R: Dayton Student Survey on Substance Use

DaytonSurvey {vcdExtra} | R Documentation

## 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)

    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)

[Package vcdExtra version 0.7-1 Index]