MarihuanaAlcohol {cmm}

R Documentation

Marihuana and alcohol use during adolescence, five-wave panel

Description

Panel study with five time points. A group of 269 youths were interviewed at ages 13, 14, 15, 16, and 17, and asked (among other things) about their marijuana and alcohol use (Eliot, Huizinga & Menard, 1989). The data are tabulated in Bergsma, Croon, and Hagenaars (2009, p. 128). 208 observations do not have missing values.

Sections 4.2 and 4.4 in Bergsma, Croon, and Hagenaars (2009).

Usage

data(MarihuanaAlcohol)

Format

A data frame with 269 observations on the following variables.

Gender

(factor): 1 = Male; 2 = Female.

M1

Use of marihuana at time 1 (ordered): 1 = Never; 2 = Occasionally; 3 = Frequently.

M2

Use of marihuana at time 2 (ordered): see M1

.

M3

Use of marihuana at time 3 (ordered): see M1

.

M4

Use of marihuana at time 4 (ordered): see M1

.

M5

Use of marihuana at time 5 (ordered): see M1

.

A1

Use of alcohol at time 1 (ordered): see M1

.

A2

Use of alcohol at time 2 (ordered): see M1

.

A3

Use of alcohol at time 3 (ordered): see M1

.

A4

Use of alcohol at time 4 (ordered): see M1

.

A5

Use of alcohol at time 5 (ordered): see M1

.

Source

US National Youth Survey (NYS): teenage marijuana and alcohol use (Elliot, Huizinga and Menard, 1989)

References

Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudinal categorical data. New York: Springer.

Elliot, D. S., Huizinga, D. & Menard, S. (1989). Multiple problem youth: Delinquency, substance use, and metal health problems. New York: Springer.

Examples

data(MarihuanaAlcohol)

# Table MA: marginal loglinear analysis  (BCH Section 4.2.1)
# listwise deletion of missing values and deletion of Gender and Alcohol use
dat <- MarihuanaAlcohol[-row(MarihuanaAlcohol)[is.na(MarihuanaAlcohol)],2:6]

# at yields the vectorized 5x3 table MA (marijuana use x age)
at <- MarginalMatrix(var =  c("M1", "M2", "M3", "M4", "M5"), 
 marg = list(c("M1"), c("M2"), c("M3"), c("M4"), c("M5")), 
 dim = c(3, 3, 3, 3, 3))
obscoeff <- SampleStatistics(dat = dat, 
 coeff = list("log", at), 
 CoefficientDimensions = c(5,3), 
 Labels = c("Age", "M"), 
 ShowCoefficients = FALSE, 
 ShowParameters = TRUE)

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