title: "Computing Least Squares Sparse Principal Components with the spca Package"author: "Giovanni Maria Merola"date: "r Sys.Date()"output: rmarkdown::html_vignettevignette: >%\VignetteIndexEntry{Computing Least Squares Sparse Principal Components with spca}%\VignetteEngine{knitr::rmarkdown}%\VignetteEncoding{UTF-8}%\VignetteDepends{spca}knit: (function(input, ...) rmarkdown::render(input, output_dir = dirname(normalizePath(input)), ...)) | Introduction | LS-SPCA in brief | The spca package | Application | pca() | spca() | print() | summary() | plot() | Fixed indices | Compare solutions | Variable groups | Create an spca object | Comparison of LS-SPCA variants | Computation methods | Variable selection methods | alpha | Comparison with conventional SPCA | Tall matrices | Fat matrices | References
Package: spca 1.1.1
Giovanni Maria Merola
spca: Least Squares Sparse Principal Components Analysis
Implements least-squares sparse principal component analysis (LS-SPCA). The approach follows Merola (2015) <doi:10.1111/anzs.12128> and Merola and Chen (2019) <doi:10.1016/j.jmva.2019.04.001>.
Authors:
spca_1.1.1.tar.gz
spca_1.1.1.tar.gz(r-4.7-arm64)spca_1.1.1.tar.gz(r-4.7-x86_64)spca_1.1.1.tar.gz(r-4.6-arm64)spca_1.1.1.tar.gz(r-4.6-x86_64)
spca_0.6.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spca/json (API)
| # Install 'spca' in R: |
| install.packages('spca', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/merolagio/spca/issues
Datasets:
- holzinger - Holzinger-Swineford Student Ability data
- holzinger_scales - Holzinger-Swineford Student Ability scales
Last updated from:3e62b86e74. Checks:5 OK, 1 FAIL. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 288 | ||
| linux-devel-x86_64 | OK | 313 | ||
| source / vignettes | OK | 420 | ||
| linux-release-arm64 | OK | 276 | ||
| linux-release-x86_64 | OK | 283 | ||
| wasm-release | FAIL | 134 |
Exports:aggregate_by_groupchange_loadings_sign_spcacompare_spcais.spcanew_spcapcashow_contributions_spcaspcaspca_screeplotwachter_qqplot
Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerRcppRcppEigenrlangRMTstatS7scalesvctrsviridisLitewithr
Computing Least Squares Sparse Principal Components with spca
Last update: 2026-07-10
Started: 2026-07-10
Introduction to the spca package
Installation | Usage | Example | Load data | Preliminary PCA | Compute the sparse loadings | Inspect spca results | Variable groups | Comparison of two or more spca solutions
Last update: 2026-07-10
Started: 2026-07-10
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Least Squares Sparse Principal Components Analysis | spca-package |
| Aggregate loadings or contributions by group | aggregate_by_group |
| Change the sign of selected loadings in an spca object | change_loadings_sign_spca |
| Compare two or more spca solutions | compare_spca |
| Holzinger-Swineford Student Ability data | holzinger |
| Holzinger-Swineford Student Ability scales | holzinger_scales |
| Test for spca objects | is.spca |
| Construct an spca object from a set of loadings | new_spca |
| Compute principal components | pca |
| Plot an spca object | plot.spca |
| Print an spca object | print.spca |
| Show nonzero contributions by component | show_contributions_spca |
| Compute LS-SPCA components | spca |
| Sparse principal component analysis object | spca_object |
| Plot eigenvalues in a scree plot | spca_screeplot |
| Summarize an spca object | summary.spca |
| Wachter QQ plot for eigenvalues | wachter_qqplot |
