Principal Component Analysis for 'bigmemory' Matrices
BigPCA result objects
bigPCAcpp: Principal Component Analysis for bigmemory Matrices
Internal constructors and S3 methods for bigPCAcpp results
Principal component analysis for bigmemory::big.matrix inputs
PCA biplot helper
Plot variable contributions
Plot a PCA correlation circle
Plot sampled PCA scores
Scree plot for principal component importance
Plot PCA diagnostics for big data workflows
Robust principal component analysis
Scalable principal component analysis via streaming power iterations
Streaming big.matrix PCA helpers
Supplementary individual diagnostics
Supplementary variable diagnostics
Prepare iteratively reweighted singular value decomposition
Singular value decomposition for bigmemory::big.matrix inputs
Iteratively reweighted singular value decomposition
Robust singular value decomposition (C++ backend)
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>.
Useful links