In correspondence analysis rows and columns of a nonnegative data matrix are depicted as points in a, usually, two-dimensional plot. Although such a two-dimensional plot often provides a reasonable approximation, the situation can occur that an approximation of higher dimensionality is required. This is especially the case when the data matrix is large. In such instances it may become difficult to interpret the solution. Similar to what is done in principal component analysis and factor analysis the correspondence analysis solution can be rotated to increase the interpretability. However, due to the various scaling options encountered in correspondence analysis, there are several alternative options for rotating the solutions. In this paper we consider two options for rotation in correspondence analysis. An example is provided so that the benefits of rotation become apparent.
KeywordsCorrespondence Analysis Simple Structure Attribute Category Absolute Contribution Coordinate Matrix
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