Navigate
Home Compendium
Exercises
Tidyverse packages
dplyr ggplot2 tidyr stringr lubridate purrr forcats readr broom Tidyverse R for Data Science
Deep dives
dplyr Joins dplyr Window Functions dplyr group_by ggplot2 Themes ggplot2 Facets ggplot2 Color Scales ggplot2 Bar Charts ggplot2 Heatmaps tidyr Pivot tidyr Nest/Unnest Regex
Wrangling & EDA
Data Wrangling Data Cleaning EDA Data Visualization data.table dbplyr / SQL Web Scraping API Calls
Statistics
Hypothesis Testing Linear Regression Logistic Regression Correlation Probability Distributions
Machine Learning
Machine Learning tidymodels Random Forest XGBoost Clustering PCA Cross-Validation
Time Series
Time Series ARIMA
By Industry
Finance Marketing Analytics Healthcare Biostatistics Genomics Sports Analytics A/B Testing
Reporting & Apps
Shiny R Markdown
Levels
R for Beginners R Interview Questions
R r‑statistics.co

GAM Exercises in R: 15 Practice Problems

Fifteen practice problems on Generalized Additive Models in R: mgcv, smooth terms, splines, interaction smooths, predict. Hidden solutions.

By Selva Prabhakaran  ·  Published May 11, 2026  ·  Last updated May 11, 2026
RRun this once before any exercise
library(mgcv) library(dplyr)

  

  





Exercise 1: Simple GAM



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
gam(mpg ~ s(wt), data = mtcars)

    

      
      
    

    

  

  








Exercise 2: Two smooth terms



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
gam(mpg ~ s(wt) + s(hp), data = mtcars)

    

      
      
    

    

  

  








Exercise 3: Linear + smooth



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
gam(mpg ~ wt + s(hp), data = mtcars)

    

      
      
    

    

  

  








Exercise 4: Set basis dimension



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
gam(mpg ~ s(wt, k = 5), data = mtcars)

    

      
      
    

    

  

  








Exercise 5: Tensor product (interaction smooth)



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
gam(mpg ~ te(wt, hp), data = mtcars)

    

      
      
    

    

  

  








Exercise 6: GAM with categorical



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
gam(mpg ~ s(wt) + factor(cyl), data = mtcars)

    

      
      
    

    

  

  








Exercise 7: Different smooth basis (cr)



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
gam(mpg ~ s(wt, bs = "cr"), data = mtcars)

    

      
      
    

    

  

  








Exercise 8: Smooth by group



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
gam(mpg ~ s(wt, by = factor(cyl)) + factor(cyl), data = mtcars)

    

      
      
    

    

  

  








Exercise 9: Plot a smooth



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
fit <- gam(mpg ~ s(wt), data = mtcars)
plot(fit)

    

      
      
    

    

  

  








Exercise 10: Summary



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
fit <- gam(mpg ~ s(wt) + s(hp), data = mtcars)
summary(fit)

    

      
      
    

    

  

  








Exercise 11: Predict new



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
fit <- gam(mpg ~ s(wt), data = mtcars)
predict(fit, newdata = data.frame(wt = c(2.5, 4)))

    

      
      
    

    

  

  








Exercise 12: Edf (effective df)



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
fit <- gam(mpg ~ s(wt), data = mtcars)
summary(fit)$edf

    

      
      
    

    

  

  








Exercise 13: Concurvity (GAM collinearity)



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
fit <- gam(mpg ~ s(wt) + s(hp), data = mtcars)
concurvity(fit)

    

      
      
    

    

  

  








Exercise 14: GAM for binary outcome



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
gam(am ~ s(mpg) + s(hp), data = mtcars, family = binomial)

    

      
      
    

    

  

  








Exercise 15: Check residuals



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
fit <- gam(mpg ~ s(wt), data = mtcars)
gam.check(fit)

    

      
      
    

    

  

  








What to do next



    


  • Linear-Regression-Exercises (shipped), linear baseline.
    • 


  • Mixed-Effects-Models-Exercises (shipped), hierarchical alternative.
    • 






          
Ready to earn the GAM Certificate?
The quiz is concept-based and respects your time: pass it once and your verifiable certificate is yours to share on LinkedIn, your resume, or your portfolio. Take it when you feel comfortable with the material.
Attempt the quiz→


        


        
      


      

        

        
© 2016-2026 Selva Prabhakaran.
          This work is licensed under the Creative Commons License.
        

        

          About ·
          RSS