spatialRF (version 1.1.5)

get_response_curves: Extract response curve data for plotting

Description

Extracts data for plotting partial dependence (response) curves showing how predictions vary with each predictor from models fitted with rf(), rf_repeat(), or rf_spatial().

Usage

get_response_curves(
  model = NULL,
  variables = NULL,
  quantiles = c(0.1, 0.5, 0.9),
  grid.resolution = 200,
  verbose = TRUE
)

Value

Data frame with the following columns:

• response: Predicted response values.

• predictor: Predictor values along the gradient.

• quantile: Factor indicating which quantile was used to fix other predictors.

• model: Model index (only for rf_repeat() models with multiple repetitions).

• predictor.name: Character name of the focal predictor.

• response.name: Character name of the response variable.

Arguments

model

Model object from rf(), rf_repeat(), or rf_spatial().

variables

Character vector of predictor names to plot. If NULL, automatically selects the top 50% most important variables. Default: NULL.

quantiles

Numeric vector of quantiles (0 to 1) at which to fix non-plotted predictors. Multiple quantiles show response variation under different scenarios. Default: c(0.1, 0.5, 0.9).

grid.resolution

Integer (20 to 500) specifying the number of points along the predictor axis. Higher values produce smoother curves. Default: 200.

verbose

Logical. If TRUE, prints progress messages. Default: TRUE.

Details

Response curves (also called partial dependence plots) show how predicted values change as a focal predictor varies while holding other predictors constant at specified quantile values. This reveals the marginal effect of each predictor.

The function generates curves by:

1. Creating a grid of values for the focal predictor

2. Fixing non-plotted predictors at each quantile (e.g., 0.1, 0.5, 0.9)

3. Predicting responses across the grid

4. Repeating for each selected predictor and quantile combination

Multiple quantiles reveal whether the effect of a predictor is consistent across different environmental contexts (parallel curves) or varies depending on other conditions (non-parallel curves).

See Also

rf(), rf_repeat(), rf_spatial(), plot_response_curves(), get_importance()

Other model_info: get_evaluation(), get_importance(), get_importance_local(), get_moran(), get_performance(), get_predictions(), get_residuals(), get_spatial_predictors(), print.rf(), print_evaluation(), print_importance(), print_moran(), print_performance()

Examples

data(plants_rf)

# Extract response curve data for plotting
curves <- get_response_curves(
  model = plants_rf,
  variables = NULL,  # auto-select important variables
  quantiles = c(0.1, 0.5, 0.9)
)

# View structure
head(curves)
str(curves)

# Check unique predictors included
unique(curves$predictor.name)