PCDimension: Finding the Number of Significant Principal Components
Version 1.1.8

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 .

Package details

AuthorKevin R. Coombes, Min Wang
Bioconductor views Clustering
Date of publication2018-01-09 15:53:01
MaintainerKevin R. Coombes <[email protected]>
LicenseApache License (== 2.0)
URL http://oompa.r-forge.r-project.org/
Package repositoryView on R-Forge
Installation Install the latest version of this package by entering the following in R:
install.packages("PCDimension", repos="http://R-Forge.R-project.org")

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PCDimension documentation built on Jan. 10, 2018, 3 a.m.