Comparison of methods to display principal component analysis, focusing on biplots and the selection of biplot axes

• Type: Chapter or Part of a Book
• Date: 2015

Authors

Barbosa, C (Author) Other; Rui Alves, M (Author) Other; Beatriz B P P Oliveira (Author) FFUP

Book

Title: Handbook of Research on Computational Simulation and Modeling in Engineering

Pages: 289-331

ISBN: 978-146668824-7

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00K-62P

DOI: 10.4018/978-1-4666-8823-0.ch010

Abstract (EN): Principal components analysis (PCA) is probably the most important multivariate statistical technique, being used to model complex problems or just for data mining, in almost all areas of science. Although being well known by researchers and available in most statistical packages, it is often misunderstood and poses problems when applied by inexperienced users. A biplot is a way of concentrating all information related to sample units and variables in a single display, in an attempt to help interpretations and avoid overestimations. This chapter covers the main mathematical aspects of PCA, as well as the form and covariance biplots developed by Gabriel and the predictive and interpolative biplots devised by Gower and coworkers. New developments are also presented, involving techniques to automate the production of biplots, with a controlled output in terms of axes predictivities and interpolative accuracies, supported by the AutoBiplot.PCA function developed in R. A practical case is used for illustrations and discussions.

Language: English

Type (Professor's evaluation): Scientific