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Compute a spatial autocorrelation matrix

Source: R/spatial_weights.R
spatial_autocor.Rd

This function computes a spatial autocorrelation matrix using a radius-based nearest neighbor search. The function leverages the mgcv package to fit a generalized additive model (GAM) to the data and constructs the autocorrelation matrix using the fitted model.

Usage

spatial_autocor(X, cds, radius = 8, nsamples = 1000, maxk = 64)

Arguments

X

A numeric matrix or data.frame, where each column represents a variable and each row represents an observation.

cds

A numeric matrix or data.frame of spatial coordinates (x, y, or more dimensions) with the same number of rows as X.

radius

A positive numeric value representing the search radius for the radius-based nearest neighbor search. Default is 8.

nsamples

A positive integer indicating the number of samples to be taken for fitting the GAM. Default is 1000.

maxk

Maximum number of neighbors to request from the NN search before radius filtering (prevents O(n^2) memory). Default 64.

Value

A sparse matrix representing the spatial autocorrelation matrix for the input data.

Examples

# \donttest{
set.seed(1)
cds <- as.matrix(expand.grid(1:10, 1:10))
X <- matrix(rnorm(5*nrow(cds)), nrow=5, ncol=nrow(cds))
S <- spatial_autocor(X, cds, radius=5, nsamples=100, maxk=50)
dim(S)
#> [1] 100 100
# }