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Compute and Plot CAC for Lineup Identification

Source: R/cac_functions.R
make_cac.Rd

Main function to compute and plot CAC (Confidence-Accuracy Characteristic) for eyewitness lineup data.

Usage

make_cac(
  data,
  lineup_size = 6,
  confidence_bins = NULL,
  show_plot = TRUE,
  innocent_suspect = "auto",
  base_rate = NULL,
  ...
)

Arguments

data

A dataframe with columns: target_present, identification, confidence

lineup_size

Integer. Number of people in lineup (default = 6)

confidence_bins

Numeric vector of bin edges (optional)

show_plot

Logical. Whether to display the plot (default = TRUE)

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.

base_rate

See make_cacdata.

...

Additional arguments passed to make_cac_gg()

Value

A list containing CAC data and plot

Examples

data(lineup_example)
cac_result <- make_cac(lineup_example, confidence_bins = c(0, 60, 80, 100))

print(cac_result)
#> 
#> === Lineup CAC Analysis ===
#> 
#> Overall Accuracy: 0.825 
#> Total Suspect IDs: 503 
#> Lineup size: 6 
#> 
#> CAC Data:
#> # A tibble: 3 × 6
#>   confidence n_correct n_incorrect n_total accuracy     se
#>   <chr>          <int>       <int>   <int>    <dbl>  <dbl>
#> 1 [0,60]           233          72     305    0.764 0.0243
#> 2 (60,80]           82          12      94    0.872 0.0344
#> 3 (80,100]         100           4     104    0.962 0.0189
#> 
#> Plot: use plot(x), or save the object and extract x$plot