An implementation of Principal Components Analysis (PCA) specifically
designed for cross-covariance matrices. The xpca package provides
tools for finding the principal components that maximize the explained
variance between two sets of multivariate random variables.
You can install the development version of xpca from GitHub with:
# install.packages("devtools")
devtools::install_github("haziqj/xpca")library(xpca)
set.seed(123)
x <- matrix(rnorm(100 * 3), nrow = 100, ncol = 3)
y <- matrix(rnorm(100 * 5), nrow = 100, ncol = 5)
xpca(x, y)
#> $singular_values
#> [1] 0.26040010 0.19322889 0.09353863
#>
#> $u
#> [,1] [,2] [,3]
#> [1,] -0.9609009 0.07517796 0.2664917
#> [2,] -0.1581545 -0.93900882 -0.3053680
#> [3,] 0.2272811 -0.33557518 0.9141841
#>
#> $v
#> [,1] [,2] [,3]
#> [1,] 0.08885207 -0.1533433 -0.6954801
#> [2,] 0.69772335 0.5801151 -0.3160890
#> [3,] 0.12965638 -0.5520606 -0.3184743
#> [4,] 0.07926167 -0.4047093 -0.2625459
#> [5,] -0.69440208 0.4139948 -0.4960232
#>
#> $x_loadings
#> [,1] [,2] [,3]
#> [1,] -0.9609009 0.07517796 0.2664917
#> [2,] -0.1581545 -0.93900882 -0.3053680
#> [3,] 0.2272811 -0.33557518 0.9141841
#>
#> $y_loadings
#> [,1] [,2] [,3]
#> [1,] 0.08885207 -0.1533433 -0.6954801
#> [2,] 0.69772335 0.5801151 -0.3160890
#> [3,] 0.12965638 -0.5520606 -0.3184743
#> [4,] 0.07926167 -0.4047093 -0.2625459
#> [5,] -0.69440208 0.4139948 -0.4960232