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()`

.