Contingency Table

table <- matrix(c(189,104,10845,10933),nrow=2)
dimnames(table) = list(Group=c("Placebo", "Aspirin"), MI=c("Yes","No"))
table <- as.table(table)
table.df = as.data.frame(table)
table.df

##     Group  MI  Freq
## 1 Placebo Yes   189
## 2 Aspirin Yes   104
## 3 Placebo  No 10845
## 4 Aspirin  No 10933

#n=2450, person/gender/party
Political <- read.table("https://users.stat.ufl.edu/~aa/cat/data/Political.dat")
names(Political) <- c("person", "gender", "party")
Political <- Political[-1,]
GenderGap<- xtabs(~gender + party, data=Political)
GenderGap

##         party
## gender   Dem Ind Rep
##   female 495 590 272
##   male   330 498 265

out <- chisq.test(GenderGap)
out

## 
##  Pearson's Chi-squared test
## 
## data:  GenderGap
## X-squared = 12.569, df = 2, p-value = 0.001865

names(out)

## [1] "statistic" "parameter" "p.value"   "method"    "data.name" "observed" 
## [7] "expected"  "residuals" "stdres"

out$stdres

##         party
## gender         Dem       Ind       Rep
##   female  3.272365 -1.032199 -2.498557
##   male   -3.272365  1.032199  2.498557

out$observed

##         party
## gender   Dem Ind Rep
##   female 495 590 272
##   male   330 498 265

out$expected

##         party
## gender       Dem      Ind      Rep
##   female 456.949 602.6188 297.4322
##   male   368.051 485.3812 239.5678

#G^2 (cRT Score)
with(out, 2*sum(observed*log(observed/expected)))

## [1] 12.6009

library(epitools)
depr <- matrix(c(495,272,330,265), ncol=2, byrow=T)
dimnames(depr) <- list(Gender=c("female", "male"), Party=c("Dem", "Rep"))
depr <- as.table(depr)
oddsratio(depr, method="wald", conf=0.95, correct=F)

## $data
##         Party
## Gender   Dem Rep Total
##   female 495 272   767
##   male   330 265   595
##   Total  825 537  1362
## 
## $measure
##         odds ratio with 95% C.I.
## Gender   estimate    lower    upper
##   female 1.000000       NA       NA
##   male   1.461397 1.173813 1.819439
## 
## $p.value
##         two-sided
## Gender     midp.exact fisher.exact   chi.square
##   female           NA           NA           NA
##   male   0.0006953425 0.0007909605 0.0006758479
## 
## $correction
## [1] FALSE
## 
## attr(,"method")
## [1] "Unconditional MLE & normal approximation (Wald) CI"

Reference

[1] Alan Agresti, 『범주형 자료분석 개론』, 박태성, 이승연 옮긴이, 자유아카데미, 2020
[2] Alan Agresti, 『Categorical Data Analysis (3rd Edition)』, Wiley, 2018