pca_supplementary_individuals

Compute principal component scores and quality metrics for
supplementary individuals (rows) projected into an existing PCA solution.

High performance principal component analysis routines
that operate directly on 'bigmemory::big.matrix' objects. The
package avoids materialising large matrices in memory by
streaming data through 'BLAS' and 'LAPACK' kernels and provides
helpers to derive scores, loadings, correlations, and
contribution diagnostics, including utilities that stream
results into 'bigmemory'-backed matrices for file-based
workflows. Additional interfaces expose 'scalable' singular value
decomposition, robust PCA, and robust SVD algorithms so that
users can explore large matrices while tempering the influence
of outliers. 'Scalable' principal component analysis is also implemented,
Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015)
<doi:10.1145/2723372.2751520>.

Frederic Bertrand

bigPCAcpp

Principal Component Analysis for 'bigmemory' Matrices

pca_supplementary_individuals function

<dl><dt>data</dt>
<dd>Matrix-like object whose rows correspond to supplementary
individuals and columns to the original variables.</dd>
<dt>rotation</dt>
<dd>Rotation matrix from the PCA model (e.g. the <code>rotation</code>
element of a <code>bigpca</code> result).</dd>
<dt>sdev</dt>
<dd>Numeric vector of component standard deviations associated with
<code>rotation</code>.</dd>
<dt>center</dt>
<dd>Optional numeric vector giving the centring applied to each
variable when fitting the PCA. Defaults to zero centring.</dd>
<dt>scale</dt>
<dd>Optional numeric vector describing the scaling applied to each
variable when fitting the PCA. When <code>NULL</code>, no scaling is applied.</dd>
<dt>total_weight</dt>
<dd>Optional positive scalar passed to
<code>pca_individual_contributions()</code> when computing contributions. When left as
<code>NA</code> (the default), the resulting contributions for each component are
normalised to sum to one across supplementary individuals. Supplying a
value bypasses this normalisation and delegates the scaling to
<code>pca_individual_contributions()</code>.</dd></dl>

Arguments

Supplementary individual diagnostics — pca_supplementary_individuals

<dl>

<dt>data</dt>
<dd>Matrix-like object whose rows correspond to supplementary
individuals and columns to the original variables.</dd>


<dt>rotation</dt>
<dd>Rotation matrix from the PCA model (e.g. the <code>rotation</code>
element of a <code>bigpca</code> result).</dd>


<dt>sdev</dt>
<dd>Numeric vector of component standard deviations associated with
<code>rotation</code>.</dd>


<dt>center</dt>
<dd>Optional numeric vector giving the centring applied to each
variable when fitting the PCA. Defaults to zero centring.</dd>


<dt>scale</dt>
<dd>Optional numeric vector describing the scaling applied to each
variable when fitting the PCA. When <code>NULL</code>, no scaling is applied.</dd>


<dt>total_weight</dt>
<dd>Optional positive scalar passed to
<code>pca_individual_contributions()</code> when computing contributions. When left as
<code>NA</code> (the default), the resulting contributions for each component are
normalised to sum to one across supplementary individuals. Supplying a
value bypasses this normalisation and delegates the scaling to
<code>pca_individual_contributions()</code>.</dd>

</dl>

pca_supplementary_individuals: Supplementary individual diagnostics

Description

Usage

Value

Arguments