ECTS credits: 1.5 ECTS
Course parameters:
Language: English
Level of course: PhD Course
Time of year: Autumn 2024 (10 – 12 December 2024)
Capacity limits: 30
Course fee: DKK 350
Objectives of the course:
The course will provide an applied introduction to generalized additive modelling in R for biologists. Most of the statistical methods you are likely to have encountered will have specified fixed functional forms for the relationships between covariates and the response, either implicitly or explicitly. These might be linear effects or involve polynomials, such as x + x2 + x3. Generalized additive models (GAMs) are different; they build upon the generalized linear model by allowing the shapes of the relationships between response and covariates to be learned from the data using splines. Modern GAMs are a general data analysis framework, encompassing many models as special cases, including GLMs and GLMMs, and the variety of splines available to users allows GAMs to be used in surprisingly large situations. In this course we’ll show you how to leverage the power and flexibility of splines to go beyond parametric modelling techniques like GLMs.
Learning outcomes and competences:
At the end of the course, the student should be able to:
Compulsory programme:
Active participation in the course including attendance at lectures and completion of computer-based classes and exercises. Completion of short, computer-based assessments testing their understanding of a topic and the practical skills taught. For credit, students must complete a data analysis exercise to be submitted one week after the end of the course (19 December).
Course contents:
The course is based on a series of lectures and computer-based practical classes led by an international expert in generalized additive modelling and who is the author of gratia, an R package for working with GAMs fitted using the mgcv package.
The course covers the following topics:
Prerequisites:
This course is suitable for Phd students (including senior thesis-based MSc students) and researchers working with biological data who want to fit models that allow for nonlinear relationships (effects) of covariates on responses. The course will be of particular interest to PhD candidates and researchers in inter alia biology, animal science, ecology, agriculture, and environmental science. Some prior knowledge of R is required, and some prior knowledge of generalized linear modelling in R would be an advantage.
Name of lecturer:
Assistant Professor Gavin Simpson, Department of Animal and Veterinary Sciences, Aarhus University gavin@anivet.au.dk
Literature:
Open access teaching resources prepared by the course leader will be supplemented by original literature (papers). Electronic copies of the open access teaching resources will be provided to each participant before the course starts.
Course homepage:
https://github.com/gavinsimpson/au-viborg-gam-course
Course assessment:
The course will be assessed through a data analysis exercise (take home) to be submitted by 19 December 2024.
Provider:
The course is provided by the Department of Animal and Veterinary Sciences, Aarhus University
Time:
3 days of teaching in a single block (10 – 12 December 2024). Classes are held from 09.30 to 16.00 each day.
Place:
The course will be taught at AU Campus Viborg
Course fee:
DKK 350
Registration:
Please send an e-mail to Julie Jensen, e-mail jsj@anivet.au.dk no later than 6 December 2024 to register.
If you have any questions, please contact course leader Assistant Professor Gavin Simpson, Department of Animal and Veterinary Sciences, Aarhus University, e-mail: gavin@anivet.au.dk