Generalized Linear Models in R

Generalized Linear Models in R Workshop


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r-glmGeneralized Linear Models are a necessary tool in any data analyst’s toolbox.

They include a set of models that work when the distributional assumptions of linear models are not met, assumptions like normality and constant variance.

And since, with real data, those assumptions aren’t going to work all the time, you need to be able to run models that do work.

R’s glm() function runs a wide variety of generalized linear models.

  • Logistic and probit regression for binary, ordinal, and multicategory outcomes
  • Poisson and negative binomial regressions for count outcomes
  • Gamma regression for uncensored survival outcomes

The great flexibility and power of glm() to run so many models also means there’s a lot to learn.  Each type of model has specific issues that need to be checked and included in the analysis.

In this workshop, you’ll learn how to use glm() to specify each of these models and include the options specific to each one.

We will cover the topics at a relaxed pace, and you will have many opportunities to ask questions.

The example data sets are taken from education and the physical and biological sciences, but the ideas and syntax are applicable for creating graphs for every discipline, including business, the bio-medical sciences, psychology and the social sciences.

In this Workshop, you will learn:

Module 1: An Introduction to Generalized Linear Models

  • The glm() command
  • Odds and the Logit
  • Binary Outcome Variables (logistic regression)
  • The Dispersion Parameter
  • Fisher’s Scoring Algorithm
  • Plotting for each Predictor
  • Assessing Model Fit, Deviance, and Information Criteria

Module 2: GLMs with Continuous Outcomes

  • Modelling Proportion Data with Logistic Regression
  • Modelling Proportions with Categorical Predictors
  • Survival Analysis using Gamma Distributions
  • Fitting GLMs with Negative Binomial Errors

Module 3: Poisson Models

  • Regression on Count Data using Poisson Errors
  • A GLM with Poisson Errors and Categorical Predictors
  • A Model using Poisson Errors and Several Categorical Predictors

Module 4: Multinomial Logistic and Probit Models

  • Multinomial Logistic Regression
    • the multinom() function
  • Probit Regression with Binary Data
    • the pchisq() function


But can I keep up?

The workshop is pitched at a level that should make it of interest to both students and professionals.

You need prior experience in R if you want to be able to use what you’ve learned. However, the material should be accessible to those somewhat new to R.

You will need a background in generalized linear models.  The focus of this workshop is on using the software to run the models, not on what the models mean (though we will go through the output and discuss how to interpret them).

You do need to spend some time each week. You will learn concepts and get some clarity if you don’t practice what you’ve learned on your own. But you won’t entirely get it.

This is a workshop where you want to get your hands dirty with some data. Please expect to spend 2-3 hours per module doing the exercises.

This workshop is for you if you:

  • Have at least a basic understanding of generalized linear models and want to learn how to do them in R.
  • Have been using R, but would like to develop your R skills.
  • Have the time to really invest in learning. It will require about 4-6 hours per module all together.

It is not for you if you:

  • Have never before learned about generalized linear models.
  • Have never used R before.
  • Have no time to practice what you’ve learned.
  • Want to learn generalized linear mixed models in R–we’re not covering those here.


  • You will get the most out of the workshop if you have had at a minimum of statistics classes in linear and generalized linear models.
  • You will need to have R installed on your computer.
  • You should have some experience with R. You should have at least a basic understanding of how R handles objects, data, and how to input commands and read output.  It’s ideal if you’re familiar with linear models in R.


The workshop is offered as an interactive, live online workshop + workshop center website

Attend the workshop live via webinar. You will hear the instructor and see exactly what is happening on his screen. Ask questions through phone, microphone, or chat.

During the webinar sessions, the instructor will present concepts and explain the meaning of the techniques in that module, demonstrate how to implement those techniques in R on different examples, and answer your questions.

You also get video screenshot recordings of each workshop session made available within 48 hours. So you can review the material right away, a year later, or while doing the exercises. Or if you need to miss one (or all!) of the live sessions, you can still participate on your own schedule.

You will also have access to our workshop center website. The site is our home base for the workshop. It’s where you’ll find everything you need to support your learning of each module:

Text data files. These are real data files from real research projects. You get full access to use the data to try everything on your own.

Handouts with all the R code to run and explore all of the examples yourself. You won’t learn it unless you try it. So you’ll get my code so you can follow along.

Exercises. (Yes, homework!). You really need to practice this stuff. Get your hands dirty. So we’re giving you the data to try it on your own with new models to try out. But don’t worry–you won’t be on your own stuck on some coding error that won’t work. You’ll get the code to do the exercises and the answers in case you get stuck.

But best of all, you can submit questions. So if there’s something you thought was clear during the workshop, but isn’t now that you’re reviewing the explanatory material, just type in your question. David will answer quick questions right there on the website.

Bonus videos from some webinars on relevant topics to help jog your memory or clear up misunderstandings. Included are:

– A Review of Logarithms for the Data Analyst
– What Happened to R squared?: Assessing Model Fit for Logistic, Multilevel and Other Models that Use Maximum Likelihood
– Types of Regression Models and When to Use Them

You’ll have access for a year to this workshop membership site. So as we add resources, re-record sessions, and answer more questions each time we offer the workshop, you have access to the updated material.

The Instructor:


David LillisDavid Lillis has taught R to many researchers and statisticians.

His company, Sigma Statistics and Research Limited, provides both on-line instruction and face-to-face workshops on R, and coding services in R.

David holds a doctorate in applied statistics and is a frequent contributor to The Analysis Factor, including our blog series R is Not So Hard.

What Our Students Say About David’s Workshops:


“David was clear, knowledgeable, and responsive. Great handouts!”

– Anonymous

“For me, it was a good introduction to R, using simpler (well understood) statistical techniques that did not detract from the introduction to R.”

– Martin Watts, PhD

It was great to see someone using R ‘live’ to see how it works.

– Cate Bailey

The explanation was very clear, the topics were useful.

– Jose Hernandez

“Great format, great presenter.”

– Tom Bohon

“I am a new R user; It just gave me another motivation why I need to learn it further. Great work.”

– Essam Alshreafi

“Learned more efficient ways to parse data and evaluate the contents of a given data set. Very nice stuff! Overall, I thought this webinar was very well done. It was informative and moved at a great pace.”

– Anonymous

“More on R from David would be great. His tips were wonderful.”

– Gary Kitchen

Thanks for this, thought it was great and what a magnificent way to teach people stuff… keep up the good work!

– Joop van Eerbeek

“It was a very thorough set of workshops, a good pace, and it should prove to be an asset to my future data analysis projects.”

– David Scott

“I would recommend this course because it provides a proper introduction to R, paving the way for a self directed learning in R.”

– Mohammed Ahmed


Full Workshop Investment: $247

We’re sorry but this workshop is not currently enrolling.
Please join our waiting list to receive a notification when it is offered again.

Refund Policy: Your registration fee is fully refundable up to 72 hours in advance. Because enrollment is limited and you have access to all the materials to work through on your own, no refunds will be granted after the program begins.


As with all our programs, your satisfaction is guaranteed. If you participate fully in this workshop–watch, read, and try out everything included–and find you are not satisfied for any reason, we will give you a full refund, no questions asked.

Just notify us within 90 days of purchasing the program.