require("plyr")
require("abind")
data(iris)
C <- daply(iris, "Species", function(x) cov(x[, -ncol(x)]))
C <- aperm(C, c(2, 3, 1)) # put the 1st dimension to the end
dim(C)
dimnames(C)
mod <- cpc(C)
str(mod)
round(mod$CPC, 2)
# See Trendafilov (2010). Stepwise estimation of common principal components.
# Computational Statistics & Data Analysis, 54(12), 3446-3457.
# doi:10.1016/j.csda.2010.03.010
# p. 10, Example 2
#
# [,1] [,2] [,3] [,4]
#[1,] 0.75 -0.09 0.63 0.20
#[2,] 0.44 0.79 -0.33 -0.26
#[3,] 0.47 -0.60 -0.54 -0.34
#[4,] 0.15 0.02 -0.45 0.88
#
# The eigenvectors must be the same, as the default method in `cpc` function
# is the power algorithm proposed by Trendafilov.
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