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https://github.com/egpivo/SpatPCA

R Package: Regularized Principal Component Analysis for Spatial Data
https://github.com/egpivo/SpatPCA

admm covariance-estimation eigenfunctions lasso matrix-factorization pca r r-package rcpparmadillo rcppparallel regularization spatial spatial-data-analysis splines

Last synced: about 2 months ago
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R Package: Regularized Principal Component Analysis for Spatial Data

Host: GitHub
URL: https://github.com/egpivo/SpatPCA
Owner: egpivo
License: gpl-2.0
Created: 2017-01-21T04:37:27.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2024-02-10T05:59:31.000Z (8 months ago)
Last Synced: 2024-07-11T16:16:59.331Z (2 months ago)
Topics: admm, covariance-estimation, eigenfunctions, lasso, matrix-factorization, pca, r, r-package, rcpparmadillo, rcppparallel, regularization, spatial, spatial-data-analysis, splines
Language: R
Homepage: https://egpivo.github.io/SpatPCA/
Size: 108 MB
Stars: 19
Watchers: 3
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # SpatPCA: Regularized Principal Component Analysis for Spatial Data

[![License](https://eddelbuettel.github.io/badges/GPL2+.svg)](https://www.gnu.org/licenses/gpl-2.0.html)

[![R build status](https://github.com/egpivo/SpatPCA/workflows/R-CMD-check/badge.svg)](https://github.com/egpivo/SpatPCA/actions)

[![Coverage Status](https://img.shields.io/codecov/c/github/egpivo/SpatPCA/master.svg)](https://app.codecov.io/github/egpivo/SpatpCA?branch=master)

[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/SpatPCA)](https://CRAN.R-project.org/package=SpatPCA)

[![Downloads (monthly)](https://cranlogs.r-pkg.org/badges/SpatPCA?color=brightgreen)](https://www.r-pkg.org/pkg/SpatPCA)

[![Downloads (total)](https://cranlogs.r-pkg.org/badges/grand-total/SpatPCA?color=brightgreen)](https://www.r-pkg.org/pkg/SpatPCA)

[![JCGS](https://img.shields.io/badge/JCGS-10.18637%2F10618600.2016.1157483-brightgreen)](https://doi.org/10.1080/10618600.2016.1157483)

## Description

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

- Identify dominant spatial patterns (eigenfunctions) with both smooth and localized characteristics.

- Conduct spatial prediction (Kriging) at new locations.

- Adapt to regularly or irregularly spaced data, spanning 1D, 2D, and 3D datasets.

- Implement using the alternating direction method of multipliers (ADMM) algorithm.

## Installation

You can install **SpatPCA** using either of the following methods:

### Install from CRAN

```r

install.packages("SpatPCA")

```

### Install the Development Version from GitHub

```r

remotes::install_github("egpivo/SpatPCA")

```

### Compilation Requirements

To compile C++ code with the required [`RcppArmadillo`](https://CRAN.R-project.org/package=RcppArmadillo) and [`RcppParallel`](https://CRAN.R-project.org/package=RcppParallel)  packages, follow these instructions based on your operating system:

#### For Windows users

Install [Rtools](https://CRAN.R-project.org/bin/windows/Rtools/)

#### For Mac users

1. Install Xcode Command Line Tools

2. install the `gfortran` library. You can achieve this by running the following commands in the terminal:

  ```bash

  brew update

  brew install gcc

  ```

  For a detailed solution, refer to [this link](https://thecoatlessprofessor.com/programming/rcpp-rcpparmadillo-and-os-x-mavericks-lgfortran-and-lquadmath-error/), or download and install the library [`gfortran`](https://github.com/fxcoudert/gfortran-for-macOS/releases) to resolve the error `ld: library not found for -lgfortran`.

## Usage

To use **SpatPCA**, first load the package:

```r

library(SpatPCA)

```

Then, apply the `spatpca` function with the following syntax:

```r

spatpca(position, realizations)

```

   - Input: Realizations with the corresponding positions.

   - Output: Return the most dominant eigenfunctions automatically.

For more details, refer to the [Demo](https://egpivo.github.io/SpatPCA/articles/).

## Authors

- [Wen-Ting Wang](https://www.linkedin.com/in/wen-ting-wang-6083a17b) ([GitHub](https://www.github.com/egpivo))

- [Hsin-Cheng Huang](https://sites.stat.sinica.edu.tw/hchuang/)

 

## Maintainer

[Wen-Ting Wang](https://www.linkedin.com/in/wen-ting-wang-6083a17b) ([GitHub](https://www.github.com/egpivo))

## Reference

Wang, W.-T. and Huang, H.-C. (2017). [Regularized principal component analysis for spatial data](https://arxiv.org/pdf/1501.03221v3.pdf, "Regularized principal component analysis for spatial data"). *Journal of Computational and Graphical Statistics*, **26**, 14-25.

 

## License

GPL (>= 2)

## Citation

- To cite package ‘SpatPCA’ in publications use:

```

  Wang W, Huang H (2023). SpatPCA: Regularized Principal Component Analysis for

  Spatial Data_. R package version 1.3.5,

  .

```

- A BibTeX entry for LaTeX users is

```

  @Manual{,

    title = {SpatPCA: Regularized Principal Component Analysis for Spatial Data},

    author = {Wen-Ting Wang and Hsin-Cheng Huang},

    year = {2023},

    note = {R package version 1.3.5},

    url = {https://CRAN.R-project.org/package=SpatPCA},

  }

```