perform.knn: k-Nearest Neighbor classifier
In stylo: Stylometric Multivariate Analyses

Description Usage Arguments Value Author(s) See Also Examples

A machine-learning supervised classifier; this function is a wrapper for the k-NN procedure provided by the package class.

1 2	perform.knn(training.set, test.set, classes.training.set = NULL, classes.test.set = NULL, k.value = 1)

`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).
`k.value`	number of nearest neighbors considered.

The function returns a vector of "guessed" classes: each test sample is linked with one of the classes represented in the training set.

Maciej Eder

perform.svm, perform.nsc, perform.delta, perform.naivebayes

## Not run: 
perform.knn(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.knn(train, test, train.classes, test.classes)