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)