Block-Permutation Variable Importance — borg

Computes permutation importance that respects spatial structure. Instead of permuting individual rows (which breaks spatial autocorrelation and inflates importance), permutes values within spatial blocks.

Usage

borg_importance(
  model,
  data,
  target,
  coords = NULL,
  predictors = NULL,
  n_blocks = 10,
  n_rep = 10,
  metric = c("rmse", "mae"),
  seed = 42
)

Arguments

model: A fitted model with a predict() method.
data: Data frame with predictor columns.
target: Character. Target variable name.
coords: Character vector of length 2. Coordinate column names. If NULL, falls back to standard row permutation.
predictors: Character vector. Variables to assess. If NULL, uses all columns except target and coords.
n_blocks: Integer. Number of spatial blocks for permutation. Default: 10.
n_rep: Integer. Number of permutation repeats per variable. Default: 10.
metric: Character. "rmse" (default) or "mae".
seed: Integer. Random seed. Default: 42.

Value

A data frame with class "borg_importance" containing:

variable: Predictor name
importance: Mean increase in error after permutation
importance_sd: SD across repeats
rank: Importance rank (1 = most important)

Has an autoplot() method.

Examples

set.seed(42)
d <- data.frame(x = runif(100), y = runif(100),
                 a = rnorm(100), b = rnorm(100))
d$z <- d$a * 2 + rnorm(100, sd = 0.5)
model <- lm(z ~ a + b, data = d)
imp <- borg_importance(model, d, target = "z", coords = c("x", "y"))
imp