genBootIndeces

Let \(n\) be the original sample size, \(p\) be the number of measurements per subject, and \(B\) be the number of bootstrap samples. <code>genBootIndeces</code> generates a (\(B\) by \(n\)) matrix containing \(B\) indexing vectors that can be used to create \(B\) bootstrap samples, each of size \(n\).

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

genBootIndeces function

<dl><dt>B</dt>
<dd>number of desired bootstrap samples</dd>
<dt>n</dt>
<dd>size of original sample from which we'll be resampling.</dd></dl>

Arguments

Generate a random set of bootstrap resampling indeces — genBootIndeces

<dl>

<dt>B</dt>
<dd>number of desired bootstrap samples</dd>


<dt>n</dt>
<dd>size of original sample from which we'll be resampling.</dd>

</dl>

genBootIndeces: Generate a random set of bootstrap resampling indeces

Description

Usage

Value

Arguments

Examples