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  3. Nonlinear Modeling with Generalized Additive Models (GAMs) in R

Exercise

Pollution models with multi-scale interactions

The meuse dataset contains some predictor variables that are on the same scale (x, y), and some that are on different scales (elev, dist, om). In a previous exercise, you fit a model where you predicted cadmium pollution as a function of location and elevation: 

mod <- gam(cadmium ~ s(x, y) + s(elev), 
           data = meuse, method = "REML")

In this exercise, you'll build a model that allows multiple variables to interact despite these different scales using a tensor smooth, te().

Instructions

100 XP
  • Convert this to a model where x, y, and elev all interact in a single te() term, varying on their own scales.
  • Then summarize the model and visualize it with plot().