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Exploratory Principal Component Analysis

epca is an R package for comprehending any data matrix that contains low-rank and sparse underlying signals of interest. The package currently features two key tools:

• sca for sparse principal component analysis.
• sma for sparse matrix approximation, a two-way data analysis for simultaneously row and column dimensionality reductions.

Installation

You can install the released version of epca from CRAN with:

install.packages("epca")

or the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("fchen365/epca")

Example

The usage of sca and sma is straightforward. For example, to find k sparse PCs of a data matrix X:

sca(X, k)

Similarly, we can find a rank-k sparse matrix decomposition by

sma(X, k)

For more examples, please see the vignette:

vignette("epca")

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.

Reference

Chen F and Rohe K, “A New Basis for Sparse PCA.” (arXiv)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.