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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
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Package details |
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| Author | Kevin R. Coombes, Min Wang |
| Bioconductor views | Clustering |
| Maintainer | Kevin R. Coombes <krc@silicovore.com> |
| License | Apache License (== 2.0) |
| Version | 1.1.13 |
| URL | http://oompa.r-forge.r-project.org/ |
| Package repository | View on CRAN |
| Installation |
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