For inspection, custom interpolation, or reproducibility. In the paper this is the prevalence-conditioned threshold table.
Examples
head(grass_reference_table())
#> prevalence reference_level metric reference J
#> 1 0.01 0.7 AC1 0.2718851 0.4
#> 2 0.05 0.7 AC1 0.2563739 0.4
#> 3 0.10 0.7 AC1 0.2380261 0.4
#> 4 0.15 0.7 AC1 0.2210682 0.4
#> 5 0.20 0.7 AC1 0.2057489 0.4
#> 6 0.25 0.7 AC1 0.1923077 0.4
head(grass_reference_table(reference_level = 0.85))
#> prevalence reference_level metric reference J
#> 127 0.01 0.85 AC1 0.6532018 0.7
#> 128 0.05 0.85 AC1 0.6349059 0.7
#> 129 0.10 0.85 AC1 0.6117540 0.7
#> 130 0.15 0.85 AC1 0.5887428 0.7
#> 131 0.20 0.85 AC1 0.5664740 0.7
#> 132 0.25 0.85 AC1 0.5456570 0.7