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Compare ANRI Between Groups with Bootstrap

Source: R/anri_functions.R
compare_anri.Rd

Computes ANRI for two groups and tests whether they differ using bootstrap confidence intervals for the difference.

Usage

compare_anri(
  data,
  group_var,
  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 plus a grouping variable

group_var

Character. Name of grouping variable (must have exactly 2 levels)

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_group1: ANRI for first group

  • anri_group2: ANRI for second group

  • difference: Point estimate of difference (group1 - group2)

  • ci_lower: Lower bound of CI for difference

  • ci_upper: Upper bound of CI for difference

  • significant: Whether difference is significant (CI excludes 0)

  • group_names: Names of the two groups

  • bootstrap_results_group1: Bootstrap object for group 1

  • bootstrap_results_group2: Bootstrap object for group 2

Details

This function: 1. Computes ANRI separately for each group 2. Bootstraps each group independently 3. Computes bootstrap distribution of the difference 4. Tests H0: ANRI_1 = ANRI_2 using CI for difference

**Interpretation**:

  • Positive difference: Group 1 has higher resolution

  • Negative difference: Group 2 has higher resolution

  • CI excludes 0: Statistically significant difference

Examples

# \donttest{
set.seed(123)
n <- 300
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)),
  condition = rep(c("sequential", "simultaneous"), times = n / 2)
)
cmp <- compare_anri(data, group_var = "condition",
                    confidence_bins = seq(0, 100, 20),
                    n_bootstrap = 100, seed = 1)
cmp$difference
#> [1] -0.008229687
c(cmp$ci_lower, cmp$ci_upper)
#> [1] -0.1141146  0.1586221
# }