Generalized linear models
GLMs are a class of models that expand upon basic linear regression to deal with non-normal response variables, and non-linear relationships between the predictor and response. If this application is too specific, go back and check out the earlier page, General Linear Models.
Instructional Videos:
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Additional Resources
StatsTree - Generalized Linear Models
Whitlock & Schluter - The Analysis of Biological Data
Chapter 17: pages 572-576 [Sapling]
Generalized linear models
Intro: Introduction to advanced statistical modeling.
Logistic regression in R
Intermediate: Code tutorial video for logistic regression in R.
Generalized Linear Models, McCullagh and Nelder (1989)
Advanced: GLM textbook.
GLMs: understanding the link function
Intro: Generalized Linear Models (‘GLMs’) are one of the most useful modern statistical tools, because they can be applied to many different types of data. … [Read More]
Example GLMs in R
Intermediate: Quick-R reference page for GLMs.
Review Questions
A data scientist collected neck length measurements from four different giraffe species. Initial data exploration showed that all standard linear regression assumptions were met. Write an “R formula” that models the relationship between the two variables- giraffe species, and neck length. Which R function would you use?
An ecologist exposed three-spined sticklebacks to hot water temperatures for five different durations (1, 2, 5, 10, or 25 minutes), and recorded whether each fish survived or not. Write an “R formula” that models the relationship between the two variables- duration, and survival. Which R function would you use?
A conservation biologist counted the number of individual seeds produced by monkey flowers from six different populations of Mimulus guttatus. Write an “R formula” that models the relationship between the two variables- population #, and the number of seeds. Which R function would you use? (hint: the word “counted” is supposed to be a cue…)
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