Syllabus

Course Learning Goals

  1. Give graduate students in agriculture, ecology, and related sciences an overview of multivariate datasets and their analyses.
  2. Develop proficiency in coding and conducting statistical analyses in R.
  3. Prepare students for further individual learning according to their specific needs.
  4. Promote collaborative discovery and resourcefulness.

Topics Covered

See the Schedule for week-by-week details.

Week Topic Key Packages
1 R intro: objects, vectors, data frames, basic ggplot2 ggplot2
2 Linear regression: lm(), residuals, predictions, R², F-statistics, interactions ggplot2, plotly
3 General/Generalized linear models (GLM) base R
4 Nonlinearity, GAMs, writing functions mgcv
5 Model selection: ANOVA-based forward selection, AIC/BIC, mixed effects MuMIn, MASS
6 Regularization (Ridge, Lasso, Elastic Net), cross-validation, AUC glmnet
7 PCA and dimensionality reduction reshape2, data.table
8 Clustering (K-means, hierarchical), apply functions base R
9 MANOVA, random forests, neural nets, transformations caret, randomForest, neuralnet
10 Structural equation modeling (SEM), redundancy analysis (RDA) lavaan, piecewiseSEM, vegan

Course Organization

  • Lectures (Mon/Wed 1:10–2:30 PM): Background presentation + live coding tutorial. Bring your laptop.
  • Discussion (Fri 12:10–1:00 PM): Problem set Q&A, bonus material, catch-up.
  • Zoom: Available for all sessions. Recordings posted within 24hrs (best-effort; attend in person when possible).

Assignments & Grading

10 weekly homework sets + 1 final assignment. Submitted through Canvas.

Grading scale: Standard UC Davis A–F scale.

Textbooks & Resources

No required textbook. Recommended free resources:

Policies

  • Attendance: In-person attendance strongly encouraged; Zoom available but not guaranteed.
  • Reproducibility: All submitted R code must use set.seed() before any random operations.
  • Academic integrity: Collaboration encouraged on problem sets; submitted work must be your own.