dist.from.cov: Compute a squared distance matrix from a covariance matrix.

In rwc: Random Walk Covariance Models

Compute a squared distance matrix from a covariance matrix.

Description

This computes a squared distance matrix from a covariance matrix, or other positive semi-definite matrix. The resulting squared distance matrix is the variogram matrix or the resistance distance matrix under a random walk model for connectivity as in Hanks and Hooten (2013).

Usage

dist.from.cov(Sigma)

Arguments

Sigma

A symmetric positive definite matrix.

Value

A negative definite matrix of the same dimensions as Sigma.

Author(s)

Ephraim M. Hanks

References

Hanks and Hooten 2013. Circuit theory and model-based inference for landscape connectivity. Journal of the American Statistical Association. 108(501), 22-33.

Examples

## create a Wishart covariance matrix with independent structure

Z=matrix(rnorm(10*20),ncol=20,nrow=10)
W=Z %*% t(Z)

## convert to resistance distance matrix
D=dist.from.cov(W)

## convert back to covariance matrix
C=cov.from.dist(D)

## compare C and W
max(abs(C-W))

rwc documentation built on April 4, 2025, 4:40 a.m.