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:Kevin R. Coombes, Min Wang

PCDimension.pdf |PCDimension.html
PCDimension/json (API)

# Install PCDimension in R:
install.packages('PCDimension', repos = c('', ''))

Peer review:

Bug tracker:

  • spca - Sample PCA Dataset

14 exports 0.91 score 12 dependencies 3 dependents 1.3k downloads

Last updated 2 years agofrom:c5ed1dc5bca2ad434ea3c48b24d2221490670dc7




Rendered fromPCDimension.Rnwusingutils::Sweaveon Jun 10 2024.

Last update: 2017-12-15
Started: 2017-12-15

Readme and manuals

Help Manual

Help pageTopics
Divide Steps into "Long" and "Short" to Compute Auer-Gervini DimensionagDimCPT agDimFunction agDimKmeans agDimKmeans3 agDimSpectral agDimTtest agDimTtest2 agDimTwiceMean makeAgCpmFun
Estimating Number of Principal Components Using the Auer-Gervini MethodagDimension AuerGervini AuerGervini-class PCDimension plot,AuerGervini,missing-method summary,AuerGervini-method
The Broken Stick MethodbrokenStick bsDimension
Compare Methods to Divide Steps into "Long" and "Short"compareAgDimMethods
Principal Component Statistics Based on RandomizationrndLambdaF
Sample PCA Datasetspca spca-data