Introduction
Theory, Examples & Exercises

 Constrained ordination
Data & Functions
Others
Permalink: http://bit.ly/anadatr Author: David Zelený
Introduction
Theory, Examples & Exercises
Data & Functions
Others
Permalink: http://bit.ly/anadatr Author: David Zelený
This method is also known as MDS (Metric Multidimensional Scaling). While PCA preserves Euclidean distances among samples and CA chisquare distances, PCoA provides Euclidean representation of a set of objects whose relationship is measured by any similarity or distance measure chosen by the user. As well as PCA and CA, PCoA returns a set of orthogonal axes whose importance is measured by eigenvalues. This means that calculating PCoA on Euclidean distances among samples yields the same results as PCA calculated on covariance matrix of the same dataset (if scaling 1 is used).