%Gra: an SAS macro for generalized redundancy analysis

Pietro Giorgio Lovaglio, Gianmarco Vacca

Research output: Contribution to journalArticle

Abstract

In the framework of redundancy analysis and reduced rank regression, the extended redundancy analysis model managed to account for more than two blocks of manifest variables in its specification. A further extension, the generalized redundancy analysis (GRA), has been recently proposed in literature, with the aim of incorporating external covariates into the model, thanks to a new estimation algorithm that manages to separate all the contributions of the exogenous and external covariates in the formation of the latent composites. At present, software to estimate GRA models is not available. In this paper, we provide an SAS macro, %GRA, to specify and fit structural relationships, with an application to illustrate the use of the macro.
Original languageEnglish
Pages (from-to)1048-1060
Number of pages13
JournalJournal of Statistical Computation and Simulation
Volume87
DOIs
Publication statusPublished - 2017

Keywords

  • Applied Mathematics
  • Modeling and Simulation
  • Redundancy analysis
  • SAS macro
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • alternating least squares
  • latent components
  • reduced rank regression

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