Application
Applications should be sent by e-mail to James Kurén Weldon (james.weldon@slu.se). The application should contain:
- Name
- Swedish “Personnummer”, if available. Else birth date
- Affiliation (including post address)
- Will take only Part 1 or the Full course (see below)
- A pdf (or corresponding) of a certificate stating that you are a PhD student.
We have 30 places on the course. Attendants will be accepted in the order we receive the applications.
Last day for application is 30 September 2024.
Post Doc researchers may attend the lectures if there is room. Send a corresponding application, but tell that you are a post doc researcher!
Location
The whole course will be on-line.
To minimise travels and allow participants from remote universities, the whole course will be online. During the pandemic we learned that this course works well with only remote teaching.
Time
The course is given in two parts: The first part involves on-line lectures and supervised computer exercises, and the second part involves supervised individual work with own data. Course participants may register for either only the first part, or both parts of the course.
- Part 1: 1 week, 21 October - 25 October 2024
- Part 2: 2 weeks, directly after part 1, i.e. 28 October-8 November 2024. Last day will be a compulsory seminar.
Eligibility
The course is open for PhD students. We cannot accept undergraduate students.
Prerequisites
A basic course in ordinary statistics is recommended. We also recommend that participants should be familiar with R and RStudio and be able to run R scripts, but programing experience in R is not necessary.
Students taking part 2 of the course must have own data to work with.
Aims
The course aims to illustrate the application of number of multivariate methods on ecological data. Following the course, participants should be familiar with different multivariate techniques, and how they can be applied on various types of data. The course will focus on developing an understanding of the application of multivariate techniques, with only a minimum amount of effort placed on comprehending the underlying mathematical details.
After the course, the students should be able to analyse multivariate ecological data, using R.
Contents
A number of ordination and classification procedures will be demonstrated, such as cluster analysis, correspondence analysis (CA), canonical correspondence analysis (CCA), redundancy analysis (RDA), principal components analysis (PCA), partial least square-analysis (PLS) and anosim. The course literature consists of 10 papers that will be available on the course web page.
Requirements for examination and Credits
- For part 1: Pass on all exercises during the first course week and pass on the home assignment. Presence at least at 80% of the lectures.
- For part 2: Approved oral and written report from the project.
Students will receive 3 ECTS credits for the first (lecture) part and 4.5 ECTS credits for completing the whole course.
Software
All assignments are based on R. There is no need for previous knowledge in R programming. There will be ready scripts for all exercises, which will be run in a cloud-based service requiring only a browser. However, we recommend installing R and R Studio on your computer to facilitate working on your own.
Fee
There is no course fee.
Teachers
Ulf Grandin, Martyn Futter and James Kurén Weldon.
