Creates a formatted table comparing model fits for publication or reporting.
Value
For format = "console", the comparison dataframe. For
"markdown" and "latex", a knitr_kable object, which
is a character vector with **one element per line**. Print it with
cat(x, sep = "\n") inside a chunk using results = "asis";
plain cat(x) joins the lines with spaces and the table collapses
into a single paragraph.
Details
Creates a publication-ready table with key statistics for each model:
2-HT: Parameters (dP, dA, b, g), AIC, BIC
EIG: Information gain, efficiency
Full ROC: AUC
Examples
data(lineup_example)
comparison <- compare_models(lineup_example)
#> Fitting 2-HT model...
#> Computing EIG...
#> Computing Full ROC...
#> Ordering categories by their sample diagnosticity ratios is optimistically biased: the ordering is estimated from the same data used to compute the AUC. With roughly 700 lineups per arm the bias can be substantial. Use order = "apriori" for inference or between-condition comparisons; see vignette("fullroc_analysis").
#>
#> Model comparison complete!
format_comparison_table(comparison)
#> Model Measure Value
#> 1 2-HT (Winter et al., 2022) Log-likelihood -1280.6300
#> 2 2-HT (Winter et al., 2022) AIC 2569.2600
#> 3 2-HT (Winter et al., 2022) BIC 2590.2400
#> dP 2-HT (Winter et al., 2022) dP (detection presence) 0.5260
#> dA 2-HT (Winter et al., 2022) dA (detection absence) 0.1070
#> 11 EIG (Starns et al., 2023) Expected Information Gain 0.2189
#> 12 EIG (Starns et al., 2023) Information Efficiency 21.9000
#> 13 EIG (Starns et al., 2023) Prior Entropy 1.0000
#> 14 Full ROC (Smith & Yang, 2020) Full AUC 0.7775
#> 15 Full ROC (Smith & Yang, 2020) Operating Points 19.0000
#> 16 Full ROC (Smith & Yang, 2020) Max Hit Rate 1.0000
#> Interpretation
#> 1 Model fit quality
#> 2 Lower is better
#> 3 Lower is better
#> dP 0 to 1
#> dA 0 to 1
#> 11 Higher is better (bits)
#> 12 Percentage (0-100%)
#> 13 Maximum possible IG
#> 14 Higher is better (0-1)
#> 15 Number of decision criteria
#> 16 Cumulative (0-1)