genQ

<code>genQ</code> generates a square matrix of random normal noise, and then takes the QR decomposition to return Q, a random orthogonal square matrix.

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 <doi:10.48550/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

genQ function

<dl><dt>n</dt>
<dd>the dimension of the desired random orthonormal matrix</dd>
<dt>lim_attempts</dt>
<dd>the random matrix of normal noise must be full rank to generate the appropriate QR decomposition. <code>lim_attempts</code> gives the maximum number of attempts for generating a full rank matrix of normal noise.</dd></dl>

Arguments

Generate random orthonormal matrix — genQ

<dl>

<dt>n</dt>
<dd>the dimension of the desired random orthonormal matrix</dd>


<dt>lim_attempts</dt>
<dd>the random matrix of normal noise must be full rank to generate the appropriate QR decomposition. <code>lim_attempts</code> gives the maximum number of attempts for generating a full rank matrix of normal noise.</dd>

</dl>

genQ: Generate random orthonormal matrix

Description

Usage

Value

Arguments

Examples