spatialPartition: Spatial Variance Partitioning with MEMs

In spacc: Fast Spatial Species Accumulation Curves

Spatial Variance Partitioning with MEMs

Description

Partition diversity variation into spatial and non-spatial components using forward selection of Moran's Eigenvector Maps.

Usage

spatialPartition(x, mem, metric = NULL, forward = TRUE, alpha = 0.05)

Arguments

x	A spacc result object (e.g., spacc_metrics, spacc_alpha, spacc_hill) or a numeric vector of per-site diversity values.
mem	A spacc_mem object from spatialEigenvectors().
metric	Character. Which metric to extract from x (passed to internal extraction). Default NULL uses the first available.
forward	Logical. Use forward selection? Default TRUE. If FALSE, all MEMs are included.
alpha	Numeric. Significance threshold for forward selection. Default 0.05.

Details

Forward selection of MEMs proceeds by adding the MEM that most improves the model AIC at each step, stopping when no MEM improves AIC by more than 2 units or when p > alpha.

Value

An object of class spacc_mem_partition containing:

r_squared_spatial	R-squared of the spatial model
r_squared_total	Total R-squared (same as spatial here)
selected_mems	Names of selected MEM vectors
n_selected	Number of selected MEMs
anova_table	ANOVA table from the final model
coefficients	Model coefficients

See Also

spatialEigenvectors() for computing MEMs

Examples

coords <- data.frame(x = runif(30), y = runif(30))
species <- matrix(rpois(30 * 15, 2), nrow = 30)

mem <- spatialEigenvectors(coords)
alpha <- alphaDiversity(species, q = 0)
part <- spatialPartition(alpha$q0, mem)
print(part)

spacc documentation built on June 20, 2026, 5:07 p.m.