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bootSVD (version 0.1)

Fast, Exact Bootstrap Principal Component Analysis for High Dimensional Data

Description

Implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in (arxiv.org/abs/1405.0922).

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Version

Install

install.packages('bootSVD')

Monthly Downloads

456

Version

0.1

License

GPL-2

Maintainer

Aaron Fisher

Last Published

July 21st, 2014

Functions in bootSVD (0.1)

fastSVD

Fast SVD of a wide or tall matrix

getMomentsAndMomentCI

Calculate bootstrap moments and moment-based confidence intervals for the PCs.

bootSVD

Calculates bootstrap distribution of PCA (i.e. SVD) results

EEG_mu

Functional mean from EEG dataset

reindexDsByK

Allows for 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 ($b$).

genQ

Generate random orthonormal matrix

qrSVD

Wrapper for svd, which uses random preconditioning to restart when svd fails to converge

genBootIndeces

Generate a random set of bootstrap resampling indeces

EEG_leadingV

Leading 5 Principal Components (PCs) from EEG dataset

bootPCA

Quickly calculates bootstrap PCA results (wrapper for bootSVD)

Quickly print an R object's size

EEG_score_var

Empirical variance of the first 5 score variables from EEG dataset

bootSVD_LD

Calculate bootstrap distribution of $n$-dimensional PCs

simEEG

Simulation functional EEG data

reindexPCsByK

Allows for calculation of low dimensional standard errors & percentiles, by re-indexing the $A^b$ by PC index ($k$) rather than bootstrap index ($b$).

As2Vs

Convert low dimensional bootstrap components to high dimensional bootstrap components