dist.delta {stylo}

Delta Distance

Description

Function for computing Delta similarity measure of a matrix of values, e.g. a table of word frequencies. Apart from the Classic Delta, two other flavors of the measure are supported: Argamon's Delta and Eder's Delta. There are also non-Delta distant measures available: see e.g. dist.cosine and dist.simple.

Usage

dist.delta(x, scale = TRUE)

dist.argamon(x, scale = TRUE)

dist.eder(x, scale = TRUE)

Arguments

x

a matrix or data table containing at least 2 rows and 2 cols, the samples (texts) to be compared in rows, the variables in columns.

scale

the Delta measure relies on scaled frequencies – if you have your matrix scaled already (i.e. converted to z-scores), switch this option off. Default: TRUE.

Value

The function returns an object of the class dist, containing distances between each pair of samples. To convert it to a square matrix instead, use the generic function as.dist.

Author(s)

Maciej Eder

References

Argamon, S. (2008). Interpreting Burrows's Delta: geometric and probabilistic foundations. "Literary and Linguistic Computing", 23(2): 131-147.

Burrows, J. F. (2002). "Delta": a measure of stylistic difference and a guide to likely authorship. "Literary and Linguistic Computing", 17(3): 267-287.

Eder, M. (2015). Taking stylometry to the limits: benchmark study on 5,281 texts from Patrologia Latina. In: "Digital Humanities 2015: Conference Abstracts".

Eder, M. (2022). Boosting word frequencies in authorship attribution. In: "CHR 2022 Computational Humanities Research 2022", pp. 387-397. https://ceur-ws.org/Vol-3290/long_paper5362.pdf

Evert, S., Proisl, T., Jannidis, F., Reger, I., Pielstrom, S., Schoch, C. and Vitt, T. (2017). Understanding and explaining Delta measures for authorship attribution. Digital Scholarship in the Humanities, 32(suppl. 2): 4-16.

See Also

stylo, classify, dist.cosine, as.dist

Examples

# first, preparing a table of word frequencies
        Iuvenalis_1 = c(3.939, 0.635, 1.143, 0.762, 0.423)
        Iuvenalis_2 = c(3.733, 0.822, 1.066, 0.933, 0.511)
        Tibullus_1  = c(2.835, 1.302, 0.804, 0.862, 0.881)
        Tibullus_2  = c(2.911, 0.436, 0.400, 0.946, 0.618)
        Tibullus_3  = c(1.893, 1.082, 0.991, 0.879, 1.487)
        dataset = rbind(Iuvenalis_1, Iuvenalis_2, Tibullus_1, Tibullus_2, 
                        Tibullus_3)
        colnames(dataset) = c("et", "non", "in", "est", "nec")

# the table of frequencies looks as follows
        print(dataset)
        
# then, applying a distance
        dist.delta(dataset)
        dist.argamon(dataset)
        dist.eder(dataset)

# converting to a regular matrix
        as.matrix(dist.delta(dataset))