The Multivariate Statistics for Wildlife and Ecology Research (NRC 631) Lab includes several major projects corresponding to the major statistical techniques covered in this course (see Schedule). Each of these projects are briefly described below and include links to detailed descriptions of each assignment and downloads of all required data and scripts. All documents in pdf format require Adobe Acrobat Reader to view.

Mvstats.R is suite of R functions written to facilitate the analysis of multivariate data sets in this course. This suite of functions is written in the spirit of growing up to become a fully fledged library in R, but for now it is just a humble set of functions that must be sourced before using. This library will be updated constantly, possibly for each lab, so it will need to be downloaded and sourced each lab. In addition, the corresponding help file contains all the supporting documentation for the functions and is written in the spirit of the standard R function help pages (for better or worse).

• mvstats.R (R program)
• mvstats.pdf (help file)
• cartware.R (R program)
• cartware.pdf (help file)

The first lab will introduce students to the R programming and statistical computing environment. Students will gain only a preliminary working understanding of how to work in R. The purpose of this lab is to give students enough understanding and working skills to successfully complete the remaining lab projects, not teach students to become R experts.

• Lab assignment
• Project data

• Lab assignment
• Project data

This project is designed to give students hands-on experience using a variety of unconstrained ordination techniques, including Principal Components Analysis (PCA), Correspondence Analysis (CA), Detrended Correspondence Analysis (DCA), Principal Coordinates Analysis (PCO) and Nonmetric Multidimensional Scaling (NMDS) to analyze a multivariate data set. As such, students will not be expected to analyze the data set from every possible angle and using every possible analytical procedure available. Rather, the purpose of this project is to give students some basic experience in how unconstrained ordination techniques work and how R an be used to do the analysis.

• Project 1 assignment
• PCA lab exercise
• CA/DCA/NMDS exercise
• Ordination Rscripts

This project is designed to give students hands-on experience using Cluster Analysis (CA) to analyze a multivariate data set. As such, students will not be expected to analyze the data set from every possible angle and using every possible analytical procedure discussed in class. Rather, the purpose of this project is to give students some basic experience in how CA works and how R can be used to do the analysis.

• Project 2 assignment
• Cluster lab exercise
• Project data
• Cluster Rscript

• Project 3 assignment
• Project Data Sets:
  • Wood turtle habitat selection
  • Warbling verio habitat selection
  • Wetlands classification
  • Herring morphometrics
  • Frosted elfin larvae habitat selection
• Lab exercises:
  • MRPP/ANOSIM/MANTEL lab exercise
  • DA lab exercise
  • CART lab exercise
• R scripts:
  • MRPP/ANOSIM/MANTEL Rscript
  • DA Rscript
  • CART Rscript

This project is designed to give students hands-on experience using constrained ordination (CCA/RDA/CAP) to analyze a multivariate data sets. Specifically, students will learn how to conduct Canonical Correspondence Analysis (CCA) or Redundancy Analysis (RDA) using R. Constrained Analysis of Principal Coordinates (CAP) is similar but will not be done here. Students will learn how to screen and adjust the data set (e.g., select species and explanatory variables) and conduct the constrained ordination. In addition, students will learn how to evaluate the performance of the model and interpret the relationships among species, samples and environmental variables. Finally, students will learn how to do a simple 2- or 3-part variance decomposition. Students will not be expected to analyze the data set from every possible angle and using every possible analytical procedure available. Rather, the purpose of this project is to give students some basic experience in how CCA/RDA work and how R can be used to do the analysis.

• Project 4 assignment
• Moth data set
• Lab exercises:
  • CCA/RDA lab exercise
  • Variance decomposition lab exercise
• R scripts:
  • CCA/RDA Rscript
  • Variance decompostion Rscript