Check Residual Spatial Autocorrelation — borg_check

After fitting a model, checks whether residuals still exhibit spatial autocorrelation. If they do, the model has not fully captured the spatial process and predictions may be biased.

Usage

borg_check_residuals(
  model,
  data = NULL,
  coords = NULL,
  alpha = 0.05,
  crs = NULL
)

Arguments

model: A fitted model with a residuals() method, or a numeric vector of residuals.
data: Data frame with coordinate columns (needed if model is a numeric vector).
coords: Character vector of length 2. Coordinate column names.
alpha: Numeric. Significance level for Moran's I test. Default: 0.05.
crs: Optional coordinate reference system (an sf CRS, EPSG code, or PROJ string). When geographic (lon/lat), distances use the Haversine formula; otherwise planar Euclidean.

Value

A list with class "borg_residual_check" containing:

morans_i: Moran's I of residuals
p_value: P-value from Moran's I test
significant: Logical. Whether residual autocorrelation is significant
variogram: Residual variogram data frame (if computed)
assessment: Character. "clean", "mild", or "strong"

Has an autoplot() method showing the residual variogram.

Examples

set.seed(42)
d <- data.frame(x = runif(100, 0, 100), y = runif(100, 0, 100))
d$z <- sin(d$x / 10) + rnorm(100, sd = 0.5)
model <- lm(z ~ x + y, data = d)
check <- borg_check_residuals(model, d, coords = c("x", "y"))
check