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Principal Coordinates Analysis (PCoA) is a flexible technique for working with multiple variables and observations. By working with a distance or dissimilarity matrix, PCoA can be applied to quantitative variables, qualitative variables, or even a mix of both. Consequently, PCoA is a great tool to simplify, visualize, and understand relationships among observations. Through PCoA, you will […]

NMDS is a non-parametric (no eigenvalues!) dimension reduction method that simply seeks to produce a plot where similar samples are close to each other and dissimilar samples are farther apart.  Rather than preserving exact distances among samples, it preserves the ordered (rank) relationships. This method is applied on a distance matrix, which can be computed […]