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Setting complexity of the motorcycle model

The number of basis functions in a smooth has a great impact on the shapes a model can take. Here, you'll practice modifying the number of basis functions in a model and examining the results.

Diese Übung ist Teil des Kurses

Nonlinear Modeling with Generalized Additive Models (GAMs) in R

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Anleitung zur Übung

  • Fit a GAM with 3 basis functions to the mcycle data, with accel as a smooth function of times.
  • Fit the same GAM again, but this time with 20 basis functions.
  • Use the provided plot() functions to visualize both models.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

library(mgcv)

# Fit a GAM with 3 basis functions
gam_mod_k3 <- gam(accel ~ s(times, k = ___), data = mcycle)

# Fit with 20 basis functions
gam_mod_k20 <- gam(___)

# Visualize the GAMs
par(mfrow = c(1, 2))
plot(gam_mod_k3, residuals = TRUE, pch = 1)
plot(gam_mod_k20, residuals = TRUE, pch = 1)
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