I have a conundrum here.
I'm attempting to recreate the log-likelihood test example in 'The Cambridge Handbook of Corpus Linguistics (2015). However when I run the same test on the same data I get a different result in R.
Chapter 6 on collocation is by Richard Xiao and on page 111 (the end of section 2.2, table 6.1). He does a log-likelihood measure (LL) on data from the BNC. Specifically he uses a LL on the collocation "sweet smell" and on page 109 gives the following figures:
"In the BNC, for example, the frequency counts of sweet and smell are 3,460 and 3,508 respectively in N (98,313,429) tokens, and the two words co-occur 90 times within the 8-word span"
Now back on page 111 he gives the following contingency table:
So if I understand correctly:
a = number of co-occurrences (90)
b = instances of the word 'sweet' (3460)
c = instances of the word 'smell' (3508)
d = total words in the corpus (983131429) - instances of 'sweet' and 'smell' so in total: (98306461)
So then I run the following formula he gives on that same page in R:
LL <- 2*(a*log(a)+b*log(b)+c*log(c)+d*log(d) -(a+b)*log(a+b)-(a+c)*log(a+c)-(b+d)*log(b+d) -(c+d)*log(c+d)+(a+b+c+d)*log(a+b+c+d))
The LL score I get is ~620, Xiao reports a score of 688. So I wasn't too far off, but why was I off? I checked the syntax of the formula several times, even copied and pasted it out of the e-version of the book and I get the same result. I even tested this against the
LL.collostrfunction given in Levshina's 2015 book, which gives me an almost identical value of ~620.
So my only guess is that I'm not interpreting the contingency table correctly. I fiddled around with it a little bit, setting d to the total number of words in the corpus, and a to the combination of all instances of 'sweet' and 'smell' but I get numbers even farther off by doing that, greater than 1000.
So what's going on here? How am I supposed to interpret that contingency table? Xiao doesn't give a lot of details on that table.
# III. Log-likelihood #a = co-occurrence tokens (sweet smell) a <- 90 #b = word A tokens (sweet) b <- 3460 #word B tokens (smell) c <- 3508 #total words in corpus minus A and B d <- 98313429 - (b+c) LL1 <- 2*(a*log(a)+b*log(b)+c*log(c)+d*log(d) -(a+b)*log(a+b)-(a+c)*log(a+c)-(b+d)*log(b+d) -(c+d)*log(c+d)+(a+b+c+d)*log(a+b+c+d)) library(Rling) LL2 <- LL.collostr(a, b, c, d)
Biber, D., & Reppen, R. (Eds.). (2015). The Cambridge Handbook of English Corpus Linguistics (Cambridge Handbooks in Language and Linguistics). Cambridge: Cambridge University Press. doi:10.1017/CBO9781139764377
Levshina, Natalia. 2015. How to do Linguistics with R: Data exploration and statistical analysis. Amsterdam/Philadelphia: John Benjamins Publishing Co. doi:10.1075/z.195.