Compare Cross-Validation Methods

Compares performance metrics across different cross-validation methods to assess the impact of spatial dependence on model evaluation.

Usage

compare_cv(results_list, methods = NULL)

Arguments

results_list

Named list of spatial_metrics objects from different CV methods

methods

Character vector of method names (optional, uses names of results_list)

Value

An object of class "cv_comparison" containing:

summary

Data frame with summary statistics for each method

by_fold

Data frame with fold-level results

best_method

Method with best performance according to RMSE

comparison

Performance comparison between methods

Details

The function compares metrics across different CV methods:

• Creates summary table with mean and SD of metrics per method

• Identifies best performing method

• Provides fold-level comparison

• Calculates performance differences between methods

This is useful for comparing spatial CV methods against random CV to demonstrate the impact of spatial dependence.

See also

Other model evaluation functions: spatial_metrics()

Examples

if (FALSE) { # \dontrun{
# Train model with different CV methods
results_random <- list(RMSE = 0.5, MAE = 0.4, R2 = 0.8)
results_block <- list(RMSE = 0.7, MAE = 0.6, R2 = 0.6)
results_cluster <- list(RMSE = 0.6, MAE = 0.5, R2 = 0.7)

comparison <- compare_cv(
  list(random = results_random, block = results_block, cluster = results_cluster)
)
print(comparison)
} # }