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bigpca (version 1.1)

PCA, Transpose and Multicore Functionality for 'big.matrix' Objects

Description

Adds wrappers to add functionality for big.matrix objects (see the bigmemory project). This allows fast scalable principle components analysis (PCA), or singular value decomposition (SVD). There are also functions for transposing, using multicore 'apply' functionality, data importing and for compact display of big.matrix objects. Most functions also work for standard matrices if RAM is sufficient.

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Version

Install

install.packages('bigpca')

Monthly Downloads

13

Version

1.1

License

GPL (>= 2)

Maintainer

Nicholas Cooper

Last Published

November 21st, 2017

Functions in bigpca (1.1)

PCA, Transpose and Multicore Functionality for 'big.matrix' Objects

estimate.eig.vpcs

Estimate the variance percentages for uncalculated eigenvalues

Correct a big.matrix by principle components

Transpose function for big.matrix objects

PCA/Singular Value Decomposition for big.matrix

clear_active_bms

Function to clear big.matrix objects in the calling environment

big.algebra.install.help

Attempt to install the bigalgebra package

bigpca-internal

Internal bigpca Functions

Select a subset of a big.matrix

A multicore 'apply' function for big.matrix objects

import.big.data

Load a text file into a big.matrix object

Make scree plots for any PCA

Quickly estimate the 'elbow' of a scree plot (PCA)

Retrieve a big.matrix object

generate.test.matrix

Generate a test matrix of random data

Selection of the most correlated variable subset

Selection of a representative variable subset

quick.pheno.assocs

Quick association tests for phenotype

Tidier display function for big matrix objects

select.least.assoc

Select subset of rows least associated with a categorical variable

svn.bigalgebra.install

Attempt to install the bigalgebra package using SVN

Reduce one dimension of a large matrix in a strategic way

Derive a subset of a large dataset