**Introduction**

**Theory, Examples & Exercises**

*Unconstrained ordination**Constrained ordination*

**Data, Links & References**

**Other stuff**

Author: David Zelený

**Introduction**

**Theory, Examples & Exercises**

*Unconstrained ordination**Constrained ordination*

**Data, Links & References**

**Other stuff**

Author: David Zelený

en:pcoa

This method is also known as MDS (Metric Multidimensional Scaling). While PCA preserves Euclidean distances among samples and CA chi-square 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).

en/pcoa.txt · Last modified: 2017/02/15 09:30 by David Zelený