PCA of the mean matrix
Performs Principal Component Analysis (PCA) of the mean matrix aggregated over mode number indicated by aggregmode.
pcamean(X, n, m, p, laba, labb, labc)
X | 
 Matrix (or data.frame coerced to a matrix) of order (  | 
n | 
 Number of   | 
m | 
 Number of   | 
p | 
 Number of   | 
laba | 
 Optional vector of length   | 
labb | 
 Optional vector of length   | 
labc | 
 Optional vector of length   | 
A list including the following components:
Y | 
 An object of class   | 
ev | 
 A vector containing the eigenvalues of   | 
A1 | 
 Component matrix for the   | 
B1 | 
 Component matrix for the   | 
C1 | 
 Component matrix for the   | 
A2 | 
 Component matrix for the   | 
B2 | 
 Component matrix for the   | 
C2 | 
 Component matrix for the   | 
aggregmode denotes the mode over which means are computed (1 for A-mode, 2 for B-mode, 3 for C-mode).
aggregmode is provided interactively. 
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. Kaiser (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika 23:187–200. 
 C. Harris \& H. Kaiser (1964). Some mathematical notes on three-mode factor analysis. Psychometrika 29:347–362.
data(TV) TVdata=TV[[1]] labSCALE=TV[[2]] labPROGRAM=TV[[3]] labSTUDENT=TV[[4]] # permutation of the modes so that the A-mode refers to students TVdata <- permnew(TVdata, 16, 15, 30) TVdata <- permnew(TVdata, 15, 30, 16) ## Not run: # PCA on the mean matrix TVpcamean <- pcamean(TVdata, 30, 16, 15, labSTUDENT, labSCALE, labPROGRAM) # PCA on the mean matrix (when labels are not available) TVpcamean <- pcamean(TVdata, 30, 16, 15) ## End(Not run)
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