Multivariate and Other Methods

Multivariate data are common in the environmental sciences, occurring whenever we measure several response variables from each replicate sample (for example, the abundances of many species). These pages cover:

• Model-based analysis of multivariate abundance data with mvabund
• Grouping samples by their similarity with cluster analysis
• Summarising many variables into a few with principal components analysis
• Representing relationships among samples with multidimensional scaling

For other ways of plotting multivariate data, see Visualising multivariate data.

This part also covers generalised additive models (GAMs) for non-linear relationships, forecasting with time series, and meta-analysis:

• Meta-analyses 1: Introduction and calculating effect sizes
  
• Meta-analyses 2: Fixed effect and random effect models
  
• Meta-analyses 3: More complex models

Chapters in this part