CPCA: A program for principal component analysis with external information on subjects and variables

Abstract

A program is described for principal component analysis with external information on subjects and variables. This method is called constrained principal component analysis (CPCA), in which regression analysis and principal component analysis are combined into a unified framework that allows a full exploration of data structures both within and outside known information on subjects and variables. Many existing methods are special cases of CPCA, and the program can be used for multivariate multiple regression, redundancy analysis, double redundancy analysis, dual scaling with external criteria, vector preference models, and GMANOVA (growth curve models). Copyright 1998 Psychonomic Society, Inc.