Fast, Exact Bootstrap Principal Component Analysis for High Dimensional Data

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Documentation for package ‘bootSVD’ version 1.1

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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