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Multivariate GAMs of auto performance

GAMs can accept multiple variables of different types. In the following exercises, you'll work with the mpg dataset available in the gamair package to practice fitting models of different forms.

This exercise is part of the course

Nonlinear Modeling with Generalized Additive Models (GAMs) in R

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Exercise instructions

  • Use the head() and str() functions to examine the mpg data set.
  • Fit a GAM to these data to predict city.mpg as the sum of smooth functions of weight, length, and price.
  • Use the plot() function provided to visualize the model.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

library(mgcv)

# Examine the data
___
___

# Fit the model
mod_city <- gam(city.mpg ~ ___, 
                data = mpg, method = "REML")

# Plot the model
plot(mod_city, pages = 1)
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