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Bootstrap Confidence Intervals for ANRI

Source: R/anri_functions.R
bootstrap_anri.Rd

Computes bootstrap confidence intervals around ANRI estimates using percentile method.

Usage

bootstrap_anri(
  data,
  confidence_bins,
  choosers_only = TRUE,
  lineup_size = 6,
  n_bootstrap = 1000,
  conf_level = 0.95,
  seed = NULL,
  confidence_scale = c("auto", "0-1", "0-100"),
  innocent_suspect = "auto"
)

Arguments

data

Dataframe with standard lineup format

confidence_bins

Numeric vector of bin edges

choosers_only

Logical. Whether to analyze only suspect IDs (default = TRUE)

lineup_size

Integer. Lineup size (default = 6)

n_bootstrap

Integer. Number of bootstrap replications (default = 1000)

conf_level

Numeric. Confidence level for CIs (default = 0.95)

seed

Integer. Random seed for reproducibility (default = NULL)

confidence_scale

How the confidence scale is interpreted: "auto" (default), "0-1", or "0-100". See make_calibration_data.

innocent_suspect

How innocent-suspect identifications in target-absent lineups are obtained. "designated": the lineups had a designated innocent suspect, so target-absent "suspect" rows are counted directly, and zero such rows means a false-ID rate of zero. "estimated": there was no designated innocent suspect, so the innocent-suspect count is estimated as filler IDs divided by lineup_size. "auto" (the default) chooses "designated" when any target-absent row is a suspect ID or the data have a non-missing suspect_pos column, and "estimated" otherwise, with a message.

Value

A list containing:

  • anri: Point estimate of ANRI

  • nri: Point estimate of NRI

  • ci_lower: Lower bound of bootstrap CI

  • ci_upper: Upper bound of bootstrap CI

  • conf_level: Confidence level used

  • n_bootstrap: Number of bootstrap replications

  • bootstrap_distribution: Vector of bootstrap ANRI values

Details

Bootstrap resampling procedure: 1. Resample observations with replacement 2. Compute ANRI for each bootstrap sample 3. Construct percentile-based confidence interval

The percentile method uses the empirical quantiles of the bootstrap distribution. For a 95

References

Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Bootstrap. Chapman & Hall/CRC.

Examples

# \donttest{
set.seed(123)
n <- 200
data <- data.frame(
  target_present = rep(c(TRUE, FALSE), each = n / 2),
  identification = sample(c("suspect", "filler", "reject"), n,
                          replace = TRUE, prob = c(0.5, 0.25, 0.25)),
  confidence = round(runif(n, 0, 100))
)
boot_result <- bootstrap_anri(data, confidence_bins = seq(0, 100, 20),
                              n_bootstrap = 200, seed = 1)
c(boot_result$ci_lower, boot_result$ci_upper)
#> [1] -0.03187222  0.12030595
# }