# EVS: European Values Study (EVS): attitude towards women's role in... In cmm: Categorical Marginal Models

## Description

European Values Study 1999/2000, Views on Women's Roles http://www.europeanvaluesstudy.eu/

The data are tabulated in Bergsma, Croon, and Hagenaars (2009, Table 5.1a). Section 5.1.2 in Bergsma, Croon and Hagenaars (2009).

## Usage

 `1` ```data(EVS) ```

## Format

A data frame with 960 observations on the following variables.

`S`

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

`A`

Date of Birth (ordered): 1 = Before 1945; 2 = 1945-1963; 3 = After 1963.

`E`

Level of education (ordered): 1 = Lower; 2 = Intermediate; 3 = Higher.

`R`

Religion (ordered): 1 = Religious person; 2 = Not a religious person; 3 = Convinced atheist.

`W`

Attitude women's role in society (factor): 1 = Traditional; 2 = Nontraditional.

## Source

European Values Study 1999/2000

## References

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43``` ```data(EVS) # Table SAERW var = c("S", "A", "E", "R", "W"); dim = c(2, 3, 3, 3, 2); # matrices for table SA at1 <- MarginalMatrix(var, c("S", "A"), dim); bt1 <- ConstraintMatrix(c("S", "A"), list(c("S"), c("A")), c(2, 3)); # matrices for table SAER at2 <- MarginalMatrix(var, c("S", "A", "E", "R"), dim); bt2 <- ConstraintMatrix(var = c("S", "A", "E", "R"), suffconfigs = list(c("S", "A", "E"), c("S", "R"), c("A", "R")), dim = c(2, 3, 3, 3)); # matrices for table SAERW: constraints at3 <- MarginalMatrix(var, c("S", "A", "E", "R", "W"), dim); bt3 <- ConstraintMatrix(var = c("S", "A", "E", "R", "W"), suffconfigs = list(c("S", "A", "E", "R"), c("S", "W"), c("A", "W"), c("E", "W"), c("R", "W")), dim = c(2, 3, 3, 3, 2)) # matrix for table SAERW: design matrix x <- DesignMatrix(var = c("S", "A", "E", "R", "W"), suffconfigs = list(c("S", "A", "E", "R"), c("S", "W"), c("A", "W"), c("E", "W"), c("R", "W")), dim = c(2, 3, 3, 3, 2)); # model1: specification using only constraints at <- rbind(at1, at2, at3); bt <- DirectSum(bt1, bt2); bt <- DirectSum(bt, bt3); model1 <- list(bt, "log", at); # model2: same as model1 but using both constraints and a design matrix # to specify a loglinear model for the joint distribution at <- rbind(at1, at2); bt <- DirectSum(bt1, bt2); model2 <- list(list(bt, "log", at), x); nkps3 <- MarginalModelFit(EVS, model2, MaxError = 10.^-25, MaxSteps = 1000, ShowProgress = 10, MaxStepSize = .3 ); ```

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