%Gra: an SAS macro for generalized redundancy analysis

Pietro Giorgio Lovaglio, Gianmarco Vacca

Risultato della ricerca: Contributo in rivistaArticolo in rivista


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.
Lingua originaleEnglish
pagine (da-a)1048-1060
Numero di pagine13
RivistaJournal of Statistical Computation and Simulation
Stato di pubblicazionePubblicato - 2017


  • 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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