Compute Correlation Matrix Between Family Indices

Calculates pairwise correlations between family composite indices to identify synergies and trade-offs across ecosystem service dimensions.

Usage

compute_family_correlations(units, families = NULL, method = "pearson")

Arguments

units

sf object with computed family indices (family_*)

families

Character vector of family column names to analyze. If NULL (default), auto-detects all columns starting with "family_"

method

Correlation method: "pearson" (default), "spearman", or "kendall"

Value

Correlation matrix (class "matrix") with family names as row/column names

Details

The function computes pairwise correlations between selected family indices to reveal ecological relationships: - **Positive correlations** suggest synergies (e.g., Biodiversity × Age) - **Negative correlations** indicate trade-offs (e.g., Protection × Risk) - **Near-zero correlations** show independence

Missing values (NA) are handled using pairwise complete observations.

Bilingual Support

This function supports bilingual messages via `nemeton_set_language()`.

See also

[identify_hotspots()], [plot_correlation_matrix()]

Other analysis: identify_hotspots()

Examples

if (FALSE) { # \dontrun{
# Load demo data with family indices
data(massif_demo_units)
units <- massif_demo_units
units$family_B <- runif(nrow(units), 30, 90)
units$family_T <- runif(nrow(units), 40, 85)
units$family_C <- runif(nrow(units), 45, 80)

# Compute correlation matrix
corr_matrix <- compute_family_correlations(units)
print(corr_matrix)

# Use Spearman for non-linear relationships
corr_spearman <- compute_family_correlations(units, method = "spearman")

# Analyze specific families only
corr_subset <- compute_family_correlations(
  units,
  families = c("family_B", "family_T")
)
} # }