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>.

Getting started

Package details

AuthorKevin R. Coombes, Min Wang
Bioconductor views Clustering
MaintainerKevin R. Coombes <krc@silicovore.com>
LicenseApache License (== 2.0)
URL http://oompa.r-forge.r-project.org/
Package repositoryView on CRAN
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PCDimension documentation built on July 1, 2022, 1:06 a.m.