dist.simple: Cosine Distance
In stylo: Stylometric Multivariate Analyses

Description Usage Arguments Value Author(s) See Also Examples

Function for computing Eder's Simple distance of a matrix of values, e.g. a table of word frequencies. This is done by normalizing the input dataset by a square root function, and then applying Manhattan distance.

1	dist.simple(x)

`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.

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

stylo, classify, dist.delta, as.dist

# 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, in two flavors
        dist.simple(dataset)
        as.matrix(dist.simple(dataset))