GAM regression using ggplot2

To perform a Generalized Additive Model (GAM) regression using ggplot2, you can use the `gam()` function from the `mgcv` package and the `ggplot()` function from the `ggplot2` package.

Here is an example using the `mtcars` dataset:

library(mgcv)
library(ggplot2)

# Fit a GAM model
model <- gam(mpg ~ s(wt) + s(hp), data = mtcars)

# Create a data frame with the predicted values
newdata <- data.frame(wt = seq(min(mtcars$wt), max(mtcars$wt), length.out = 100),
                      hp = seq(min(mtcars$hp), max(mtcars$hp), length.out = 100))
newdata$mpg <- predict(model, newdata)

# Plot the predicted values
ggplot(newdata, aes(x = wt, y = mpg)) +
  geom_line() +
  labs(x = "Weight", y = "Miles per gallon") +
  theme_classic()

In this example, we fit a GAM model with `mpg` as the response variable and `wt` and `hp` as the predictors. We then create a data frame with 100 values for `wt` and `hp`, and use the `predict()` function to obtain the predicted values of `mpg`. Finally, we use `ggplot()` to create a line plot of the predicted values. We add labels to the x and y axes using `labs()` and use `theme_classic()` to change the theme of the plot.