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  3. Generalized Linear Models in R
  • 1

    GLMs, an extension of your regression toolbox

    Free

    This chapter teaches you how generalized linear models are an extension of other models in your data science toolbox. The chapter also uses Poisson regression to introduce generalize linear models.

  • 2

    Logistic Regression

    This chapter covers running a logistic regression and examining the model outputs.

  • 3

    Interpreting and visualizing GLMs

    This chapter teaches you about interpreting GLM coefficients and plotting GLMs using ggplot2.

  • 4

    Multiple regression with GLMs

    In this chapter, you will learn how to do multiple regression with GLMs in R.

Initializing

Exercise

Methods for trend lines

During the previous exercise, you used ggplot2's default geom_smooth(). During this exercise, you will use a glm() instead. This will allow you to "see" a logistic regression with ggplot2. Specifically, you plot the probability someone takes the bus given their commute distance.

You will need to tell geom_smooth() to use the glm() method. Recall from Chapter 2 that the default family for a glm() is a Gaussian family, which produces the same results as a lm(). Thus, you will also need to specify the method argument from glm().
The code for creating gg_jitter, which you built in the last exercise, has been provided for you.

Instructions

100 XP
  • Use the "glm" method with geom_smooth().
  • With the method.args, set the family to 'binomial'.