Dayton Student Survey on Substance Use

Description

This data, from Agresti (2002), Table 9.1, gives the result of a 1992 survey in Dayton Ohio of 2276 high school seniors on whether they had ever used alcohol, cigarettes and marijuana.

Usage

data(DaytonSurvey)

Format

A frequency data frame with 32 observations on the following 6 variables.

cigarette

a factor with levels Yes No

alcohol

a factor with levels Yes No

marijuana

a factor with levels Yes No

sex

a factor with levels female male

race

a factor with levels white other

Freq

a numeric vector

Details

Agresti uses the letters G (sex), R (race), A (alcohol), C (cigarette), M (marijuana) to refer to the table variables, and this usage is followed in the examples below.

Background variables include sex and race of the respondent (GR), typically treated as explanatory, so that any model for the full table should include the term sex:race. Models for the reduced table, collapsed over sex and race are not entirely unreasonable, but don't permit the estimation of the effects of these variables on the responses.

The full 5-way table contains a number of cells with counts of 0 or 1, as well as many cells with large counts, and even the ACM table collapsed over GR has some small cell counts. Consequently, residuals for these models in mosaic displays are best represented as standardized (adjusted) residuals.

Source

Agresti, A. (2002). Categorical Data Analysis, 2nd Ed., New York: Wiley-Interscience, Table 9.1, p. 362.

References

Thompson, L. (2009). R (and S-PLUS) Manual to Accompany Agresti's Categorical Data, http://www.stat.ufl.edu/~aa/cda/Thompson_manual.pdf

Examples

data(DaytonSurvey)

# mutual independence
mod.0  <- glm(Freq ~ ., data=DaytonSurvey, family=poisson)   

# mutual independence + GR
mod.GR <- glm(Freq ~ . + sex*race, data=DaytonSurvey, family=poisson)  
anova(mod.GR, test = "Chisq")

# all two-way terms
mod.all2way <- glm(Freq ~ .^2, data=DaytonSurvey, family=poisson)  
anova(mod.all2way, test = "Chisq")

# compare models
LRstats(mod.0, mod.GR, mod.all2way)

# collapse over sex and race
Dayton.ACM <- aggregate(Freq ~ cigarette+alcohol+marijuana, 
                        data=DaytonSurvey, 
                        FUN=sum)
Dayton.ACM