Regularized Principal Component Analysis for Spatial Data.

SpatPCA Package

Description

SpatPCA is an R package designed for efficient regularized principal component analysis, providing the following features:

• Identification of dominant spatial patterns (eigenfunctions) with both smooth and localized characteristics.
• Spatial prediction (Kriging) at new locations.
• Adaptability for regularly or irregularly spaced data, spanning 1D, 2D, and 3D datasets.
• Implementation using the alternating direction method of multipliers (ADMM) algorithm.

Installation

To install the current development version from GitHub, use the following R code:

remotes::install_github("egpivo/SpatPCA")

For compiling C++ code with the required RcppArmadillo and RcppParallel packages, follow these instructions:

• Windows users: Install Rtools
• Mac users: Install Xcode Command Line Tools, and install the gfortran library. You can achieve this by running the following commands in the terminal:

brew update
brew install gcc

For a detailed solution, refer to this link, or download and install the library gfortran to resolve the error ld: library not found for -lgfortran.

Usage

library(SpatPCA)
spatpca(position, realizations)

• Input: Realizations with the corresponding positions.
• Output: Return the most dominant eigenfunctions automatically.
• For more details, refer to the Demo.

Author

• Wen-Ting Wang
• Hsin-Cheng Huang

Maintainer

Wen-Ting Wang

Reference

Wang, W.-T. and Huang, H.-C. (2017). Regularized principal component analysis for spatial data. Journal of Computational and Graphical Statistics, 26, 14-25.

License

GPL-3