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Compute and Plot Calibration for Lineup Identification

Source: R/calibration_functions.R
make_calibration.Rd

Main function to compute and plot calibration analysis for eyewitness lineup data.

Usage

make_calibration(
  data,
  confidence_bins = NULL,
  choosers_only = TRUE,
  lineup_size = 6,
  show_plot = TRUE,
  confidence_scale = c("auto", "0-1", "0-100"),
  innocent_suspect = "auto",
  ...
)

Arguments

data

A dataframe with columns: target_present, identification, confidence

confidence_bins

Numeric vector of bin edges (optional)

choosers_only

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

lineup_size

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

show_plot

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

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.

...

Additional arguments passed to make_calibration_gg()

Value

A list containing calibration data, statistics, and plot

Examples

set.seed(123)
n <- 200
lineup_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))
)
cal_result <- make_calibration(lineup_data,
                               confidence_bins = c(0, 60, 80, 100))

print(cal_result)
#> 
#> === Lineup Calibration Analysis ===
#> 
#> Analysis type: Choosers only (suspect IDs)
#> Total N: 103 
#> 
#> Calibration Statistics:
#>   C (Calibration):         0.0521 
#>   O/U (Over/Under):        -0.0149 
#>   NRI (Resolution):        0.0096 
#> 
#> Overall Performance:
#>   Mean Accuracy:      0.515 
#>   Mean Confidence:    0.500 
#> 
#> Calibration Data by Bin:
#> # A tibble: 3 × 7
#>   bin          n mean_confidence accuracy n_correct n_incorrect
#>   <chr>    <dbl>           <dbl>    <dbl>     <dbl>       <dbl>
#> 1 [0,60]      61            28.2    0.475        29          32
#> 2 (60,80]     22            72.0    0.591        13           9
#> 3 (80,100]    20            92.0    0.55         11           9
#> # ℹ 1 more variable: mean_confidence_prop <dbl>
#> 
#> Plot: save this object first, then extract it with x$plot