Compute and Plot DPP — compute

Main wrapper function to compute and plot DPP analysis.

Usage

compute_dpp(
  data,
  lineup_size = 6,
  show_plot = TRUE,
  alpha = NULL,
  beta = NULL,
  innocent_suspect = "auto",
  ...
)

Arguments

data: Dataframe with standard lineup format
lineup_size: Integer. Lineup size (default = 6)
show_plot: Logical. Whether to display plot (default = TRUE)
alpha, beta: Optional Equation 3 weights; see make_dpp.
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 plot_dpp()

Value

An object of class "lineup_dpp": the make_dpp() result plus the plot.

Examples

data(lineup_example)
dpp_result <- compute_dpp(lineup_example, show_plot = FALSE)
print(dpp_result)
#> 
#> === Deviation from Perfect Performance (DPP) ===
#> 
#> Average DPP: 0.7002 
#>   (0 = perfect, 1 = chance, 2 = worst; Smith et al., 2019, Eq. 2)
#> 
#> Point of maximum utility: confidence >= 20 (DPP = 0.5329)
#> 
#> DPP by operating point:
#> # A tibble: 9 × 4
#>   confidence culprit_rate innocent_rate   dpp
#>        <dbl>        <dbl>         <dbl> <dbl>
#> 1        100       0.0971       0       0.903
#> 2         90       0.143        0.00571 0.863
#> 3         80       0.189        0.0143  0.826
#> 4         70       0.26         0.0229  0.763
#> 5         60       0.351        0.03    0.679
#> 6         50       0.436        0.0543  0.619
#> 7         40       0.507        0.0771  0.57 
#> 8         30       0.556        0.103   0.547
#> 9         20       0.593        0.126   0.533
#> 
#> Plot: save this object first, then extract it with x$plot