```
# Case-control study assessing whether exposure to tap water
# is associated with cryptosporidiosis among AIDS patients
tab <- matrix(c(2, 29, 35, 64, 12, 6), 3, 2, byrow=TRUE)
dimnames(tab) <- list("Tap water exposure" = c("Lowest", "Intermediate", "Highest"),
"Outcome" = c("Case", "Control"))
tab <- Rev(tab, margin=2)
OddsRatio(tab[1:2,])
OddsRatio(tab[c(1,3),])
OddsRatio(tab[1:2,], method="mle")
OddsRatio(tab[1:2,], method="midp")
OddsRatio(tab[1:2,], method="wald", conf.level=0.95)
# in case of zeros consider using glm for calculating OR
dp <- data.frame (a=c(20, 7, 0, 0), b=c(0, 0, 0, 12), t=c(1, 0, 1, 0))
fit <- glm(cbind(a, b) ~ t, data=dp, family=binomial)
exp(coef(fit))
# calculation of log oddsratios in a 2x2xk table
migraine <- xtabs(freq ~ .,
cbind(expand.grid(treatment=c("active","placebo"),
response=c("better","same"),
gender=c("female","male")),
freq=c(16,5,11,20,12,7,16,19))
)
log(apply(migraine, 3, OddsRatio))
# OddsRatio table for logistic regression models
r.glm <- glm(type ~ ., data=MASS::Pima.tr2, family=binomial)
OddsRatio(r.glm)
plot(OddsRatio(r.glm), xlim=c(0.5, 2), main="OddsRatio - glm", pch=NA,
lblcolor=DescTools::hred, args.errbars=list(col=DescTools::horange, pch=21,
col.pch=DescTools::hblue,
bg.pch=DescTools::hyellow, cex.pch=1.5))
```

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