Compute the empirical spatial correlogram

Bins pairwise Kendall correlations by inter-site distance to give average correlation as a function of distance. The result is the key input for fitting a covariance model in [hm_fit_correlation_model()], following Eq. 5–6 of Clavijo Mesa et al. (2026, SF paper).

Usage

hm_empirical_correlogram(tau, D, n_bins = 30L, max_dist = NULL)

Arguments

tau

Symmetric numeric matrix of pairwise Kendall correlations from [hm_empirical_kendall()].

D

Symmetric numeric distance matrix in km from [hm_distance_matrix()]. Must have the same dimensions as tau.

n_bins

Number of equal-width distance bins. Defaults to 30.

max_dist

Maximum distance in km to include. Defaults to the observed maximum.

Value

A data.frame with columns bin_center, mean_tau, n_pairs, bin_lo, and bin_hi.

References

Clavijo Mesa, M.V., Broggi, M., Di Maio, F. & Zio, E. (2026). International Journal of Disaster Risk Reduction, 141, 106172.

See also

[hm_empirical_kendall()], [hm_distance_matrix()], [hm_fit_correlation_model()]

Examples

if (FALSE) { # \dontrun{
tau  <- hm_empirical_kendall(pit$Z, cell_idx = 1:50)
D    <- hm_distance_matrix(coords[1:50, ])
corg <- hm_empirical_correlogram(tau, D, n_bins = 20)
head(corg)
} # }