TY - JOUR
T1 - %Gra: an SAS macro for generalized redundancy analysis
AU - Lovaglio, Pietro Giorgio
AU - Vacca, Gianmarco
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Applied Mathematics
KW - Modeling and Simulation
KW - Redundancy analysis
KW - SAS macro
KW - Statistics and Probability
KW - Statistics, Probability and Uncertainty
KW - alternating least squares
KW - latent components
KW - reduced rank regression
KW - Applied Mathematics
KW - Modeling and Simulation
KW - Redundancy analysis
KW - SAS macro
KW - Statistics and Probability
KW - Statistics, Probability and Uncertainty
KW - alternating least squares
KW - latent components
KW - reduced rank regression
UR - http://hdl.handle.net/10807/120338
UR - http://www.tandf.co.uk/journals/titles/00949655.html
U2 - 10.1080/00949655.2016.1243685
DO - 10.1080/00949655.2016.1243685
M3 - Article
SN - 0094-9655
VL - 87
SP - 1048
EP - 1060
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
ER -