Apply the LambdaG algorithm

This function calculates the likelihood ratio of grammar models, or \(\lambda_G\), as in Nini et al. (under review). In order to run the analysis as in this paper, all data must be preprocessed using contentmask() with the "algorithm" parameter set to "POSnoise".

Usage

lambdaG(q.data, k.data, ref.data, N = 10, r = 30, cores = NULL)

Arguments

q.data

The questioned or disputed data as a quanteda tokens object with the tokens being sentences (e.g. the output of tokenize_sents()).

k.data

The known or undisputed data as a quanteda tokens object with the tokens being sentences (e.g. the output of tokenize_sents()).

ref.data

The reference dataset as a quanteda tokens object with the tokens being sentences (e.g. the output of tokenize_sents()). This can be the same object as k.data.

N

The order of the model. Default is 10.

r

The number of iterations. Default is 30.

cores

The number of cores to use for parallel processing (the default is one).

Value

The function will test all possible combinations of Q texts and candidate authors and return a data frame containing \(\lambda_G\), an uncalibrated log-likelihood ratio (base 10). \(\lambda_G\) can then be calibrated into a likelihood ratio that expresses the strength of the evidence using calibrate_LLR(). The data frame contains a column called "target" with a logical value which is TRUE if the author of the Q text is the candidate and FALSE otherwise.

References

Nini, A., Halvani, O., Graner, L., Gherardi, V., Ishihara, S. Authorship Verification based on the Likelihood Ratio of Grammar Models. https://arxiv.org/abs/2403.08462v1

Examples

q.data <- enron.sample[1] |> quanteda::tokens("sentence")
k.data <- enron.sample[2:10] |> quanteda::tokens("sentence")
ref.data <- enron.sample[11:ndoc(enron.sample)] |> quanteda::tokens("sentence")
lambdaG(q.data, k.data, ref.data)
#>    K                     Q target  score
#> 1 Kh known [Kh Mail_1].txt   TRUE 39.186
#> 2 Kw known [Kh Mail_1].txt  FALSE -6.720