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Applying Multivariate Methods using R, CAP and Ecom
Peter Henderson & Richard Seaby

NOTE: for data sets for the previous version of this book, A Practical Handbook for Multivariate Methods, please go here.

Applying Multivariate Methods features R code examples, and a wide range of data sets from published work in different fields. So that the reader can really explore the featured methods in depth, we have made the R code and data sets available to be downloaded.

R code
All the R code examples featured in the book are available in a single zipped file - Rexamples.zip
Please note: the R code snippets given in the book were tested and shown to work with the data sets supplied, at the time of going to press (June 2019). Please be aware that the various packages used are frequently updated, and so some editing of the code may be necessary. We will endeavour to ensure that the downloadable versions of the code available here are kept updated. However, as the R packages themselves are beyond our control, we cannot guarantee that the code snippets will continue to work, or give the same results, indefinitely, without some modification.

Data sets
To download each data set, simply click on the link. The file size is tiny, usually 2 - 3 kb.

Each data set is in the form of a zipped file, containing the data as a .csv (comma-separated text) file, along with a file with a .pcg extension. This second file holds data relating to group assignments; if you open the .csv file in Community Analysis Package or Ecom, the samples will automatically be assigned to their selected groups, provided the .pcg file is in the same folder as the main data file.

The data to be used in Ecom comprise two data sets; one featuring species/sample data, and the other data relating to environmental variables.

Chapter 3: Principal Component Analysis (PCA)
Chapter 4: Correspondence Analysis (CA)
Chapter 5: Multidimensional Scaling (MDS)
Chapter 6: Linear Discriminant Analysis (DA)
Chapter 7: Canonical Correspondence Analysis (CCA)
Chapter 8: TWINSPAN (Two-Way Indicator Species Analysis)
Chapter 9: Hierarchical Agglomerative Cluster Analysis (ACA)
Chapter 10: Analysis of Similarities (ANOSIM)