make_spatial_folds: Create multiple spatially independent training and testing...

In spatialRF: Easy Spatial Modeling with Random Forest

Create multiple spatially independent training and testing folds

Description

Applies make_spatial_fold() to every row in xy.selected, generating one spatially independent fold centered on each focal point. Used for spatial cross-validation in rf_evaluate().

Usage

make_spatial_folds(
  data = NULL,
  dependent.variable.name = NULL,
  xy.selected = NULL,
  xy = NULL,
  distance.step.x = NULL,
  distance.step.y = NULL,
  training.fraction = 0.75,
  n.cores = parallel::detectCores() - 1,
  cluster = NULL
)

Arguments

data	Data frame containing response variable and predictors. Required only for binary response variables.
dependent.variable.name	Character string with the name of the response variable. Must be a column name in data. Required only for binary response variables.
xy.selected	Data frame with columns "x" (longitude), "y" (latitude), and "id" (record identifier). Defines the focal points for fold creation. Typically a spatially thinned subset of xy created with thinning() or thinning_til_n().
xy	Data frame with columns "x" (longitude), "y" (latitude), and "id" (record identifier). Contains all spatial coordinates for the dataset.
distance.step.x	Numeric value specifying the buffer growth increment along the x-axis. Default: NULL (automatically set to 1/1000th of the x-coordinate range).
distance.step.y	Numeric value specifying the buffer growth increment along the y-axis. Default: NULL (automatically set to 1/1000th of the y-coordinate range).
training.fraction	Numeric value between 0.1 and 0.9 specifying the fraction of records to include in the training fold. Default: 0.75.
n.cores	Integer specifying the number of CPU cores for parallel execution. Default: parallel::detectCores() - 1.
cluster	Optional cluster object created with parallel::makeCluster(). If provided, overrides n.cores. User is responsible for stopping the cluster with parallel::stopCluster(). Default: NULL.

Details

This function creates multiple spatially independent folds for spatial cross-validation by calling make_spatial_fold() once for each row in xy.selected. Each fold is created by growing a rectangular buffer from the corresponding focal point until the desired training.fraction is achieved.

Parallel execution:

The function uses parallel processing to speed up fold creation. You can control parallelization with n.cores or provide a pre-configured cluster object.

Typical workflow:

1. Thin spatial points with thinning() or thinning_til_n() to create xy.selected

2. Create spatial folds with this function

3. Use the folds for spatial cross-validation in rf_evaluate()

Value

List where each element corresponds to a row in xy.selected and contains:

• training: Integer vector of record IDs (from xy$id) in the training fold.

• testing: Integer vector of record IDs (from xy$id) in the testing fold.

See Also

make_spatial_fold(), rf_evaluate(), thinning(), thinning_til_n()

Other preprocessing: auto_cor(), auto_vif(), case_weights(), default_distance_thresholds(), double_center_distance_matrix(), is_binary(), make_spatial_fold(), the_feature_engineer(), weights_from_distance_matrix()

Examples

data(plants_df, plants_xy)

# Thin to 10 focal points to speed up example
xy.thin <- thinning_til_n(
  xy = plants_xy,
  n = 10
)

# Create spatial folds centered on the 10 thinned points
folds <- make_spatial_folds(
  xy.selected = xy.thin,
  xy = plants_xy,
  distance.step.x = 0.05,
  training.fraction = 0.6,
  n.cores = 1
)

# Each element is a fold with training and testing indices
length(folds)  # 10 folds
names(folds[[1]])  # "training" and "testing"

# Visualize first fold (training = red, testing = blue, center = black)
if (interactive()) {
  plot(plants_xy[c("x", "y")], type = "n", xlab = "", ylab = "")
  points(plants_xy[folds[[1]]$training, c("x", "y")], col = "red4", pch = 15)
  points(plants_xy[folds[[1]]$testing, c("x", "y")], col = "blue4", pch = 15)
  points(
    plants_xy[folds[[1]]$training[1], c("x", "y")],
    col = "black",
    pch = 15,
    cex = 2
  )
}

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