R: Fast, Exact Bootstrap Principal Component Analysis for High Dimensional Data

# Fast, Exact Bootstrap Principal Component Analysis for High Dimensional Data

## Documentation for package ‘bootSVD’ version 1.2

- DESCRIPTION file
- Package README

## Help Pages

As2Vs | Convert low dimensional bootstrap components to high dimensional bootstrap components
bootPCA | Quickly calculates bootstrap PCA results (wrapper for bootSVD)
bootSVD | Calculates bootstrap distribution of PCA (i.e. SVD) results
bootSVD_LD | Calculate bootstrap distribution of n-dimensional PCs
EEG_leadingV | Leading 5 Principal Components (PCs) from EEG dataset
EEG_mu | Functional mean from EEG dataset
EEG_score_var | Empirical variance of the first 5 score variables from EEG dataset
fastSVD | Fast SVD of a wide or tall matrix
ffmatrixmult | Matrix multiplication with "ff_matrix" or "matrix" inputs
genBootIndeces | Generate a random set of bootstrap resampling indeces
genQ | Generate random orthonormal matrix
getMomentsAndMomentCI | Calculate bootstrap moments and moment-based confidence intervals for the PCs.
os | Quickly print an R object's size
qrSVD | Wrapper for 'svd', which uses random preconditioning to restart when svd fails to converge
reindexMatricesByK | Used for calculation of low dimensional standard errors & percentiles, by re-indexing the A^b by PC index (k) rather than bootstrap index (b).
reindexVectorsByK | Used to study of the bootstrap distribution of the k^th singular values, by re-indexing the list of d^b vectors to be organized by PC index (k) rather than bootstrap index (b).
simEEG | Simulation functional EEG data