Association Measures and Matrices
Most methods of multivariate analysis, in particular ordination and clustering techniques, are explicitly or implicitly based on the comparison of all possible pairs of objects or descriptors. The comparisons take the form of association measures (often called coefficients or indices), which are assembled in a square and symmetric association matrix, of dimensions n × n when objects are compared, or p × p when variables are compared. Since the subsequent analyses are done on association matrices, the choice of an appropriate measure is crucial. In this Chapter you will quickly revise the main categories of association coefficients, learn how to compute, examine and visually compare dissimilarity matrices (Q mode) and dependence matrices (R mode), apply these techniques to a classical dataset and learn or revise some basics of programming functions with the R language.
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