plot_evaluation: Visualize spatial cross-validation results
In spatialRF: Easy Spatial Modeling with Random Forest

plot_evaluation

R Documentation

Visualize spatial cross-validation results

Description

Creates boxplots comparing model performance metrics across training, testing, and full datasets from spatial cross-validation performed by rf_evaluate(). Displays distributions of R-squared, RMSE, and other metrics across all spatial folds.

Usage

plot_evaluation(
  model,
  fill.color = viridis::viridis(3, option = "F", alpha = 0.8, direction = -1),
  line.color = "gray30",
  verbose = TRUE,
  notch = TRUE
)

Arguments

`model`	Model fitted with `rf_evaluate()`. Must be of class "rf_evaluate".
`fill.color`	Character vector with three colors (one for each model type: Testing, Training, Full) or a function that generates a color palette. Accepts hexadecimal codes (e.g., `c("#440154FF", "#21908CFF", "#FDE725FF")`) or palette functions (e.g., `viridis::viridis(3)`). Default: `viridis::viridis(3, option = "F", alpha = 0.8, direction = -1)`.
`line.color`	Character string specifying the color of boxplot borders. Default: `"gray30"`.
`verbose`	Logical. If `TRUE`, prints the plot to the graphics device. Default: `TRUE`.
`notch`	Logical. If `TRUE`, displays notched boxplots where notches represent approximate 95% confidence intervals around the median. Non-overlapping notches suggest significant differences between medians. Default: `TRUE`.

Details

This function visualizes the distribution of performance metrics across spatial folds, with separate boxplots for three model variants:

Testing: Performance on spatially independent testing folds (most reliable estimate of generalization)
Training: Performance on training folds (typically optimistic)
Full: Performance on the complete dataset (reference baseline)

Interpreting the plot:

The boxplots show the distribution of each metric across all spatial folds. Ideally:

Testing performance should be reasonably close to training performance (indicates good generalization)
Large gaps between training and testing suggest overfitting
Low variance across folds indicates stable, consistent model performance
High variance suggests performance depends strongly on spatial location

The plot includes a title showing the number of spatial folds used in the evaluation.

Available metrics:

Displayed metrics depend on the response variable type:

Continuous response: R-squared, RMSE (Root Mean Squared Error), NRMSE (Normalized RMSE)
Binary response: AUC (Area Under ROC Curve), pseudo R-squared

Value

ggplot object that can be further customized or saved. The plot displays boxplots of performance metrics (R-squared, RMSE, NRMSE, pseudo R-squared, or AUC depending on model type) across spatial folds, faceted by metric.

Examples

if(interactive()){

data(plants_rf, plants_xy)

# Perform spatial cross-validation
plants_rf <- rf_evaluate(
  model = plants_rf,
  xy = plants_xy,
  repetitions = 5,
  n.cores = 1
)

# Visualize evaluation results
plot_evaluation(plants_rf)

# Without notches for simpler boxplots
plot_evaluation(plants_rf, notch = FALSE)

# Custom colors
plot_evaluation(
  plants_rf,
  fill.color = c("#E64B35FF", "#4DBBD5FF", "#00A087FF")
)

# Print summary statistics
print_evaluation(plants_rf)

# Extract evaluation data for custom analysis
evaluation_data <- get_evaluation(plants_rf)
head(evaluation_data)

}

spatialRF documentation built on Dec. 20, 2025, 1:07 a.m.