This function is used to transform the scores returned by any of the authorship analysis functions into a Log-Likelihood Ratio (LLR).
Arguments
- calibration.dataset
A data frame containing the calibration dataset, typically the output of an authorship analysis function like
impostors().- dataset
A data frame containing the scores that have to be calibrated into LLRs using the calibration dataset. This is typically the result of applying a function like
impostors()to the Q texts.- latex
A logical value. If FALSE (default), then the hypothesis labels are printed as plain text (Hp/Hd). If TRUE the labels are written to be read in LaTeX ($H_p$/$H_d$).
Value
The function returns a data frame with the LLRs (base 10), as well as the verbal label according to Marquis et al (2016) and a verbal interpretation of the results.
References
Marquis, Raymond, Alex Biedermann, Liv Cadola, Christophe Champod, Line Gueissaz, Geneviève Massonnet, Williams David Mazzella, Franco Taroni & Tacha Hicks. 2016. Discussion on how to implement a verbal scale in a forensic laboratory: Benefits, pitfalls and suggestions to avoid misunderstandings. Science & Justice 56(5). 364–370. https://doi.org/10.1016/j.scijus.2016.05.009.
Examples
calib <- data.frame(score = c(0.5, 0.2, 0.8, 0.01, 0.6), target = c(TRUE, FALSE, TRUE, FALSE, TRUE))
q <- data.frame(score = c(0.6, 0.002))
calibrate_LLR(calib, q)
#> score LLR Verbal label
#> 1 0.600 16.135 Extremely strong support for Hp
#> 2 0.002 -22.723 Extremely strong support for Hd
#> Interpretation
#> 1 The similarity is 13645831365889294 times more likely to be observed in the case of Hp than in the case of Hd
#> 2 The similarity is 5.28445251775179e+22 times more likely to be observed in the case of Hd than in the case of Hp