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
Exercise instructions
- Use the
head()
andstr()
functions to examine thempg
data set. - Fit a GAM to these data to predict
city.mpg
as the sum of smooth functions ofweight
,length
, andprice
. - 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)