counties in Illinois vary. Specifically, you will examine two questions:

  1. Do the number of reported cases vary between people aged 15-19 compared to 20-24?
  2. Are the number of reported cases changing across time for these two age group?

This data comes from the State of Illinois who provides summaries of infections such as chlamydia by age groups and counties. First, you will fit a Poisson glmer to the data. Then, you examine the results. In the next exercise, you will plot the data.

Instructions 1/3
undefined XP
  • 1
  • 2
  • 3
  • Run a glmer() with "poisson" family error term, predicting count as a function of fixed effects age (1st fixed-effect) and year (2nd fixed-effect), and include year as a random-effect of county as well. Use the data ILdata.
  • Look at the summary() of the model.