nd_csd: L_2 Distance of Continuous Spectral Densities

Description Usage Arguments Value References Examples

Description

The method employs spectral density of eigenvalues from Laplacian in that for each, we have corresponding spectral density ρ(w) as a sum of narrow Lorentz distributions with bandwidth parameter. Since it involves integration of a function over the non-compact domain, it may blow up to infinity and the code automatically aborts the process.

Usage

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nd.csd(A, out.dist = TRUE, bandwidth = 1)

Arguments

A

a list of length N containing (M\times M) adjacency matrices.

out.dist

a logical; TRUE for computed distance matrix as a dist object.

bandwidth

common bandwidth of positive real number.

Value

a named list containing

D

an (N\times N) matrix or dist object containing pairwise distance measures.

spectra

an (N\times M-1) matrix where each row is top-M-1 vibrational spectra.

References

\insertRef

ipsen_evolutionary_2002NetworkDistance

Examples

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## load example data
data(graph20)

## compute distance matrix
output = nd.csd(graph20, out.dist=FALSE, bandwidth=1.0)

## visualize
opar = par(no.readonly=TRUE)
par(pty="s")
image(output$D[,20:1], main="two group case", axes=FALSE, col=gray(0:32/32))
par(opar)

NetworkDistance documentation built on Aug. 21, 2021, 5:07 p.m.