Survival data for 1307 passengers aboard the RMS Titanic. The outcome is whether the passenger survived.
Format
A data frame with 1307 rows and 4 variables:
- pclass
Passenger class: factor with levels
"1","2","3".- survived
Survived: factor with levels
"No","Yes". Binary outcome.- sex
Sex: factor with levels
"Female","Male".- embarked
Port of embarkation: 0 = Southampton, 1 = Cherbourg/ Queenstown.
Details
Overall survival rate is approximately 38%, making this a common outcome – a setting where OR meaningfully diverges from PR.
Examples
data(titanic)
prop.table(table(titanic$survived, titanic$sex), margin = 2)
#>
#> Female Male
#> No 0.8090154 0.2737069
#> Yes 0.1909846 0.7262931
fit <- glm(as.integer(survived == "Yes") ~ sex + pclass,
family = binomial, data = titanic)
# OR vs PR comparison
OR <- exp(coef(fit))
PR <- prLogisticDelta(fit, standardisation = "marginal")
print(PR)
#>
#> Prevalence Ratio Estimation via Logistic Regression
#> ----------------------------------------------------
#> Model : glm
#> Method : delta
#> Standardis. : marginal
#> Conf. level : 95%
#> ----------------------------------------------------
#>
#> Estimate 2.5% 97.5%
#> sexMale 3.5635 3.0375 4.1805
#> pclass1 1.7992 1.5325 2.1123
#>