Multivariate statistics with R in environment
ENV-521 / 4 credits
Teacher:
Language: English
Remark: pas donné en 2021-22
Summary
Introduction to multivariate data analysis and modelling. The course helps for a critical choice of methods and their integration in a research planning. It prepares for complexe data analysis in various fields of environemental sciences. Use of dedicated R libraries
Content
- Biological and environmental descriptors, multidimensional data, coding and transformation
- Resemblance and dependence measures, association matrices
- Analysis of discontinuities: unsupervised clustering techniques
- Analysis of discontinuities: supervised clustering, regression and classification trees
- Gradient analysis: ordination techniques in reduced space (PCA, CA, PCoA, NMDS)
- Direct gradient analysis: symmetric coupling of tables (COIA, MFA)
- Direct gradient analysis: constrained ordination (RDA, CCA, pRDA, pCCA, db-RDA)
- Statistical tests for multivariable responses
Keywords
Multivariable analysis, statistics for complexe data sets
Learning Prerequisites
Recommended courses
Probabilities and statistics
Experimental Design and Data Analysis with R" (EDDAR - ENG 467)
Learning Outcomes
By the end of the course, the student must be able to:
- Select appropriately methods for data analysis knowing the basic principles of calculation in the field of their application
- Construct a plan for data analysis
- Interpret properly the results given by the different methods
- Apply the methods with exercices and a personal project
- Work out / Determine means for combining data from two or more independant data sets describing the same objects and test the relationship
Teaching methods
Lecture and exercises on computer, personel project for applying methods.
Expected student activities
Participating at the lecture and reading the hand-out
Applying the various methods with the exercices and provided data set
Personal project with report and defense
Assessment methods
50 % project report during the semester
50 % oral exam (30 min) during exam session on the personal project
Supervision
| Office hours | Yes |
| Assistants | Yes |
Resources
Bibliography
BIBLIOGRAPHY
Legendre, P., & Legendre, L. (2012) Numerical Ecology. 3e ed., Elsevier ***
Jongman, R.H.G, Ter Braak, C.J.F. & Van Tongeren, O.F.R. (1987) Data analysis in community and landscape ecology. PUDOC, Wageningen
Borcard, D., Gillet, F. & Legendre, P. (2011) Numerical Ecology with R. Springer Verlag.*
*** for theory and fundamental concepts
* to work with R (codes)
Ressources en bibliothèque
- Numerical Ecology / Legendre
- Data analysis in community and landscape ecology / Jongman
- Numerical Ecology with R /Borcard
Notes/Handbook
Available on Moodle.epfl.ch
Websites
Moodle Link
Prerequisite for
Master project
In the programs
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Multivariate statistics with R in environment
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Multivariate statistics with R in environment
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Exam form: Oral (winter session)
- Subject examined: Multivariate statistics with R in environment
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
Reference week
| Mo | Tu | We | Th | Fr | |
| 8-9 | |||||
| 9-10 | |||||
| 10-11 | |||||
| 11-12 | |||||
| 12-13 | |||||
| 13-14 | |||||
| 14-15 | |||||
| 15-16 | |||||
| 16-17 | |||||
| 17-18 | |||||
| 18-19 | |||||
| 19-20 | |||||
| 20-21 | |||||
| 21-22 |
Légendes:
Lecture
Exercise, TP
Project, other