GSS93 {cmm} | R Documentation |
Political Orientation and Religion in the United States in 1993 (General Social Survey, 1993)
Description
Self-reported Political Orientation (P
), Religion (R
), and Opinion
of Teenage Birth-control (B
) of a sample of 911 US citizens in 1993.
The data stem from the General Social Survey. The data are tabulated
in Bergsma, Croon, and Hagenaars (2009, Table 2.1, Table 2.3).
See Section~2.1 in Bergsma, Croon, and Hagenaars (2009).
Several worked examples involving this data set are listed below but more can be found at http://stats.lse.ac.uk/bergsma/cmm/R files/GSS93.R
Usage
data(GSS93)
Format
A data frame with 911 observations on the following three variables.
P
Political orientation (ordered): 1 = Extremely liberal; 2 = Liberal; 3 = Slightly liberal; 4 = Moderate; 5 = Slightly conservative; 6 = Conservative; 6 = Extremely conservative.
R
Religion (factor): 1 = Protestant; 2 = Catholic; 3 = Other.
B
Opinion of birth control (ordered): 1 = Strongly agree; 2 = Agree; 3 = Disagree; 4 = Strongly disagree;
Source
General Social Survey (1993)
References
Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudinal categorical data. New York: Springer
General Social Survey (1993).
Examples
data(GSS93)
## Table 2.1 of Bergsma, Croon, & Hagenaars (2009)
addmargins(table(GSS93[,1:2]))
## Table 2.2 of Bergsma, Croon, & Hagenaars (2009)
# Specify coefficients
coeff <- list("log",diag(21))
SampleStatistics(dat = GSS93[, 1 : 2],
coeff = coeff,
CoefficientDimensions = c(7, 3),
Labels = c("P","R"),
ShowParameters = TRUE,
ShowCoefficients = FALSE)
## Table 2.3 of Bergsma, Croon, & Hagenaars (2009)
ftable(B + R ~ P , data = GSS93)
########################################################
## Models for table PR
#independence of P and R
bt1 <- ConstraintMatrix(c("P", "R"), list(c("P"), c("R")), c(7,3));
#linear by nominal model
bt2 <- ConstraintMatrix(var = c("P", "R"),
suffconfigs = list(c("P", "R")),
dim = c(7, 3),
SubsetCoding = list(c("P", "R"), c("Linear", "Nominal")))
#linear by nominal model with equality of first two nominal parameters
bt3 <- ConstraintMatrix(var = c("P", "R"),
suffconfigs = list(c("P", "R")),
dim = c(7, 3),
SubsetCoding = list(c("P", "R"), list("Linear", rbind(c(1, 1, 0), c(0, 0, 1)))))
m <- MarginalModelFit(dat = GSS93[,1:2],
model = list(bt2,"log"),
ShowCoefficients = FALSE,
ShowProgress = 1,
ShowParameters = TRUE,
CoefficientDimensions = c(7, 3),
Labels = c("P", "R"),
ParameterCoding = list("Polynomial", "Effect"))
########################################################
## Models for table PRB
#various loglinear models
bt1 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("B")), c(7,3,4))
bt2 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("R","B")), c(7,3,4))
bt3 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("P","B")), c(7,3,4))
bt4 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("P","B"),c("R","B")), c(7,3,4))
bt5 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("P","B"),c("R","B")), c(7,3,4),
SubsetCoding = list(list(c("P", "B"), c("Linear", "Linear")),
list(c("R", "B"), c("Nominal", "Linear"))))
m <- MarginalModelFit(dat = GSS93,
model = list(bt2,"log"),
ShowCoefficients = FALSE,
ShowProgress = 1,
ShowParameters = TRUE,
CoefficientDimensions =c(7, 3, 4),
Labels = c("P", "R", "B"),
ParameterCoding = list("Polynomial", "Polynomial", "Effect"))