| pcamean {ThreeWay} | R Documentation |
PCA of the mean matrix
Description
Performs Principal Component Analysis (PCA) of the mean matrix aggregated over mode number indicated by aggregmode.
Usage
pcamean(X, n, m, p, laba, labb, labc)
Arguments
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 |
Value
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 |
Note
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.
Author(s)
Maria Antonietta Del Ferraro mariaantonietta.delferraro@yahoo.it
Henk A.L. Kiers h.a.l.kiers@rug.nl
Paolo Giordani paolo.giordani@uniroma1.it
References
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.
Examples
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)