Accident: Traffic Accident Victims in France in 1958

In vcdExtra: 'vcd' Extensions and Additions

Traffic Accident Victims in France in 1958

Description

Bertin (1983) used these data to illustrate the cross-classification of data by numerous variables, each of which could have various types and could be assigned to various visual attributes.

Format

A data frame in frequency form (comprising a 5 x 2 x 4 x 2 table) with 80 observations on the following 5 variables.

age

an ordered factor with levels 0-9 < 10-19 <20-29 < 30-49 < ⁠50+⁠

result

a factor with levels Died Injured

mode

mode of transportation, a factor with levels 4-Wheeled Bicycle Motorcycle Pedestrian

gender

a factor with levels Female Male

Freq

a numeric vector

Details

For modeling and visualization purposes, the data can be treated as a 4-way table using loglinear models and mosaic displays, or as a frequency-weighted data frame using a binomial response for result ("Died" vs. "Injured") and plots of predicted probabilities.

age is an ordered factor, but arguably, mode should be treated as ordered, with levels Pedestrian < Bicycle < Motorcycle < 4-Wheeled as Bertin does. This affects the parameterization in models, so we don't do this directly in the data frame.

Source

Bertin (1983), p. 30; original data from the Ministere des Travaux Publics

References

Bertin, J. (1983), Semiology of Graphics, University of Wisconsin Press.

Examples

# examples
data(Accident)
head(Accident)

# for graphs, reorder mode
Accident$mode <- ordered(Accident$mode,
   levels=levels(Accident$mode)[c(4,2,3,1)])

# Bertin's table
accident_tab <- xtabs(Freq ~ gender + mode + age + result, data=Accident)
structable(mode + gender ~ age + result, data=accident_tab)

## Loglinear models
## ----------------

# mutual independence
acc.mod0 <- glm(Freq ~ age + result + mode + gender,
                data=Accident,
                family=poisson)
LRstats(acc.mod0)

mosaic(acc.mod0, ~mode + age + gender + result)

# result as a response
acc.mod1 <- glm(Freq ~ age*mode*gender + result,
                data=Accident,
                family=poisson)
LRstats(acc.mod1)

mosaic(acc.mod1, ~mode + age + gender + result,
    labeling_args = list(abbreviate = c(gender=1, result=4)))

# allow two-way association of result with each explanatory variable
acc.mod2 <- glm(Freq ~ age*mode*gender + result*(age+mode+gender),
                data=Accident,
                family=poisson)
LRstats(acc.mod2)
mosaic(acc.mod2, ~mode + age + gender + result,
    labeling_args = list(abbreviate = c(gender=1, result=4)))

acc.mods <- glmlist(acc.mod0, acc.mod1, acc.mod2)
LRstats(acc.mods)

## Binomial (logistic regression) models for result
## ------------------------------------------------
library(car)  # for Anova()
acc.bin1 <- glm(result=='Died' ~ age + mode + gender,
    weights=Freq, data=Accident, family=binomial)
Anova(acc.bin1)

acc.bin2 <- glm(result=='Died' ~ (age + mode + gender)^2,
    weights=Freq, data=Accident, family=binomial)
Anova(acc.bin2)

acc.bin3 <- glm(result=='Died' ~ (age + mode + gender)^3,
    weights=Freq, data=Accident, family=binomial)
Anova(acc.bin3)

# compare models
anova(acc.bin1, acc.bin2, acc.bin3, test="Chisq")

# visualize probability of death with effect plots
## Not run: 
library(effects)
plot(allEffects(acc.bin1), ylab='Pr (Died)')

plot(allEffects(acc.bin2), ylab='Pr (Died)')

## End(Not run)


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vcdExtra documentation built on Dec. 11, 2025, 9:06 a.m.