Unit 10

Introduction

Welcome to Week 10 of BIO144!

Course book

This week we will be covering Chapter 10: Ordination and multivariate data of the course book.

Code walkthrough

The R code of this week’s chapter is in a code walkthrough script, with a video walkthrough of it. Both are linked at the top of the course book chapter. Work through them before the practical: the last section of the script (“Practical toolbox”) shows code that this week’s practical uses but the chapter does not.

Learning objectives

These learning objectives, together with the content of the course book chapter, define what you need to know for the exam.

After this unit, students will be able to:

  1. Recognise when a biological question involves many response variables, and explain the limitations of analysing each one separately.
  2. Reshape multivariate data between wide and long format, and explain which format is needed for which task.
  3. Explain the aim of ordination: to summarise similarity among observations measured on many variables using a few axes.
  4. Explain why variables are centred, and usually scaled, before a PCA.
  5. Use R to perform a principal component analysis (PCA) and obtain the proportion of variation explained by each axis.
  6. Interpret PCA loadings and biplots to identify which variables contribute most to each axis.
  7. Choose a distance measure for NMDS appropriate to the data (e.g. Euclidean distance on scaled numeric variables).
  8. Use R to perform a non-metric multidimensional scaling (NMDS) and obtain its stress value.
  9. Interpret ordination plots in terms of similarities and differences among observations.
  10. Distinguish PCA from NMDS, and choose between them for a given dataset.
  11. Evaluate what an ordination does and does not show (e.g. it summarises patterns but does not by itself test hypotheses; tests need separate methods).
  12. Choose an appropriate approach to test whether groups differ in multivariate composition (MANOVA, PERMANOVA) and in multivariate spread (PERMDISP), and to relate an explanatory variable to an ordination (envfit, ordisurf).
  13. Use principal components as explanatory variables in a regression, as one way to deal with collinearity.

Lecture

Please attend the lecture. If for any reason you can’t, please watch the podcast, which is available on OLAT.

Homework, Practical, and Weekly Quiz

Available here. You will need to be on a UZH network to access this link, so if you are not on campus, you will need to connect to the UZH VPN first.