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

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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AuthorUwe Menzel
Date of publication2016-06-23 19:57:40
MaintainerUwe Menzel <uwemenzel@gmail.com>

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add.Gaussian.noise Man page
create.rand.mat Man page
kb.distance Man page
kld Man page
rm.connections Man page
rm.denoise.mat Man page
rm.discard.zeros Man page
rm.distance.plot Man page
rm.ev.density Man page
rm.ev.unfold Man page
rm.exp.distrib Man page
rm.get.distance Man page
rm.get.file.extension Man page
rm.get.sparseness Man page
rm.get.threshold Man page
rm.likelihood.plot Man page
rm.matrix.validation Man page
rm.reorder.ev Man page
rm.show.plots Man page
rm.show.test Man page
rm.spacing.distribution Man page
rm.spacing.scatter Man page
rm.sse Man page
rm.sse.plot Man page
RMThreshold Man page
RMThreshold-package Man page
rm.trapez.int Man page
rm.unfold.gauss Man page
rm.unfold.spline Man page
wigner.semi.circle Man page
wigner.surmise Man page

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