new_bigpca_result

<p>These helpers provide a light-weight S3 layer around the PCA outputs so
users can interact with them through familiar generics such as
<code><a href="/link/summary()?package=bigPCAcpp&version=0.9.0" data-mini-rdoc="bigPCAcpp::summary()">summary()</a></code> and <code><a href="/link/plot()?package=bigPCAcpp&version=0.9.0" data-mini-rdoc="bigPCAcpp::plot()">plot()</a></code>.</p>

internal

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

new_bigpca_result function

<p>These helpers provide a light-weight S3 layer around the PCA outputs so
users can interact with them through familiar generics such as
<code><a href='https://rdrr.io/r/base/summary.html'>summary()</a></code> and <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot()</a></code>.</p>

new_bigpca_result: Internal constructors and S3 methods for bigPCAcpp results

Description

Usage

Arguments