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Block Bootstrap Confidence Intervals for CV Metrics

Source: R/borg_bootstrap.R
borg_bootstrap.Rd

Estimates confidence intervals for cross-validation performance metrics using spatial block bootstrap. Standard bootstrap assumes independent observations; block bootstrap preserves the dependency structure detected by borg_diagnose().

Usage

borg_bootstrap(
  model,
  data,
  target,
  coords = NULL,
  formula = NULL,
  fit_fun = NULL,
  folds = NULL,
  metric = c("rmse", "mae", "rsq"),
  n_boot = 200,
  n_blocks = 10,
  conf_level = 0.95,
  seed = 42
)

Arguments

model

A fitted model with a predict() method, or a model-fitting function (see fit_fun).

data

Data frame with predictors and target.

target

Character. Target variable name.

coords

Character vector of length 2. Coordinate column names. If provided, uses spatial blocking. Otherwise, uses random blocks.

formula

Model formula. Required if model is a function.

fit_fun

Function. Model fitting function. If NULL, attempts to refit from model's call.

folds

A borg_cv object or fold list. If provided, bootstrap resamples within the fold structure.

metric

Character. Performance metric: "rmse" (default), "mae", "rsq".

n_boot

Integer. Number of bootstrap replicates. Default: 200.

n_blocks

Integer. Number of spatial blocks for resampling. Default: 10.

conf_level

Numeric. Confidence level. Default: 0.95.

seed

Integer. Random seed. Default: 42.

Value

A list with class "borg_bootstrap" containing:

estimate

Point estimate of the metric

ci_lower

Lower confidence bound

ci_upper

Upper confidence bound

conf_level

Confidence level

boot_distribution

Numeric vector of bootstrap estimates

se

Bootstrap standard error

bias

Bootstrap bias estimate

metric

Metric name

n_boot

Number of bootstrap replicates

method

"spatial_block" or "random_block"

Has print() and autoplot() methods.

Details

Spatial block bootstrap

Data is partitioned into spatial blocks using k-means clustering on coordinates. Each bootstrap replicate resamples blocks (with replacement), then includes all observations from selected blocks. This preserves within-block spatial correlation while generating valid resamples.

CI methods

Returns bias-corrected and accelerated (BCa) intervals when possible, falling back to percentile intervals.

References

Lahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer.

Examples

set.seed(42)
d <- data.frame(x = runif(150), y = runif(150), a = rnorm(150))
d$z <- 2 * d$a + rnorm(150, sd = 0.5)
model <- lm(z ~ a, data = d)
boot <- borg_bootstrap(model, d, target = "z", coords = c("x", "y"),
                         n_boot = 50)
boot