Package: PCDimension 1.1.13
Kevin R. Coombes
PCDimension: Finding the Number of Significant Principal Components
Implements methods to automate the Auer-Gervini graphical Bayesian approach for determining the number of significant principal components. Automation uses clustering, change points, or simple statistical models to distinguish "long" from "short" steps in a graph showing the posterior number of components as a function of a prior parameter. See <doi:10.1101/237883>.
Authors:
PCDimension_1.1.13.tar.gz
PCDimension_1.1.13.tar.gz(r-4.5-noble)PCDimension_1.1.13.tar.gz(r-4.4-noble)
PCDimension_1.1.13.tgz(r-4.4-emscripten)PCDimension_1.1.13.tgz(r-4.3-emscripten)
PCDimension.pdf |PCDimension.html✨
PCDimension/json (API)
NEWS
| # Install 'PCDimension' in R: |
| install.packages('PCDimension', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://r-forge.r-project.org/projects/oompa
- spca - Sample PCA Dataset
Last updated 2 years agofrom:c5ed1dc5bc. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 07 2024 |
| R-4.5-linux | NOTE | Nov 07 2024 |
Exports:agDimCPTagDimensionagDimKmeansagDimKmeans3agDimSpectralagDimTtestagDimTtest2agDimTwiceMeanAuerGervinibrokenStickbsDimensioncompareAgDimMethodsmakeAgCpmFunrndLambdaF
Dependencies:BiobaseBiocGenericschangepointClassDiscoveryclustercpmgenericskernlablatticemclustoompaBaseoompaDatazoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Divide Steps into "Long" and "Short" to Compute Auer-Gervini Dimension | agDimCPT agDimFunction agDimKmeans agDimKmeans3 agDimSpectral agDimTtest agDimTtest2 agDimTwiceMean makeAgCpmFun |
| Estimating Number of Principal Components Using the Auer-Gervini Method | agDimension AuerGervini AuerGervini-class PCDimension plot,AuerGervini,missing-method summary,AuerGervini-method |
| The Broken Stick Method | brokenStick bsDimension |
| Compare Methods to Divide Steps into "Long" and "Short" | compareAgDimMethods |
| Principal Component Statistics Based on Randomization | rndLambdaF |
| Sample PCA Dataset | spca spca-data |