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Create spatially independent training and testing folds

Source: R/make_spatial_fold.R
make_spatial_fold.Rd

Generates two spatially independent data folds by growing a rectangular buffer from a focal point until a specified fraction of records falls inside. Used internally by make_spatial_folds() and rf_evaluate() for spatial cross-validation.

Usage

make_spatial_fold(
  data = NULL,
  dependent.variable.name = NULL,
  xy.i = NULL,
  xy = NULL,
  distance.step.x = NULL,
  distance.step.y = NULL,
  training.fraction = 0.8
)

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.i

Single-row data frame with columns "x" (longitude), "y" (latitude), and "id" (record identifier). Defines the focal point from which the buffer grows.

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: 0.8.

Value

List with two elements:

  • 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.

Details

This function creates spatially independent training and testing folds for spatial cross-validation. The algorithm works as follows:

  1. Starts with a small rectangular buffer centered on the focal point (xy.i)

  2. Grows the buffer incrementally by distance.step.x and distance.step.y

  3. Continues growing until the buffer contains the desired number of records (training.fraction * total records)

  4. Assigns records inside the buffer to training and records outside to testing

Special handling for binary response variables:

When data and dependent.variable.name are provided and the response is binary (0/1), the function ensures that training.fraction applies to the number of presences (1s), not total records. This prevents imbalanced sampling in presence-absence models.

See also

make_spatial_folds(), rf_evaluate(), is_binary()

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

Examples

data(plants_df, plants_xy)

# Create spatial fold centered on first coordinate
fold <- make_spatial_fold(
  xy.i = plants_xy[1, ],
  xy = plants_xy,
  training.fraction = 0.6
)

# View training and testing record IDs
fold$training
#>   [1]   1   2   4   5   6   7  10  12  14  15  19  20  21  22  23  26  28  31
#>  [19]  32  33  34  35  36  37  45  47  48  50  51  54  56  58  59  62  63  64
#>  [37]  65  66  67  71  73  74  77  78  79  80  81  83  87  94  95  96  97  98
#>  [55]  99 100 102 104 106 107 108 109 110 111 112 113 114 115 118 119 122 125
#>  [73] 126 127 129 131 135 136 138 139 141 145 151 153 154 155 156 157 159 160
#>  [91] 161 162 163 165 166 168 171 174 177 178 179 181 182 185 186 187 188 189
#> [109] 191 192 193 194 195 198 201 204 205 206 207 208 209 210 212 213 214 215
#> [127] 216 218 219 220 221 222 223 224 226 227
fold$testing
#>  [1]   3   8   9  11  13  16  17  18  24  25  27  29  30  38  39  40  41  42  43
#> [20]  44  46  49  52  53  55  57  60  61  68  69  70  72  75  76  82  84  85  86
#> [39]  88  89  90  91  92  93 101 103 105 116 117 120 121 123 124 128 130 132 133
#> [58] 134 137 140 142 143 144 146 147 148 149 150 152 158 164 167 169 170 172 173
#> [77] 175 176 180 183 184 190 196 197 199 200 202 203 211 217 225

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