Course Schedule
This 2-day intensive course covers essential multivariate statistical methods for biological and ecological research. Each session combines theoretical foundations, live R demonstrations, and hands-on exercises with real datasets.
Duration: 2 days, 09:00-16:30 each day
Format: In-person, hands-on computer lab
Prerequisites: Basic R knowledge (data frames, subsetting, plotting)
Preparation: See R Setup & Basics for installation instructions
Day 1: Exploration & Patterns (Unsupervised Methods)
Focus: Discovering patterns and structures in data without predefined groups
Schedule
| Time | Topic | Materials |
|---|---|---|
| 09:00-09:30 | Welcome & Course Overview | Slides |
| 09:30-10:30 | Correlation & Covariance + Exercise | Notes |
| 10:30-10:45 | ☕ Break | |
| 10:45-12:15 | Principal Component Analysis (PCA) + Exercise | Notes |
| 12:15-13:15 | 🍽️ Lunch Break | |
| 13:15-14:30 | Clustering Methods + Exercise | Notes |
| 14:30-14:45 | ☕ Break | |
| 14:45-16:15 | Ordination (NMDS/PCoA) + Exercise | Notes |
| 16:15-16:30 | Day 1 Wrap-up & Q&A |
Learning Objectives
By the end of Day 1, you will be able to:
- Understand and interpret correlation/covariance matrices
- Apply PCA to reduce dimensionality and visualize patterns
- Use hierarchical and k-means clustering to find groups
- Perform NMDS ordination on ecological/microbiome data
- Choose appropriate distance metrics for your data type
Day 2: Classification & Integration (Supervised Methods)
Focus: Predicting group membership and relating patterns to explanatory variables
Schedule
| Time | Topic | Materials |
|---|---|---|
| 09:00-10:30 | Linear Discriminant Analysis (LDA) + Exercise | Notes |
| 10:30-10:45 | ☕ Break | |
| 10:45-12:15 | Logistic Regression + Exercise | Notes |
| 12:15-13:15 | 🍽️ Lunch Break | |
| 13:15-14:30 | Constrained Ordination (RDA/CCA) + Exercise | Notes |
| 14:30-14:45 | ☕ Break | |
| 14:45-16:00 | Integration: Your Data + Method Selection | Decision Guide |
| 16:00-16:30 | Course Wrap-up & Advanced Topics |
Learning Objectives
By the end of Day 2, you will be able to:
- Classify samples using LDA and interpret discriminant functions
- Build and validate logistic regression models
- Use constrained ordination to relate community composition to environment
- Choose the appropriate method for your research question
- Apply methods to your own datasets
What to Bring
Required
- Laptop with R (≥4.0) and RStudio installed
- R packages installed (see R Setup & Basics)
- Power adapter (limited outlets available)
Course Materials
All materials are available through this website:
- Core Methods - Detailed notes for each topic
- Method Selection Guide - When to use which method
- R Companion - Code snippets and troubleshooting
- Going Further - Advanced topics for self-study
Materials will remain accessible after the course for your reference.
*See you in March!