cpca-package: Package cpc.

In cpca: Methods to perform Common Principal Component Analysis (CPCA)

Description

Methods to perform Common Principal Component Analysis (CPCA).

Examples

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.

Example output

Loading required package: plyr
Loading required package: abind
[1] 4 4 3
[[1]]
[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 

[[2]]
[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 

$Species
[1] "setosa"     "versicolor" "virginica" 

List of 3
 $ D    : num [1:4, 1:3] 0.1908 0.0787 0.0276 0.0121 0.4668 ...
 $ CPC  : num [1:4, 1:4] -0.7467 -0.4423 -0.4743 -0.1476 0.0911 ...
 $ ncomp: int 4
      [,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

cpca documentation built on May 2, 2019, 3:47 p.m.