Spatial Variance Partitioning with MEMs

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.

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

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.

See also

spatialEigenvectors() for computing MEMs

Examples

# \donttest{
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)
# }