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%ERA: A SAS Macro for Extended Redundancy Analysis

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  • Lovaglio, Pietro Giorgio
  • Vacca, Gianmarco

Abstract

A new approach to structural equation modeling based on so-called extended redundancy analysis has been recently proposed in the literature, enhanced with the added characteristic of generalizing redundancy analysis and reduced-rank regression models for more than two blocks. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites that were estimated as linear combinations of exogenous variables, permitting a great flexibility to specify and fit a variety of structural relationships. In this paper, we propose the SAS macro %ERA to specify and fit structural relationships in the extended redundancy analysis (ERA) framework. Two examples (simulation and real data) are provided in order to reproduce results appearing in the original article where ERA was proposed.

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  • Lovaglio, Pietro Giorgio & Vacca, Gianmarco, 2016. "%ERA: A SAS Macro for Extended Redundancy Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(c01).
    • Handle: RePEc:jss:jstsof:v:074:c01
    DOI: http://hdl.handle.net/10.18637/jss.v074.c01
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    1. Arnold Wollenberg, 1977. "Redundancy analysis an alternative for canonical correlation analysis," Psychometrika, Springer;The Psychometric Society, vol. 42(2), pages 207-219, June.
    2. Henk Kiers & Jos Berge, 1989. "Alternating least squares algorithms for simultaneous components analysis with equal component weight matrices in two or more populations," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 467-473, September.
    3. Takane, Yoshio & Hwang, Heungsun, 2005. "An extended redundancy analysis and its applications to two practical examples," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 785-808, June.
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