randSVD

Uses randomised linear algebra, see Halko et al. (2010). Singular value
decomposition (SVD) decomposes a matrix \(X=U\Sigma W^T\)

Carry out principal component analysis (PCA) of very large pedigrees such as found
in breeding populations! This package exploits sparse matrices and randomised
linear algebra to deliver a gazillion-times speed-up compared to naive singular value decoposition (SVD)
(and eigen decomposition).

Hannes Becher

randPedPCA

Fast PCA for Large Pedigrees

Hanbin Lee

Ros Craddock

Gregor Gorjanc

randSVD function

<dl><dt>L</dt>
<dd>a pedigree's L inverse matrix in sparse 'spam' format</dd>
<dt>rank</dt>
<dd>An <code>integer</code>, how many principal components to return</dd>
<dt>depth</dt>
<dd><code>integer</code>, the number of iterations for generating the range matrix</dd>
<dt>numVectors</dt>
<dd>An <code>integer &gt; rank</code> to specify the oversampling for the</dd>
<dt>cent</dt>
<dd><code>logical</code>, whether or not to (implicitly) centre the additive
relationship matrix</dd></dl>

Arguments

Singular value decomposition in sparse triangular matrix — randSVD

<dl>

<dt>L</dt>
<dd>a pedigree's L inverse matrix in sparse 'spam' format</dd>


<dt>rank</dt>
<dd>An <code>integer</code>, how many principal components to return</dd>


<dt>depth</dt>
<dd><code>integer</code>, the number of iterations for generating the range matrix</dd>


<dt>numVectors</dt>
<dd>An <code>integer &gt; rank</code> to specify the oversampling for the</dd>


<dt>cent</dt>
<dd><code>logical</code>, whether or not to (implicitly) centre the additive
relationship matrix</dd>

</dl>

randSVD: Singular value decomposition in sparse triangular matrix

Description

Usage

Value

Arguments