Generalised Procrustes Analysis with optimal scaling: Exploring data from a power supplier

(2009). Generalised procrustes analysis with optimal scaling: exploring data from a power supplier. Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. Abstract Generalised Procrustes Analysis (GPA) is a method for matching several, possibly large, data sets by fitting to each other using transformations, typically rotations. The linear version of GPA has been applied in a wide range of contexts. A non-linear extension of GPA is developed which uses Optimal Scaling (OS). The approach is suited to match data sets that contain nominal variables. A data base of a Dutch power supplier that contains many categorical variables unfit for the usual linear GPA methodology is used to illustrate the approach.