genBootIndeces

number of desired bootstrap samples

size of original sample from which we'll be resampling.

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 <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

genBootIndeces: Generate a random set of bootstrap resampling indeces

Description

Usage

Arguments

Value

Examples