pca_plot_contributions

Highlights the variables that contribute most to a selected principal
component. The helper works with dense matrices returned by
<code>pca_variable_contributions()</code> as well as with <code>bigmemory::big.matrix</code>
objects via sampling.

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_plot_contributions function

<dl><dt>contributions</dt>
<dd>Contribution matrix where rows correspond to variables
and columns to components.</dd>
<dt>component</dt>
<dd>Integer index of the component to visualise.</dd>
<dt>top_n</dt>
<dd>Number of variables with the largest absolute contribution to
include in the bar plot.</dd>
<dt>draw</dt>
<dd>Logical; set to <code>FALSE</code> to skip plotting.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code><a href="/link/barplot()?package=bigPCAcpp&version=0.9.0" data-mini-rdoc="bigPCAcpp::barplot()">barplot()</a></code>.</dd></dl>

Arguments

Plot variable contributions — pca_plot_contributions

<dl>

<dt>contributions</dt>
<dd>Contribution matrix where rows correspond to variables
and columns to components.</dd>


<dt>component</dt>
<dd>Integer index of the component to visualise.</dd>


<dt>top_n</dt>
<dd>Number of variables with the largest absolute contribution to
include in the bar plot.</dd>


<dt>draw</dt>
<dd>Logical; set to <code>FALSE</code> to skip plotting.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code><a href='https://rdrr.io/r/graphics/barplot.html'>barplot()</a></code>.</dd>

</dl>

pca_plot_contributions: Plot variable contributions

Description

Usage

Value

Arguments