Package: PCDimension 1.1.14
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_1.1.14.tar.gz
PCDimension_1.1.14.tar.gz(r-4.7-any)PCDimension_1.1.14.tar.gz(r-4.6-any)
PCDimension_1.1.14.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
PCDimension/json (API)
| # 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
Datasets:
- spca - Sample PCA Dataset
Last updated from:ab3ca5107a. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 121 | ||
| source / vignettes | OK | 216 | ||
| linux-release-x86_64 | OK | 128 | ||
| wasm-release | OK | 101 |
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 |