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Compute and Plot ROC Curve for Lineup Identification

Source: R/make_roc.R
make_roc.Rd

Main function to compute and plot an ROC curve for eyewitness lineup data. This follows the methodology of Wixted & Mickes (2012) and Mickes (2015).

Usage

make_roc(
  data,
  lineup_size = 6,
  show_plot = TRUE,
  innocent_suspect = "auto",
  ...
)

Arguments

data

A dataframe with the following columns:

  • target_present: Logical. TRUE if guilty suspect in lineup

  • identification: Character. "suspect", "filler", or "reject"

  • confidence: Numeric. Confidence rating

lineup_size

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

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.

...

Additional arguments passed to make_roc_gg()

Value

A list containing:

  • plot: ggplot2 object. Always returned, whatever show_plot is set to; show_plot controls only whether it is displayed immediately

  • roc_data: Dataframe with ROC curve points

  • pauc: Partial area under the curve

  • summary: Summary statistics

Details

ROC analysis measures discriminability - the ability to distinguish innocent from guilty suspects. According to Mickes (2015), ROC analysis is most relevant for policymakers deciding on system variables (e.g., simultaneous vs. sequential lineups, lineup size, etc.).

For estimator variables (e.g., exposure duration, retention interval), use CAC analysis instead (see make_cac).

References

Wixted, J. T., & Mickes, L. (2012). The field of eyewitness memory should abandon probative value and embrace receiver operating characteristic analysis. Perspectives on Psychological Science, 7(3), 275-278.

Mickes, L. (2015). Receiver operating characteristic analysis and confidence-accuracy characteristic analysis in investigations of system variables and estimator variables that affect eyewitness memory. Journal of Applied Research in Memory and Cognition, 4(2), 93-102.

Examples

data(lineup_example)
roc_result <- make_roc(lineup_example)

print(roc_result$pauc)
#> [1] 0.05333776