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 18:25:58 UTC
MaintainerKevin R. Coombes <[email protected]>
LicenseApache License (== 2.0)
URL http://oompa.r-forge.r-project.org/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the PCDimension package in your browser

Any scripts or data that you put into this service are public.

PCDimension documentation built on Jan. 10, 2018, 1:05 a.m.