MarihuanaAlcohol: Marihuana and alcohol use during adolescence, five-wave panel

Description Usage Format Source References Examples

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

1

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

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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)

cmm documentation built on May 2, 2019, 3:36 a.m.