# Fiches de cours

## Statistiques multivariables avec R

#### Enseignant(s) :

Buttler Alexandre
Freund Lucas Georges Jean-Paul

English

#### 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 et 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

40 % spot written control (2h) during the semester
10 % continuous control (exercises) during the semester
50 % oral exam (30 min) during exam session

#### 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)

##### Notes/Handbook

Available on Moodle.epfl.ch

Master project

### Semaine de référence

LuMaMeJeVe
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
En construction

Cours
Exercice, TP
Projet, autre

### légende

• Semestre d'automne
• Session d'hiver
• Semestre de printemps
• Session d'été
• Cours en français
• Cours en anglais
• Cours en allemand