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Main Function to Compute and Visualize EIG

Source: R/eig_functions.R
make_eig.Rd

Convenience wrapper that computes EIG and creates visualizations.

Usage

make_eig(
  data,
  prior_guilt = 0.5,
  confidence_bins = NULL,
  show_plot = TRUE,
  plot_type = "both",
  lineup_size = 6,
  innocent_suspect = "auto"
)

Arguments

data

A dataframe with columns: target_present, identification, confidence

prior_guilt

Numeric. Prior probability that suspect is guilty (default = 0.5)

confidence_bins

Numeric vector of bin edges (optional)

show_plot

Logical. Whether to create plots (default = TRUE)

plot_type

Character. Which plot to create: "ig" (information gain), "posteriors" (posterior probabilities), or "both" (default)

lineup_size

Nominal lineup size used 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 of class "lineup_eig" containing:

  • eig: Expected Information Gain value

  • response_data: Full response category data

  • plot_ig: ggplot of information gain (if requested)

  • plot_posteriors: ggplot of posteriors (if requested)

  • ... (additional components from compute_eig)

Examples

data(lineup_example)
# With confidence binning
result <- make_eig(lineup_example, confidence_bins = c(0, 60, 80, 100))

# Access plots
result$plot_ig

result$plot_posteriors