Smoking cessation after experimental intervention
Data from an experiment designed for the investigation of the effectiveness of a particular expert system intervention to convince people to quit smoking. N = 4,144 subjects were randomly assigned to either the control (assessment only) or the experimental condition (TTM intervention). Information was collected on their smoking habits and their attitudes towards smoking at the start of the study, at the sixth, twelfth, eighteenth, and twenty-fourth month. For more detailed information see Bergsma et al. (2009) and Prochaska et al. (2001).
The data are tabulated in Bergsma, Croon, and Hagenaars (2009, Tables 5.11 to 5.14).
Section 5.2.3 in Bergsma, Croon and Hagenaars (2009).
data(Smoking)
A data frame with 4144 observations on the following variables.
Group(factor): 1 = TTM intervention; 2 = Assessment only.
smst00Behavior at beginning (ordered): 1 = Precontemplation; 2 = Contemplation; 3 = Preparation; 4 = Action; 5 = Maintenance.
smst06Behavior after 6 months (ordered): see smst00
smst12Behavior after 12 months (ordered): see smst00
smst18Behavior after 18 months (ordered): see smst00
smst24Behavior after 24 months (ordered): see smst00
Cancer Prevention Research Center, Univisity of Rhode Island, US. See Prochaska, Velicer, Fave, Rossi & Tosh (2001).
Examples in book: http://stats.lse.ac.uk/bergsma/cmm/R%20files/Smoking.R
Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudinal categorical data. New York: Springer.
Prochaska, J. O., Velicer, W. F., Fava, J. L. Rossi, J. S., & Tosh, J. Y. (2001). Evaluating a population-based recruitment approach and a stage-based expert system intervention for smoking cessation. Addictive Behaviors, 26, 583-602.
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# Read data
data(Smoking)
## Not run:
dat <- Smoking
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# Table TXBR
# matrix producing 4x2x3x6 table TXBR
atTXBR <- MarginalMatrix(var = c("X", "B", "R1", "R2", "R3", "R4"),
marg = list(c("X", "B", "R1"), c("X", "B", "R2"), c("X", "B", "R3"), c("X", "B", "R4")),
dim = c(2, 3, 5, 5, 5, 5))
bt <- ConstraintMatrix(var = c("T", "X", "B", "R"), suffconfigs = list(c("T", "X", "B"), c("R")),
dim = c(4, 2, 3, 5))
model = list(bt, "log", atTXBR)
fit = MarginalModelFit(dat = dat, model = model, MaxStepSize = .3, MaxSteps = 100,
ShowProgress = 5)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.