README.md

In SpatPCA: Regularized Principal Component Analysis for Spatial Data

SpatPCA Package

Description

SpatPCA is an R package that facilitates regularized principal component analysis,

• seeking the dominant patterns (eigenfunctions), which can be smooth and localized
• computing spatial prediction (Kriging) at new locations
• suitable for either regularly or irregularly spaced data, including 1D, 2D, and 3D
• by the alternating direction method of multipliers (ADMM) algorithm

Installation

To get the current released version from CRAN:

install.packages("SpatPCA")

To get the current development version from GitHub:

devtools::install_github("egpivo/SpatPCA")

To compile C++ code with the package RcppArmadillo,

• Windows users require Rtools
• Mac users require Xcode Command Line Tools, and install the library gfortran by typing the following lines into terminal

curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

More details can be found here.

Usage

library(SpatPCA)
spatpca(position, realizations)

• Input: realizations with the corresponding position
• Output: return the most dominant eigenfunctions automatically.
• More details can be referred to Demo

Author

Wen-Ting Wang and 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

SpatPCA documentation built on Jan. 31, 2021, 5:05 p.m.