PCASup Analysis
Computes PCASup analysis for the directions concerning the reduced modes.
pcasup2(X, n, m, p, model)
X |
Matrix (or data.frame coerced to a matrix) of order ( |
n |
Number of |
m |
Number of |
p |
Number of |
model |
Tucker2 model choice (1 for T2-AB, 2 for T2-AC, 3 for T2-BC) |
A list including the following components:
A |
Matrix of the eingenvectors of the supermatrix containing the frontal slices of the array ( |
B |
Matrix of the eingenvectors of the supermatrix containing the horizontal slices of the array ( |
C |
Matrix of the eingenvectors of the supermatrix containing the lateral slices of the array ( |
la |
Vector of the eigenvalues of the supermatrix containing the frontal slices of the array ( |
lb |
Vector of the eigenvalues of the supermatrix containing the horizontal slices of the array ( |
lc |
Vector of the eigenvalues of the supermatrix containing the lateral slices of the array ( |
Cumulative sum of eigenvalues and fits from PCAsup applied to the reduced modes are automatically printed.
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.
data(Bus) # PCA-sup for T2-AB pcasupBus <- pcasup2(Bus, 7, 5, 37, 1)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.