Feb 26-27, 2018
We will continue our exploration of GLMs this week, looking at how GLM’s can be applied to data expressed as zeroes and ones, proportions, rates, and more.
This lecture will be a bit different than others. We will cover many topics in very brief detail. But each of these topics could be its own lecture. Consequently, I will ‘name-drop’ several techniques, simply state what they are and when they might be used, and encourage self-study.
- GLM’s for data expressed as zeroes and ones, proportions, and rates
- Contrast Bernoulli, Binomial, and Beta regression
- In brief: Multinomial and cumulative logistic regression (i.e. what to do when Y is categorical)
- Simulating from the model as a validation step
As always, this class will have a mixture of R code and lecture slides
Beta regression:… excuse the similar-sounding names. Each of the below articles comes at it a bit differently.
- Mangiafico: Beta regression for percent and proportion data
- Zeileis et al: Beta regression in R
- Prabhakaran: Beta regerssion with R
- Cribari-Neto and Zeileis: Beta regression in R
We will use data from the below paper for one exercise. Read if you like:
- Porsmoguer, S. B., Bănaru, D., Boudouresque, C. F., Dekeyser, I., & Almarcha, C. (2015). Hooks equipped with magnets can increase catches of blue shark (Prionace glauca) by longline fishery. Fisheries research, 172, 345-351.
A good reference for how to run various types of glm in R:
Slides available on Speakerdeck