Draws the empirical correlogram produced by [hm_empirical_correlogram()]: average Kendall \(\hat{\tau}\) as a function of inter-site distance, with point size proportional to the number of cell pairs in each bin. Reproduces the style of Fig. 8 in Clavijo Mesa et al. (2026, SF paper).
Usage
hm_plot_correlogram(
corg,
title = NULL,
colour = "#2166AC",
show_points = TRUE,
show_zero = TRUE,
ylim = NULL
)Arguments
- corg
A data.frame returned by [hm_empirical_correlogram()], with columns `bin_center`, `mean_tau`, and `n_pairs`.
- title
Optional plot title.
- colour
Line and point colour. Defaults to `"#2166AC"` (steel blue).
- show_points
Logical. If `TRUE` (default), draws points at each bin centre sized by the number of pairs.
- show_zero
Logical. If `TRUE` (default), adds a horizontal dashed line at \(\tau = 0\).
- ylim
Optional numeric vector of length 2 for the y-axis range. If `NULL` (default), the range is set automatically.
Details
A horizontal dashed reference line is drawn at \(\tau = 0\) to help identify the distance at which spatial dependence becomes negligible. Point size is scaled by `sqrt(n_pairs)` so bins with more pairs are visually more prominent without dominating the plot.
References
Clavijo Mesa, M.V., Broggi, M., Di Maio, F. & Zio, E. (2026). Inoperability assessment of interdependent critical infrastructures exposed to natural hazards considering climate change. International Journal of Disaster Risk Reduction, 141, 106172.
Examples
if (FALSE) { # \dontrun{
X_land <- hm_filter_land_cells(X)
fits <- hm_fit_marginals(X_land)
pit <- hm_pit_transform(X_land, fits)
tau <- hm_empirical_kendall(pit$Z)
D <- hm_distance_matrix(attr(X_land, "coords"))
corg <- hm_empirical_correlogram(tau, D)
hm_plot_correlogram(corg,
title = "Empirical correlogram — Copernicus wind gust")
} # }