Skip to contents

Compute Base-Rate Effect-Equivalency (BREE) Curves

Source: R/bayes_curves_functions.R
make_bree_curve.Rd

Computes BREE curves showing the base-rate shift required for one procedure to produce the same posterior probability as another procedure, following Wells, Yang, & Smalarz (2015).

Usage

make_bree_curve(
  data_proc_a,
  data_proc_b,
  reference_response = "suspect",
  confidence_bins = NULL,
  prior_grid = seq(0.01, 0.99, 0.01),
  lineup_size = 6,
  innocent_suspect = "auto"
)

Arguments

data_proc_a

A dataframe for procedure A with columns: target_present, identification, confidence (standard lineup data format)

data_proc_b

A dataframe for procedure B (same format as proc_a)

reference_response

Character string specifying which response to compare (e.g., "suspect" for suspect IDs). Default = "suspect".

confidence_bins

Optional numeric vector of bin edges if using confidence-based response (e.g., c(0, 60, 80, 100)). If provided, reference_response should specify both identification and bin (e.g., "suspect_(80,100]")

prior_grid

Numeric vector of prior probabilities for procedure A (default: seq(0.01, 0.99, 0.01))

lineup_size

Nominal lineup size for procedures without a designated innocent suspect.

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:

  • bree_curve: Dataframe with prior_a, posterior_a, prior_b, and delta

  • proc_a_likelihoods: Likelihoods for procedure A

  • proc_b_likelihoods: Likelihoods for procedure B

  • reference_response: The response category used for comparison

Details

BREE curves answer the question: "How much would the base rate need to change for Procedure B to yield the same posterior probability as Procedure A?"

For each prior probability in procedure A: 1. Compute the posterior p(guilty|x) for the reference response in procedure A 2. Find the prior probability in procedure B that yields the same posterior 3. Delta = prior_B - prior_A

Interpretation:

  • Delta > 0: Procedure B requires a higher base rate to match A's posterior (A is more diagnostic)

  • Delta < 0: Procedure B requires a lower base rate (B is more diagnostic)

  • Delta = 0: Procedures are equally diagnostic

References

Wells, G. L., Yang, Y., & Smalarz, L. (2015). Eyewitness identification: Bayesian information gain, base-rate effect-equivalency curves, and reasonable suspicion. Law and Human Behavior, 39(2), 99-122.

Examples

data(lineup_example)
odd <- seq(1, nrow(lineup_example), by = 2)
bree <- make_bree_curve(lineup_example[odd, ], lineup_example[-odd, ])
head(bree$bree_curve)
#> # A tibble: 6 × 4
#>   prior_a posterior_a prior_b     delta
#>     <dbl>       <dbl>   <dbl>     <dbl>
#> 1    0.01      0.0433 0.00901 -0.000991
#> 2    0.02      0.0838 0.0180  -0.00196 
#> 3    0.03      0.122  0.0271  -0.00292 
#> 4    0.04      0.157  0.0361  -0.00385 
#> 5    0.05      0.191  0.0452  -0.00477 
#> 6    0.06      0.223  0.0543  -0.00567