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RMThreshold (version 1.1)

Signal-Noise Separation in Random Matrices by using Eigenvalue Spectrum Analysis

Description

An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.

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Version

Install

install.packages('RMThreshold')

Monthly Downloads

142

Version

1.1

License

GPL

Maintainer

Uwe Menzel

Last Published

June 23rd, 2016

Functions in RMThreshold (1.1)

rm.discard.zeros

Discard rows and columns from a matrix that exclusively contain zero-valued off-diagonal matrix elements
create.rand.mat

Create a real-valued, symmetric random matrix
rm.connections

Create ordered list of largest matrix elements
rm.ev.density

Create a density plot and a histogram of the eigenvalue distribution
rm.spacing.distribution

Plot the empirical distribution of the eigenvalue spacings
rm.show.plots

Display a sequence of plots on screen
rm.get.threshold

Estimate an objective threshold for signal-noise separation in random matrices
rm.denoise.mat

Remove noise from a random matrix by applying a threshold
add.Gaussian.noise

Add Gaussian noise to a matrix
rm.matrix.validation

Validate input matrix prior to threshold computation
RMThreshold-package

Signal-Noise Separation in Correlation Matrices by using Eigenvalue Spectrum Analysis
RMThreshold-internal

Internal functions for the RMThreshold package