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Prepare EIG Data from Lineup Identification

Source: R/eig_functions.R
make_eig_data.Rd

Prepares response category data for Expected Information Gain (EIG) analysis following Starns et al. (2023). Creates response categories from identification decisions and confidence levels.

Usage

make_eig_data(
  data,
  confidence_bins = NULL,
  lineup_size = 6,
  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

confidence_bins

Numeric vector of bin edges for grouping confidence (e.g., c(0, 60, 80, 100) creates bins 0-60, 61-80, 81-100). If NULL, uses individual confidence levels.

lineup_size

Nominal lineup size. Used to split target-absent filler identifications when no designated innocent suspect is present.

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:

  • response_data: Dataframe with response categories and frequencies

  • n_guilty: Total number of target-present lineups

  • n_innocent: Total number of target-absent lineups

  • confidence_bins: The binning specification used (if any)

Details

Response categories are defined as combinations of identification decision (suspect/filler/reject) and confidence level or bin. This function counts the frequency of each response category separately for target-present (guilty) and target-absent (innocent) lineups.

References

Starns, J. J., Cohen, A. L., & Rotello, C. M. (2023). A complete method for assessing the effectiveness of eyewitness identification procedures: Expected information gain. Psychological Review, 130(3), 677–719.

Examples

data(lineup_example)
eig_data <- make_eig_data(lineup_example, confidence_bins = c(0, 60, 80, 100))
eig_data$response_data
#> # A tibble: 7 × 7
#>   response         n_guilty n_innocent p_x_given_guilty p_x_given_innocent
#>   <chr>               <dbl>      <dbl>            <dbl>              <dbl>
#> 1 filler_(60,80]         40         59           0.0571            0.0843 
#> 2 filler_(80,100]        13         30           0.0186            0.0429 
#> 3 filler_[0,60]         165        322           0.236             0.46   
#> 4 reject_[0,60]          67        201           0.0957            0.287  
#> 5 suspect_(60,80]        82         12           0.117             0.0171 
#> 6 suspect_(80,100]      100          4           0.143             0.00571
#> 7 suspect_[0,60]        233         72           0.333             0.103  
#> # ℹ 2 more variables: identification <chr>, confidence_level <chr>