Interactive Course

Generalized Linear Models in R

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

4 hours
14 Videos
51 Exercises
6,588 Participants
4,050 XP

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Course Description

Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions including binomial and count data. Throughout this course, you will expand your data science toolkit to include GLMs in R. As part of learning about GLMs, you will learn how to fit model binomial data with logistic regression and count data with Poisson regression. You will also learn how to understand these results and plot them with ggplot2.

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.

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

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

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

1
GLMs, an extension of your regression toolbox
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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.
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Logistic Regression

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

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

In this chapter, you will learn how to do multiple regression with GLMs in R.
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Richard helps people to experience and understand their increasingly numerical world. For his day job he develops new quantitative methods for monitoring and controlling invasive species as well as helping other scientists analyze and understand their data. He has worked on diverse datasets ranging from continent wide species distributions to pesticides in playa wetlands. After hours, he teaches SCUBA Diving as a NAUI Instructor. He has been a member of "UserR" since 2007.

Collaborators

Chester Ismay
David Campos
Shon Inouye

Prerequisites

Multiple and Logistic Regression in R

Datasets

Bus Commuter dataset

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Generalized Linear Models in R

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Course Description

GLMs, an extension of your regression toolbox

Limitations of linear models

Assumptions of linear models

Refresher on fitting linear models

Poisson regression

Fitting a Poisson regression in R

Comparing linear and Poisson regression

Intercepts-Comparisons versus means

Basic lm() functions with glm()

Applying summary(), print(), and tidy() to glm

Extracting coefficients from glm()

Predicting with glm()

Interpreting and visualizing GLMs

Poisson regression coefficients

Poisson link

lm vs. Poisson coefficients

Plotting Poisson regression

Poisson regression plotting

Understanding output from logistic regression

Understanding odds ratios

Extracting and interpreting odds-ratios

Odds-ratios & confidence intervals in the Tidyverse

ggplot2 and binomial regression

Default trend lines

Methods for trend lines

Comparing probits and logits

Logistic Regression

Overview of logistic regression

Fitting a logistic regression

Examining & interpreting logistic regression outputs

Bernoulli versus binomial distribution

Bernoulli versus binomial

Simulating binary data

Long-form logistic regression input

Wide-form input logistic regression

Comparing logistic regression outputs

Link functions-Probit compared to logit

Probit versus logit

Fitting probits and logits

Simulating a logit

Simulating a probit

Multiple regression with GLMs

Multiple logistic regression

Fitting a multiple logistic regression

Building two models

Comparing regression outputs

Comparing variable order

Formulas in R

Multiple slopes

Intercepts

Multiple intercepts

Assumptions of multiple logistic regression

Simpson's paradox

Non-linear logistic regression

Conclusion