Fast, Exact Bootstrap Principal Component Analysis for High Dimensional Data

Documentation for package ‘bootSVD’ version 1.2

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