spca-data

spca

<p>This data set contains an object of the class <code>SamplePCA</code>.
  This object results from performing a principal components analysis on
  a simulated data set.</p>

datasets

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>.

Kevin Coombes

PCDimension

Finding the Number of Significant Principal Components

Min Wang

spca-data function

<p>A <code>SamplePCA</code> object based on a simulated data matrix with
  204 rows and 14 columns, with true "principal component dimension"
  equal to one. That is, there should be one significant principal
  component.</p>

spca-data: Sample PCA Dataset

Description

Usage

Arguments

Format

See Also