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  3. Nonlinear Modeling with Generalized Additive Models (GAMs) in R
  • 1

    Introduction to Generalized Additive Models

    Free

    In this chapter, you will learn how Generalized additive models work and how to use flexible, nonlinear functions to model data without over-fitting. You will learn to use the gam() function in the mgcv package, and how to build multivariate models that mix nonlinear, linear, and categorical effects to data.

  • 2

    Interpreting and Visualizing GAMs

    In this chapter, you will take a closer look at the models you fit in chapter 1 and learn how to interpret and explain them. You will learn how to make plots that show how different variables affect model outcomes. Then you will diagnose problems in models arising from under-fitting the data or hidden relationships between variables, and how to iteratively fix those problems and get better results.

  • 3

    Spatial GAMs and Interactions

    In this chapter, you will extend the types of models you can fit to those with interactions of multiple variables. You will fit models of geospatial data by using these interactions to model complex surfaces, and visualize those surfaces in 3D. Then you will learn about interactions between smooth and categorical variables, and how to model interactions between very different variables like space and time.

  • 4

    Logistic GAMs for Classification

    In the first three chapters, you used GAMs for regression of continuous outcomes. In this chapter, you will use GAMs for classification. You will build logistic GAMs to predict binary outcomes like customer purchasing behavior, learn to visualize this new type of model, make predictions, and learn how to explain the variables that influence each prediction.

Initializing

Exercise

Parts of non-linear function

GAMs are made up of basis functions that together compose the smooth terms in models.

You can use the coef() function to extract the coefficients from the GAM model object.

After extracting the coefficients, answer the question:
How many coefficients are there for the basis functions that compose the smooth curve in this model?

Instructions 1/2

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Extract the model coefficients of the gam_mod object which is available in your workspace.