Contrained principal component analysis(CPCA)was proposed by Takane and Shibayama(1991)for structural analysis of multivariate data. In this method the data are first decomposed into several components according to external information. The decomposed submatrices are then subjected to principal component analysis(PCA)to explore possible structures within the submatrices. The method thus combines two major conventional multivariate analysis techniques, multiple regression analysis and PCA, in a unified framework. This paper illustrates the basic model, computational methods, various uses and extensions of CPCA. An illustrative example is given, and relative merits and demerits of CPCA are discussed in relation to the analysis of covariance structure(ACOVS)approach.
