perform.delta: Distance-based classifier
In stylo: Stylometric Multivariate Analyses

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

Delta: a simple yet effective machine-learning method of supervised classification, introduced by Burrows (2002). It computes a table of distances between samples, and compares each sample from the test set against training samples, in order to find its nearest neighbor. Apart from classic Delta, a number of alternative distance measures are supported by this function.

perform.delta(training.set, test.set, 
              classes.training.set = NULL, 
              classes.test.set = NULL, 
              distance = "delta", no.of.candidates = 3, 
              z.scores.both.sets = TRUE)

`training.set`	a table containing frequencies/counts for several variables – e.g. most frequent words – across a number of text samples (for the training set). Make sure that the rows contain samples, and the columns – variables (words, n-grams, or whatever needs to be analyzed).
`test.set`	a table containing frequencies/counts for the training set. The variables used (i.e. columns) must match the columns of the training set.
`classes.training.set`	a vector containing class identifiers for the training set. When missing, the row names of the training set table will be used; the assumed classes are the strings of characters followed by the first underscore. Consider the following examples: c("Sterne_Tristram", "Sterne_Sentimental", "Fielding_Tom", ...), where the classes are the authors' names, and c("M_Joyce_Dubliners", "F_Woolf_Night_and_day", "M_Conrad_Lord_Jim", ...), where the classes are M(ale) and F(emale) according to authors' gender. Note that only the part up to the first underscore in the sample's name will be included in the class label.
`classes.test.set`	a vector containing class identifiers for the test set. When missing, the row names of the test set table will be used (see above).
`distance`	a kernel (i.e. a distance measure) used for computing similarities between texts. Available options so far: "delta" (Burrow's Delta, default), "argamon" (Argamon's Linear Delta), "eder" (Eder's Delta), "simple" (Eder's Simple Distance), "canberra" (Canberra Distance), "manhattan" (Manhattan Distance), "euclidean" (Euclidean Distance), "cosine" (Cosine Distance).
`no.of.candidates`	how many nearest neighbors will be computed for each test sample (default = 3).
`z.scores.both.sets`	many distance measures convert input variables into z-scores before computing any distances. Such a variable weighting is highly dependent on the number of input texts. One might choose either training set only to scale the variables, or the entire corpus (both sets). The latter is default.

The function returns a vector of "guessed" classes: each test sample is linked with one of the classes represented in the training set. Additionally, final scores and final rankings of candidates are returned as attributes.

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.

Jockers, M. L. and Witten, D. M. (2010). A comparative study of machine learning methods for authorship attribution. "Literary and Linguistic Computing", 25(2): 215-23.

perform.svm, perform.nsc, perform.knn, perform.naivebayes, dist.delta

## Not run: 
perform.delta(training.set, test.set)

## End(Not run)

# classifying the standard 'iris' dataset:
data(iris)
x = subset(iris, select = -Species)
train = rbind(x[1:25,], x[51:75,], x[101:125,])
test = rbind(x[26:50,], x[76:100,], x[126:150,])
train.classes = c(rep("s",25), rep("c",25), rep("v",25))
test.classes = c(rep("s",25), rep("c",25), rep("v",25))

perform.delta(train, test, train.classes, test.classes)