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Spatial Model Evaluation Metrics

Source: R/spatial_metrics.R
spatial_metrics.Rd

Calculates standard model evaluation metrics for comparing observed and predicted values. These metrics are commonly used in machine learning and spatial modeling.

Usage

spatial_metrics(observed, predicted, na.rm = TRUE)

Arguments

observed

Numeric vector of observed values

predicted

Numeric vector of predicted values

na.rm

Logical, whether to remove NA values (default: TRUE)

Value

An object of class "spatial_metrics" containing:

n

Number of observations

RMSE

Root Mean Square Error

MAE

Mean Absolute Error

R2

R-squared (coefficient of determination)

MAPE

Mean Absolute Percentage Error (when applicable)

Details

The function calculates the following metrics:

  • RMSE: sqrt(mean((observed - predicted)^2))

  • MAE: mean(abs(observed - predicted))

  • R2: 1 - sum((observed - predicted)^2) / sum((observed - mean(observed))^2)

  • MAPE: mean(abs((observed - predicted) / observed)) * 100 (only when no zeros in observed)

See also

Other model evaluation functions: compare_cv()

Examples

observed <- c(1, 2, 3, 4, 5)
predicted <- c(1.1, 2.2, 2.8, 4.1, 4.9)
metrics <- spatial_metrics(observed, predicted)
print(metrics)
#> Model Evaluation Metrics
#> =======================
#> Number of observations: 5 
#> RMSE: 0.1483 
#> MAE: 0.14 
#> R2: 0.989 
#> MAPE: 6.23 %