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.
|Date of publication||2016-06-23 19:57:40|
|Maintainer||Uwe Menzel <email@example.com>|
add.Gaussian.noise: Add Gaussian noise to a matrix
create.rand.mat: Create a real-valued, symmetric random matrix
rm.connections: Create ordered list of largest matrix elements
rm.denoise.mat: Remove noise from a random matrix by applying a threshold
rm.discard.zeros: Discard rows and columns from a matrix that exclusively...
rm.ev.density: Create a density plot and a histogram of the eigenvalue...
rm.get.threshold: Estimate an objective threshold for signal-noise separation...
rm.matrix.validation: Validate input matrix prior to threshold computation
rm.show.plots: Display a sequence of plots on screen
rm.spacing.distribution: Plot the empirical distribution of the eigenvalue spacings
RMThreshold-internal: Internal functions for the RMThreshold package
RMThreshold-package: Signal-Noise Separation in Correlation Matrices by using...