Another way of examining the patterns among objects based on multiple variables is to plot the objects in multidimensional space based on their pairwise dissimilarities. We first describe multidimensional scaling as a very flexible ordination method that can be based on a wide range of dissimilarities. We also introduce cluster analysis based on dissimilarities, where the pattern among objects is represented in a tree-like plot called a dendrogram. We show how to correlate dissimilarities to other continuous and/or grouping variables and fit linear models that treat the dissimilarities as responses modeled against continuous or categorical predictors.
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