knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
This package implements an adpatation of the Higher-Criticism (HC) test to discriminate two frequency tables footnotes^[See Kipnis A. Higher Criticism for Discriminating Word-Frequency Tables and Testing Authorship (2019)].
The package includes two main functions: - two.sample.pvals -- produces a list of P-values, one for each feature in the two tables. - HC.vals -- computes the HC score of the P-values.
A third function two.sample.HC combines the two functions above so that the HC score of the two tables is obtained using a single function call.
#' # Can be used to check similarity of word-frequencies in texts: #' text1 = "On the day House Democrats opened an impeachment inquiry of #' President Trump last week, Pete Buttigieg was being grilled by Iowa #' voters on other subjects: how to loosen the grip of the rich on #' government, how to restore science to policymaking, how to reduce child #' poverty. At an event in eastern Iowa, a woman rose to say that her four #' adult children were “stuck” in life, unable to afford what she had in #' the 1980s when a $10-an-hour job paid for rent, utilities and an #' annual vacation." #' text2 = "How can the federal government help our young people that want to do #' what’s right and to get to those things that their parents worked so hard for?” #' the voter asked. This is the conversation Mr. Buttigieg wants to have. #' Boasting a huge financial war chest but struggling in the polls, Mr. Buttigieg #' is now staking his presidential candidacy on Iowa, and particularly on #' connecting with rural white voters who want to talk about personal concerns #' more than impeachment. In doing so, Mr. Buttigieg is also trying to show how #' Democrats can win back counties that flipped from Barack Obama to Donald #' Trump in 2016 — there are more of them in Iowa than any other state — #' by focusing, he said, on “the things that are going to affect folks’ #' lives in a concrete way." tb1 = table(strsplit(tolower(text1),' ')) tb2 = table(strsplit(tolower(text2),' ')) pv = two.sample.pvals(tb1,tb2) print(pv$pv) >  1.0000 1.0000 0.2304 1.0000 1.0000 1.0000 NA 0.1936 NA print(pv$Var1) > go i or say should stay you and not HC.vals(pv$pv) > $HC > 0.323954762194625 > $HC.star > 0.323954762194625 > $p > 0.2304 > $p.star > 0.2304
n = 1000 #number of features N = 10*n #number of observations k = 0.1*n #number of perturbed features seq = seq(1,n) P = 1 / seq #sample from a Zipf law distribution P = P / sum(P) tb1 = data.frame(Feature = seq(1,n), # sample 1 Freq = rmultinom(n = 1, prob = P, size = N)) seq[sample(seq,k)] <- seq[sample(seq,k)] Q = 1 / seq Q = Q / sum(Q) tb2 = data.frame(Feature = seq(1,n), # sample 2 Freq = rmultinom(n = 1, prob = Q, size = N)) PV = two.sample.pvals(tb1, tb2) #compute P-values HC.vals(PV$pv) # HC test # can also test using a single function call two.sample.HC(tb1,tb2)
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