spatial_cor: Spatial autocorrelation

In anespinosa/netmem: Social Network Measures using Matrices

Spatial autocorrelation

Description

This function calculate some spatial autocorrelations for a sample of networks at different orders (distances).

Usage

spatial_cor(
  A,
  V,
  measures = c("covariance", "correlation", "moran", "geary"),
  mean = TRUE,
  diag = FALSE,
  distance1 = TRUE,
  rowstand = FALSE,
  scale = FALSE
)

Arguments

A	A symmetric matrix
V	A vector
measures	Whether to use the Covariance covariance (default), Correlation correlation, Moran I moran or Geary's C geary
mean	Whether to use the mean of the vector for the measures
diag	Whether to consider the diagonal of the matrix for the measures
distance1	Whether to return only the spatial autocorrelation considering the actor at distance 1
rowstand	Whether to use the row-standardization to estimate Moran I (Anselin, 1995)
scale	Whether to scale Moran I (Anselin, 1995)

Value

This function return the global spatial autocorrelation. Multiple orders can also be computed.

References

Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27(2), 93-115.

Geary, R.C. (1954). “The Contiguity Ratio and Statistical Mapping.” The Incorporated Statistician, 5: 115-145.

Moran, P.A.P. (1950). “Notes on Continuous Stochastic Phenomena.” Biometrika, 37: 17-23.

Examples

A <- matrix(c(
  0, 0, 1, 1,
  0, 0, 1, 0,
  1, 0, 0, 0,
  1, 0, 1, 0
), byrow = TRUE, ncol = 4)
V <- c(2, 2, 1, 1)

spatial_cor(A, V, measures = c("moran"))

anespinosa/netmem documentation built on April 5, 2025, 5:02 p.m.