`textstat_lexdiv.Rd`

Calculate the lexical diversity or complexity of text(s).

textstat_lexdiv(x, measure = c("all", "TTR", "C", "R", "CTTR", "U", "S", "Maas"), log.base = 10, ...)

x | an input object, such as a document-feature matrix object |
---|---|

measure | a character vector defining the measure to calculate. |

log.base | a numeric value defining the base of the logarithm (for measures using logs) |

... | not used |

`textstat_lexdiv`

returns a data.frame of documents and
their lexical diversity scores.

`textstat_lexdiv`

calculates a variety of proposed indices for lexical
diversity. In the following formulae, \(N\) refers to the total number of
tokens, and \(V\) to the number of types:

`"TTR"`

:The ordinary

*Type-Token Ratio*: $$TTR = \frac{V}{N}$$`"C"`

:Herdan's

*C*(Herdan, 1960, as cited in Tweedie & Baayen, 1998; sometimes referred to as*LogTTR*): $$C = \frac{\log{V}}{\log{N}}$$`"R"`

:Guiraud's

*Root TTR*(Guiraud, 1954, as cited in Tweedie & Baayen, 1998): $$R = \frac{V}{\sqrt{N}}$$`"CTTR"`

:Carroll's

*Corrected TTR*: $$CTTR = \frac{V}{\sqrt{2N}}$$`"U"`

:Dugast's

*Uber Index*(Dugast, 1978, as cited in Tweedie & Baayen, 1998): $$U = \frac{(\log{N})^2}{\log{N} - \log{V}}$$`"S"`

:Summer's index: $$S = \frac{\log{\log{V}}}{\log{\log{N}}}$$

`"K"`

:Yule's

*K*(Yule, 1944, as cited in Tweedie & Baayen, 1998) is calculated by: $$K = 10^4 \times \frac{(\sum_{X=1}^{X}{{f_X}X^2}) - N}{N^2}$$ where \(N\) is the number of tokens, \(X\) is a vector with the frequencies of each type, and \(f_X\) is the frequencies for each X.`"Maas"`

:Maas' indices (\(a\), \(\log{V_0}\) & \(\log{}_{e}{V_0}\)): $$a^2 = \frac{\log{N} - \log{V}}{\log{N}^2}$$ $$\log{V_0} = \frac{\log{V}}{\sqrt{1 - \frac{\log{V}}{\log{N}}^2}}$$ The measure was derived from a formula by Mueller (1969, as cited in Maas, 1972). \(\log{}_{e}{V_0}\) is equivalent to \(\log{V_0}\), only with \(e\) as the base for the logarithms. Also calculated are \(a\), \(\log{V_0}\) (both not the same as before) and \(V'\) as measures of relative vocabulary growth while the text progresses. To calculate these measures, the first half of the text and the full text will be examined (see Maas, 1972, p. 67 ff. for details). Note: for the current method (for a dfm) there is no computation on separate halves of the text.

This implements only the static measures of lexical diversity, not more complex measures based on windows of text such as the Mean Segmental Type-Token Ratio, the Moving-Average Type-Token Ratio (Covington & McFall, 2010), the MLTD or MLTD-MA (Moving-Average Measure of Textual Lexical Diversity) proposed by McCarthy & Jarvis (2010) or Jarvis (no year), or the HD-D version of vocd-D (see McCarthy & Jarvis, 2007). These are available from the package korRpus.

Covington, M.A. & McFall, J.D. (2010). Cutting the Gordian Knot:
The Moving-Average Type-Token Ratio (MATTR). *Journal of Quantitative
Linguistics*, 17(2), 94--100.

Maas, H.-D., (1972). \"Uber den Zusammenhang zwischen Wortschatzumfang und
L\"ange eines Textes. *Zeitschrift f\"ur Literaturwissenschaft und
Linguistik*, 2(8), 73--96.

McCarthy, P.M. & Jarvis, S. (2007). vocd: A theoretical and empirical
evaluation. *Language Testing*, 24(4), 459--488.

McCarthy, P.M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation
study of sophisticated approaches to lexical diversity assessment.
*Behaviour Research Methods*, 42(2), 381--392.

Michalke, Meik. (2014) *koRpus: An R Package for Text Analysis*.
Version 0.05-5. http://reaktanz.de/?c=hacking&s=koRpus

Tweedie. F.J. & Baayen, R.H. (1998). How Variable May a Constant Be?
Measures of Lexical Richness in Perspective. *Computers and the
Humanities*, 32(5), 323--352.

mydfm <- dfm(corpus_subset(data_corpus_inaugural, Year > 1980), verbose = FALSE) (result <- textstat_lexdiv(mydfm, c("CTTR", "TTR", "U")))#> Error in get(".SigLength", envir = env): object '.SigLength' not foundcor(textstat_lexdiv(mydfm, "all")[,-1])#> Error in get(".SigLength", envir = env): object '.SigLength' not found