### Chi-squared test, Fisher's exact test, effect size ### PART 1 ### # load data and get summary info soc <- read.csv("http://math-info.hse.ru/f/2018-19/pep/socling.csv") summary(soc) # example of ifelse() v <- c('a', 'b', 'b', 'a') ifelse(v == 'a', 1, 0) # 1 if a, 0 is not a # add a column for Moscow vs Non-Moscow library(tidyverse) soc <- soc %>% mutate(moscow = ifelse(region == "Moscow", "Moscow", "Not Moscow")) # create a contingency table tab <- table(soc\$phrase, soc\$moscow) tab # create a contingency table with proportions prop.table(tab) prop.table(tab) * 100 # in % # perform a chi-squared test # variables of interest are in brackets, # not a contingency table itself chisq.test(soc\$phrase, soc\$moscow) # perform a Fisher's exact test fisher.test(soc\$phrase, soc\$moscow) # save results of a chi-squared test and investigate them res <- chisq.test(soc\$phrase, soc\$moscow) res\$expected # expected frequencies res\$observed # observed frequencies # check why chi-square in the output is 12.518 # by hand - for the sake of clarity (18 - 17)^2 / 17 + (16 - 17)^2 / 17 + (1 - 5)^2 / 5 + (9 - 5)^2 / 5 + (6 - 3)^2 / 3 + (0 - 3)^2 / 3 # do the same, but # using R tables - more convenient sum((res\$expected - res\$observed) ** 2 / res\$expected) # look at densities of a chi-squared # distributions with different df (degrees of freedom) curve(dchisq(x, df = 1), xlim = c(0, 20)) curve(dchisq(x, df = 2), xlim = c(0, 20), col = "blue", add = TRUE) curve(dchisq(x, df = 4), xlim = c(0, 20), col = "red", add = TRUE) curve(dchisq(x, df = 12), xlim = c(0, 20), col = "green", add = TRUE) ### PART 2 ### # visualise a contingency table install.packages("vcd") library(vcd) mosaic(soc\$phrase.eng ~ soc\$moscow) mosaic(data = soc, phrase.eng ~ moscow, set_varnames = list(phrase.eng = "Phrase type", moscow = "Region of living")) ### PART 3 ### # work on your own with phonological data phono <- read.csv("https://raw.githubusercontent.com/LingData2019/LingData/master/data/elision.csv") tab2 <- table(phono\$v.elision, phono\$group) tab2 # chi-squared test chisq.test(phono\$v.elision, phono\$group) # calculate the effect size via Cramer's V install.packages("lsr") library(lsr) cramersV(tab2)