Calculates a vector of observed keyness scores for a given pair of corpora.
keyness_scores(ifl, type = "llr", laplace = 1)a numerical vector of the scores, one for each term. Terms are stored in the names attribute.
Indexed frequency list as generated by create_ifl().
The type of keyness measure. One of llr, chisq, diff, logratio or ratio. See details.
Parameter of laplace correction. Only relevant for type = "ratio" and type = "logratio". See details.
Keyness scores are calculated for an Indexed frequency list from a given pair of corpora
as generated by create_ifl().
Currently, the following types of scores are supported:
llrThe log-likelihood ratio
chisqThe Chi-Square-Statistic
diffDifference of relative frequencies
logratioBinary logarithm of the ratio of the relative frequencies, possibly using a laplace correction to avoid infinite values.
ratioratio of the relative frequencies, possibly using a laplace correction to avoid infinite values.
llr and chisq are the test-statistics for a two-by-two contingency table.
| corpus A | corpus B | TOTAL | |
| term of interest | \(o_{11}\) | \(o_{12}\) | \(r_{1}\) |
| other tokens | \(o_{21}\) | \(o_{22}\) | \(r_{2}\) |
| TOTAL | \(c_{1}\) | \(c_{2}\) | N |
Both measure deviations from equal proportions but do not indicate the direction.
For llr, the correct version using terms for all four fields of the table is used,
not the version using only two terms that is sometimes used in corpus linguistics:
$$llr = -2 * (o11 * log(o11/e11) + o12 * log(o12/e12) +
o21 * log(o21/e21) + o22 * log(o22/e22))$$
where \(oij * log(oij/eij) = 0\) if \(oij = 0\).
chisq is the usual Chi-Square statistic for a test of independece / homogeneity:
$$chisq = (o11 - e11)^2/e11 + (o12 - e12)^2/e12 +
(o21 - e21)^2/e21 + (o22 - e22)^2/e22$$
Here, \(oij\) are the observed counts as given above and \(eij\) are the correpsonding expected values under an independence / homogeneity assumption.
diff and logratio are measures of the effect size,
but using the permutation approach implemented here a p-value can
be calculated as well. Both indicate the direction of the effect,
and can be used for one- or two-sided tests.
$$diff = o11 / c1 - o12 / c2$$
logratio is based on a ratio of ratios and would be infinite when a term does not occur in either of the two corpora, irrespective of number of occurences in the other corpus. Hence, we use a laplace correction adding a (not neccesarily integer) number \(k\) of ficticious occurences to both corpora:
$$logratio = log2( ((o11 + k) / (c1 + k)) / ((o12 + k) / (c2 + k)) ) $$
where \(o11\) and \(o12\) are the number of occurences of the term of interest in Corpora A and B
and \(c1\) and \(c2\) are the total numbers of tokens in A and B.
Setting \(k\) to zero corresponds to the usual logratio (which may be
infinite). \(k\) is given by the laplace argument and
defaults to one, meaning one ficticious occurence is added to
either corpus. Doing so prevents infinite values but has little
effect when the number of occurences is large.
ratio is the same as logratio but omits the logarithm:
$$ratio = ((o11 + k) / (c1 + k)) / ((o12 + k) / (c2 + k)) $$
This leads to the same p-values but is faster to compute.