Spatiotemporal multilevel models

General regression versus mixed-effects models

We will cover what is a Random Effect and how it differs from a Fixed effect. Some example syntax in R (on a secure website portal we have configured) and Stata will show how to get a handle on models that have random intercepts and additionally, random slopes.

We will spend a little time talking about how to partition variation and get estimates of the random and fixed effects. An important element will be our discussion of similarities between mixed-effects models with basic regression. There will also be brief discussion of extending the regression to non-gaussian responses (GLMERs).

Suggested workflow (run these scripts in this order)

• Get the data file simulated_data.csv
• intercepts_and_slopes_in_general_regression.R
• intercepts_and_slopes_in_mixed_effects.R
• general_regression_vs_mixed_effects.R
• Check out make_simulated_data.R to see how the data were constructed