Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, ). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.


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SpatPCA provides regularized principal component analysis with

  • 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
  • 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")

Usage

library(SpatPCA)
### location
x_1D <- as.matrix(seq(-5, 5, length = 50))
###underlying eigenfunction
Phi_1D <- exp(-x_1D^2)/norm(exp(-x_1D^2), "F")
### Realizations on x_1D
Y_1D <- rnorm(n = 100, sd = 3)%*%t(Phi_1D) + matrix(rnorm(n = 100*50), 100, 50)
### main function: spatpca()
cv_1D <- spatpca(x = x_1D, Y = Y_1D)
### Plot the estimate
plot(x_1D, cv_1D$eigenfn[,1], type = 'l', main = "1st eigenfunction")

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-2

News

Reference manual

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install.packages("SpatPCA")

1.2.0.0 by Wen-Ting Wang, a year ago


https://github.com/egpivo/SpatPCA


Report a bug at https://github.com/egpivo/SpatPCA/issues


Browse source code at https://github.com/cran/SpatPCA


Authors: Wen-Ting Wang , Hsin-Cheng Huang


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp, RcppParallel

Linking to Rcpp, RcppArmadillo, RcppParallel

System requirements: GNU make


See at CRAN