This computes a covariance matrix from a squared-distance matrix using the centering method of Gower (1996). When the squared-distance matrix is a resistance distance matrix, or a variogram matrix from a spatial model, the resulting covariance matrix is the spatial covariance matrix corresponding to a random walk model for connectivity as in Hanks and Hooten (2013).
A negative semi-definite matrix of squared differences.
A positive semi-definite covariance matrix, for which the variogram (or resistance distance) is equal to the input R matrix.
Ephraim M. Hanks
Hanks and Hooten 2013. Circuit theory and model-based inference for landscape connectivity. Journal of the American Statistical Association. 108(501), 22-33.
Gower 1996. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3), 325-338.
1 2 3 4 5 6 7 8 9 10 11 12
Loading required package: raster Loading required package: sp Loading required package: Matrix Loading required package: mvtnorm Loading required package: MASS Attaching package: 'MASS' The following objects are masked from 'package:raster': area, select  4.907304
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.