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 .


News

		Dear Emacs, please make this -*-Text-*- mode!
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*	       1.1 SERIES NEWS			 *
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	CHANGES IN R VERSION 1.1.9

UPDATES

o	Modified use of S4 classes to avoid deprecated and discouraged
    elements. 

	CHANGES IN R VERSION 1.1.0

NEW FEATURES

o	Added agDimLeap, a new algorithm to find long steps.

o	Added a new function, compareAgDimMethods, to report the
    different dimensions reported by different algorithms.

BUG FIXES

o	Fixed an error in aDimKeans and agDoimKmeans3 that only
    flagged half of the "long" steps. 

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*	       1.0 SERIES NEWS			 *
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	CHANGES IN R VERSION 1.0.0

NEW FEATURES

o	Initial version. Class Auer-Gervini.

BUG FIXES

o	Numerous.

Reference manual

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install.packages("PCDimension")

1.1.9 by Kevin R. Coombes, a year ago


http://oompa.r-forge.r-project.org/


Browse source code at https://github.com/cran/PCDimension


Authors: Kevin R. Coombes , Min Wang


Documentation:   PDF Manual  


Apache License (== 2.0) license


Imports methods, stats, graphics, oompaBase, kernlab, changepoint, cpm

Depends on ClassDiscovery

Suggests MASS, nFactors


Depended on by Thresher.


See at CRAN