Spatial Residual Diagnostics — spatial

Analyzes the spatial distribution of model residuals to detect spatial patterns in prediction errors. This helps identify whether model errors are spatially autocorrelated, which may indicate missing spatial predictors or inappropriate model specification.

Usage

spatial_residuals(observed, predicted, coordinates, x = NULL, y = NULL)

Arguments

observed: Numeric vector of observed values
predicted: Numeric vector of predicted values
coordinates: Coordinate matrix or data.frame with x and y columns
x: Name of x coordinate column if coordinates is a data.frame
y: Name of y coordinate column if coordinates is a data.frame

Value

An object of class "spatial_residuals" containing:

residuals: Residual values (observed - predicted)
observed: Observed values
predicted: Predicted values
coordinates: Coordinate matrix
statistics: Summary statistics of residuals

Details

The function calculates residuals and provides summary statistics:

Mean, median, SD of residuals
Quantiles of residuals
Normality test statistics (if sufficient data)

Spatial autocorrelation analysis can be added in future versions when appropriate dependencies (e.g., spdep) are available.

Examples

observed <- c(1, 2, 3, 4, 5)
predicted <- c(1.1, 2.2, 2.8, 4.1, 4.9)
coords <- cbind(x = c(0, 1, 2, 3, 4), y = c(0, 1, 2, 3, 4))
residuals <- spatial_residuals(observed, predicted, coords)
print(residuals)
#> Spatial Residual Diagnostics
#> ============================
#> Number of observations: 5 
#> 
#> Residual Statistics:
#>   Mean: -0.0200
#>   Median: -0.1000
#>   SD: 0.1643
#>   Min: -0.2000
#>   Max: 0.2000
#>   Q25: -0.1000
#>   Q50: -0.1000
#>   Q75: 0.1000
#> 
#> Coordinate Range:
#>   X: [0.00, 4.00]
#>   Y: [0.00, 4.00]