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.
You can install the SpatMCA package using one of the following methods:
install.packages("SpatMCA")
remotes::install_github("egpivo/SpatMCA")
-
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: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.
To perform regularized maximum covariance analysis using SpatMCA, follow these steps:
library(SpatMCA)
spatmca(x1, x2, Y1, Y2, K = 1, num_cores = 1)
x1
,x2
: Location matrices.Y1
,Y2
: Data matrices.K
: Number of patterns.num_cores
: Number of CPU cores.
Provides information about the identified patterns
Wang, W.-T. and Huang, H.-C. (2018). Regularized spatial maximum covariance analysis, Environmetrics, 29, https://doi.org/10.1002/env.2481
GPL (>= 2)
- 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>.
- 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},
}