bootPCA

passed to <code><a rd-options="" href="/link/bootSVD?package=bootSVD&version=1.1" data-mini-rdoc="bootSVD::bootSVD">bootSVD</a></code>, with centerSamples set to TRUE.

All arguments are passed to <code><a rd-options="" href="/link/bootSVD?package=bootSVD&version=1.1" data-mini-rdoc="bootSVD::bootSVD">bootSVD</a></code>. This function should be used in exactly the same way as <code><a rd-options="" href="/link/bootSVD?package=bootSVD&version=1.1" data-mini-rdoc="bootSVD::bootSVD">bootSVD</a></code>. The only difference is that PCA typically involves re-centering each bootstrap sample, whereas calculations involving the SVD might not.

Implements fast, exact bootstrap Principal Component Analysis and
Singular Value Decompositions for high dimensional data, as described in
<doi:10.1080/01621459.2015.1062383> (see also <arXiv:1405.0922> ). For data matrices that are too large to operate
on in memory, users can input objects with class 'ff' (see the 'ff'
package), where the actual data is stored on disk. In response, this
package will implement a block matrix algebra procedure for calculating the
principal components (PCs) and bootstrap PCs. Depending on options set by
the user, the 'parallel' package can be used to parallelize the calculation of
the bootstrap PCs.

Aaron Fisher

bootSVD

Fast, Exact Bootstrap Principal Component Analysis for High
Dimensional Data

bootPCA function

passed to <code><a rd-options='' href='bootSVD'>bootSVD</a></code>, with centerSamples set to TRUE.

All arguments are passed to <code><a rd-options='' href='bootSVD'>bootSVD</a></code>. This function should be used in exactly the same way as <code><a rd-options='' href='bootSVD'>bootSVD</a></code>. The only difference is that PCA typically involves re-centering each bootstrap sample, whereas calculations involving the SVD might not.

bootPCA: Quickly calculates bootstrap PCA results (wrapper for bootSVD)

Description

Usage

Arguments

Value