find_min_disp: Find the minimally dispersed distribution of an item across...

In tlda: Tools for Language Data Analysis

Find the minimally dispersed distribution of an item across corpus parts

Description

This function returns the (hypothetical) distribution of subfrequencies that represents the smallest possible level of dispersion for a given item across a particular set of corpus parts. It requires a vector of subfrequencies and a vector of corpus part sizes. This distribution is required for the min-max transformation proposed by Gries (2022: 184-191; 2024: 196-208) to obtain frequency-adjusted dispersion scores.

Usage

find_min_disp(subfreq, partsize, freq_adjust_method = "even")

Arguments

subfreq	A numeric vector of subfrequencies, i.e. the number of occurrences of the item in each corpus part
partsize	A numeric vector specifying the size of the corpus parts
freq_adjust_method	Character string indicating which method to use for devising dispersion extremes. See details below. Possible values are "even" (default) and "pervasive"

Details

This function creates a hypothetical distribution of the total number of occurrences of the item (i.e. the sum of its subfrequencies) across corpus parts. To obtain the lowest possible level of dispersion, the argument freq_adjust_method allows the user to choose between two distributional features: pervasiveness (pervasive) or evenness (even). For details and explanations, see vignette("frequency-adjustment"). To obtain the lowest possible level of dispersion, the occurrences are either allocated to as few corpus parts as possible (pervasiveness), or they are assigned to the smallest corpus part(s) (even). Since the dispersion of items that occur only once in the corpus (hapaxes) cannot be sensibly measured or manipulated, such items are disregarded; the function returns their observed subfrequencies. The function reuses code segments from Gries's (2025) 'KLD4C' package (from the function most.uneven.distr()).

Value

An integer vector the same length as partsize

Author(s)

Lukas Soenning

References

Gries, Stefan Th. 2022. What do (most of) our dispersion measures measure (most)? Dispersion? Journal of Second Language Studies 5(2). 171–205. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1075/jsls.21029.gri")}

Gries, Stefan Th. 2024. Frequency, dispersion, association, and keyness: Revising and tupleizing corpus-linguistic measures. Amsterdam: Benjamins. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1075/scl.115")}

Gries, Stefan Th. 2025. KLD4C: Gries 2024: Tupleization of corpus linguistics. R package version 1.01. (available from https://www.stgries.info/research/kld4c/kld4c.html)

Examples

find_min_disp(
  subfreq = c(0,0,1,2,5), 
  partsize = rep(1000, 5),
  freq_adjust_method = "even")

tlda documentation built on June 8, 2025, 11:41 a.m.