README.md

xpca

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.

Installation

You can install the development version of xpca from GitHub with:

# install.packages("devtools")
devtools::install_github("haziqj/xpca")

Usage

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