WK-06 - Generalized linear models

Let us allow our response variable to be have non-normal errors

Workshop 6: Generalized linear models in R

A significant limitation of linear models is that they cannot accommodate response variables that do not have a normal error distribution. Most biological data do not follow the assumption of normality.

In this workshop, you will learn how to use generalized linear models, which are important tools to overcome the distributional assumptions of linear models.

You will learn the major distributions used depending on the nature of the response variables, the concept of the link function, and how to verify assumptions of such models.


Material

badge License: CC BY-NC-SA 4.0

Slides Book Script
English English English
Français Français Français

Archived written material is available on our wiki (EN, FR).


Contributors

This workshop was originally developed by Cédric Frenette Dussault, Vincent Fugère, Thomas Lamy, and Zofia Taranu.

Since 2014, several QCBS members contributed to consistently and collaboratively develop and update this workshop, as part of the Learning and Development Award from the Québec Centre for Biodiversity Science. They were:

2022 - 2021 - 2020 2019 - 2018 - 2017 2016 - 2015 - 2014
Pedro Henrique P. Braga Azenor Bideault Cédric Frenette Dussault
Katherine Hébert Willian Vieira Thomas Lamy
Alex Arkilanian Pedro Henrique P. Braga Zofia Taranu
Mathieu Vaillancourt Marie Hélène Brice Vincent Fugère
Kevin Cazelles
Marc-Olivier Beausoleil