Dayton Student Survey on Substance Use
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.
data(DaytonSurvey)
A frequency data frame with 32 observations on the following 6 variables.
cigarettea factor with levels Yes No
alcohola factor with levels Yes No
marijuanaa factor with levels Yes No
sexa factor with levels female male
racea factor with levels white other
Freqa numeric vector
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.
Agresti, A. (2002). Categorical Data Analysis, 2nd Ed., New York: Wiley-Interscience, Table 9.1, p. 362.
Thompson, L. (2009). R (and S-PLUS) Manual to Accompany Agresti's Categorical Data, http://www.stat.ufl.edu/~aa/cda/Thompson_manual.pdf
data(DaytonSurvey) mod.GR <- glm(Freq ~ . + sex*race, data=DaytonSurvey, family=poisson) # mutual independence + GR mod.homog.assoc <- glm(Freq ~ .^2, data=DaytonSurvey, family=poisson) # homogeneous association # collapse over sex and race Dayton.ACM <- aggregate(Freq ~ cigarette+alcohol+marijuana, data=DaytonSurvey, FUN=sum)
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