SpatMCA: Regularized Spatial Maximum Covariance Analysis

Description

SpatMCA is an R package designed for regularized maximum covariance analysis. It serves as a powerful tool for:

• Identifying smooth and localized coupling patterns to understand how one spatial process affects another.
• Handling both regularly and irregularly spaced data, spanning 1D, 2D, and 3D datasets.
• Implementing the alternating direction method of multipliers (ADMM) algorithm.

Installation

You can install the SpatMCA package using one of the following methods:

Install from CRAN:

install.packages("SpatMCA")

Install the current development version from GitHub:

remotes::install_github("egpivo/SpatMCA")

Please Note:

• Windows Users: Ensure that you have Rtools installed before proceeding with the installation.

• Mac Users: You need Xcode Command Line Tools and should install the library gfortran. Follow these steps in the terminal: bash brew update brew install gcc For a detailed solution, refer to this link, or download and install the library gfortran to resolve the “ld: library not found for -lgfortran” error.

Usage

To perform regularized maximum covariance analysis using SpatMCA, follow these steps:

library(SpatMCA)
spatmca(x1, x2, Y1, Y2, K = 1, num_cores = 1)

Parameters:

• x1, x2: Location matrices.
• Y1, Y2: Data matrices.
• K: Number of patterns.
• num_cores: Number of CPU cores. #### Output: Provides information about the identified patterns

Authors

• Wen-Ting Wang (GitHub)
• Hsin-Cheng Huang

Maintainer

Wen-Ting Wang (GitHub)

Reference

Wang, W.-T. and Huang, H.-C. (2018). Regularized spatial maximum covariance analysis, Environmetrics, 29, https://doi.org/10.1002/env.2481

License

GPL (>= 2)

Citation

1. To cite package ‘SpatMCA’ in publications use:

  Wang W, Huang H (2023). _SpatMCA: Regularized Spatial Maximum Covariance Analysis_.
  R package version 1.0.2.6, <https://github.com/egpivo/SpatMCA>.

2. A BibTeX entry for LaTeX users is

  @Manual{,
    title = {SpatMCA: Regularized Spatial Maximum Covariance Analysis},
    author = {Wen-Ting Wang and Hsin-Cheng Huang},
    year = {2023},
    note = {R package version 1.0.2.6},
    url = {https://github.com/egpivo/SpatMCA},
  }