pca_plot_scree

Displays the proportion of variance explained by the leading principal
components. The function caps the number of displayed components to keep the
visualization legible on very high-dimensional problems.

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_scree function

<dl><dt>pca_result</dt>
<dd>A list created by <code>pca_bigmatrix()</code> or
<code>pca_stream_bigmatrix()</code> containing standard deviation and explained variance
elements.</dd>
<dt>max_components</dt>
<dd>Maximum number of components to display. Defaults to
25 or the available number of components, whichever is smaller.</dd>
<dt>cumulative</dt>
<dd>Logical flag indicating whether to overlay the cumulative
explained variance line.</dd>
<dt>draw</dt>
<dd>Logical; set to <code>FALSE</code> to return the prepared data without
drawing a plot (useful for testing).</dd>
<dt>...</dt>
<dd>Additional parameters passed to <code><a href="/link/plot()?package=bigPCAcpp&version=0.9.0" data-mini-rdoc="bigPCAcpp::plot()">plot()</a></code>.</dd></dl>

Arguments

Scree plot for principal component importance — pca_plot_scree

<dl>

<dt>pca_result</dt>
<dd>A list created by <code>pca_bigmatrix()</code> or
<code>pca_stream_bigmatrix()</code> containing standard deviation and explained variance
elements.</dd>


<dt>max_components</dt>
<dd>Maximum number of components to display. Defaults to
25 or the available number of components, whichever is smaller.</dd>


<dt>cumulative</dt>
<dd>Logical flag indicating whether to overlay the cumulative
explained variance line.</dd>


<dt>draw</dt>
<dd>Logical; set to <code>FALSE</code> to return the prepared data without
drawing a plot (useful for testing).</dd>


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

</dl>

pca_plot_scree: Scree plot for principal component importance

Description

Usage

Value

Arguments