Spatial heteroscedasticity test

Evaluates spatial clustering in squared residuals using Moran's I with a permutation reference distribution. Significant positive autocorrelation in the squared residuals is evidence of spatially varying variance.

Usage

performSpatialHeteroTest(
  model,
  data,
  listw,
  permutations = 499,
  zero.policy = NULL
)

Arguments

model

A fitted stats::lm object describing the mean structure whose residual variance is to be assessed.

data

A base::data.frame (or compatible object) containing the variables referenced in model. The data must include all observations used to fit model and should not contain unresolved missing values.

listw

Spatial weights in spdep listw format or a numeric matrix coercible via spdep::mat2listw().

permutations

Number of Monte Carlo permutations used to compute the reference distribution.

zero.policy

Logical flag forwarded to the spatial diagnostic to permit islands with no neighbours.

Value

An htest result with Moran's I statistic applied to squared residuals.

References

Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer.

Bivand, R. S., Pebesma, E., & Gómez-Rubio, V. (2013). Applied Spatial Data Analysis with R (2nd ed.). Springer.

Examples

if (requireNamespace("spdep", quietly = TRUE)) {
  data(mtcars)
  coords <- cbind(runif(nrow(mtcars)), runif(nrow(mtcars)))
  nb <- spdep::knn2nb(spdep::knearneigh(coords, k = 4))
  lw <- spdep::nb2listw(nb)
  model <- lm(mpg ~ wt + hp, data = mtcars)
  performSpatialHeteroTest(model, mtcars, listw = lw, permutations = 199)
}
#> 
#> 	Spatial heteroscedasticity test (Moran's I on squared residuals)
#> 
#> data:  mpg ~ wt + hp
#> statistic = -0.069454, permutations = 199, p-value = 0.555
#> alternative hypothesis: greater
#>