Description Usage Arguments Value Author(s) References See Also Examples

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`

.

1 2 3 4 5 | ```
dist.delta(x, scale = TRUE)
dist.argamon(x, scale = TRUE)
dist.eder(x, scale = TRUE)
``` |

`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. |

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`

.

Maciej Eder

Argamon, S. (2008). Interpreting Burrows's Delta: geometric and probabilistic foundations. "Literary and Linguistic Computing", 23(2): 131-47.

Burrows, J. F. (2002). "Delta": a measure of stylistic difference and a guide to likely authorship. "Literary and Linguistic Computing", 17(3): 267-87.

Eder, M. (2015). Taking stylometry to the limits: benchmark study on 5,281 texts from Patrologia Latina. In: "Digital Humanities 2015: Conference Abstracts" http://dh2015.org/abstracts.

Jannidis, F., Pielstrom, S., Schoch, Ch. and Vitt, Th. (2015). Improving Burrows' Delta: An empirical evaluation of text distance measures. In: "Digital Humanities 2015: Conference Abstracts" http://dh2015.org/abstracts.

`stylo`

, `classify`

, `dist.cosine`

,
`as.dist`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# 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))
``` |

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