Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Pietro Giorgio Lovaglio, Gianmarco Vacca
Title: %ERA: A SAS Macro for Extended Redundancy Analysis
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.

Page views:: 1498. Submitted: 2015-01-29. Published: 2016-10-27.
Paper: %ERA: A SAS Macro for Extended Redundancy Analysis     Download PDF (Downloads: 830)
Supplements: SAS source code Download (Downloads: 144; 27KB) SAS replication code Download (Downloads: 128; 11KB)
WHO_DATA.txt: Replication data Download (Downloads: 113; 1KB)

DOI: 10.18637/jss.v074.c01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.