pca_plots

<p>These helpers visualise the results returned by <code>pca_bigmatrix()</code> and its
companions without requiring users to materialise dense intermediate
structures. Each plotting function optionally samples the inputs so the
default output remains responsive even when the underlying big matrix spans
millions of observations.</p>

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

pca_plots: Plot PCA diagnostics for big data workflows

Description

Arguments

See Also