Computes PCASup analysis for the direction concerning the reduced mode.
pcasup1(X, n, m, p, model)A list including the following components:
Matrix of the eingenvectors of the supermatrix containing the frontal slices of the array (A-mode)
Matrix of the eingenvectors of the supermatrix containing the horizontal slices of the array (B-mode)
Matrix of the eingenvectors of the supermatrix containing the lateral slices of the array (C-mode)
Vector of the eigenvalues of the supermatrix containing the frontal slices of the array (A-mode)
Vector of the eigenvalues of the supermatrix containing the horizontal slices of the array (B-mode)
Vector of the eigenvalues of the supermatrix containing the lateral slices of the array (C-mode)
Matrix (or data.frame coerced to a matrix) of order (n x mp) containing the matricized array (frontal slices)
Number of A-mode entities
Number of B-mode entities
Number of C-mode entities
Tucker1 model choice (1 for T1-A, 2 for T1-B, 3 for T2-C)
Maria Antonietta Del Ferraro mariaantonietta.delferraro@yahoo.it
Henk A.L. Kiers h.a.l.kiers@rug.nl
Paolo Giordani paolo.giordani@uniroma1.it
H.A.L. Kiers (1991). Hierarchical relations among three-way methods. Psychometrika 56: 449--470.
H.A.L. Kiers (2000). Towards a standardized notation and terminology in multiway analysis. Journal of Chemometrics 14:105--122.
L.R Tucker (1966). Some mathematical notes on three-mode factor analysis. Psychometrika 31: 279--311.
T1
data(Bus)
# PCA-sup for T1-B
pcasupBus <- pcasup1(Bus, 7, 5, 37, 2)
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