GLES {BTLLasso}

German Longitudinal Election Study (GLES)

Description

Data from the German Longitudinal Election Study (GLES), see Rattinger et al. (2014). The GLES is a long-term study of the German electoral process. It collects pre- and post-election data for several federal elections, the data used here originate from the pre-election study for 2013.

Format

A list containing data from the German Longitudinal Election Study with 2003 (partly incomplete) observations. The list contains both information on the response (paired comparisons) and different covariates.

Y

A response.BTLLasso object for the GLES data including

• response: Ordinal paired comparison response vector

• first.object: Vector containing the first-named party per paired comparison

• second.object: Vector containing the second-named party per paired comparison

• subject: Vector containing a person identifier per paired comparison

• with.order Automatically generated vector containing information on order effect. Irrelevant, because no order effect needs to be included in the analysis of GLES data.

X

Matrix containing all eight person-specific covariates

• Age: Age in years

• Gender (0: male, 1: female)

• EastWest (0: West Germany, 1: East Germany)

• PersEcon: Personal economic situation, 1: good or very good, 0: else

• Abitur: School leaving certificate, 1: Abitur/A levels, 0: else

• Unemployment: 1: currently unemployed, 0: else

• Church: Frequency of attendence in a church/synagogue/mosque/..., 1: at least once a month, 0: else

• Migration: Are you a migrant / not German since birth? 1: yes, 0: no

Z1

Matrix containing all four person-party-specific covariates

• Climate: Self-perceived distance of each person to all five parties with respect to ones attitude towards climate change.

• SocioEcon: Self-perceived distance of each person to all five parties with respect to ones attitude towards socio-economic issues.

• Immigration: Self-perceived distance of each person to all five parties with respect to ones attitude towards immigration.

Source

https://www.gesis.org/en/gles/about-gles

References

Rattinger, H., S. Rossteutscher, R. Schmitt-Beck, B. Wessels, and C. Wolf (2014): Pre-election cross section (GLES 2013). GESIS Data Archive, Cologne ZA5700 Data file Version 2.0.0.

Schauberger, Gunther and Tutz, Gerhard (2019): BTLLasso - A Common Framework and Software Package for the Inclusion and Selection of Covariates in Bradley-Terry Models, Journal of Statistical Software, to appear

Schauberger, Gunther and Tutz, Gerhard (2017): Subject-specific modelling of paired comparison data: A lasso-type penalty approach, Statistical Modelling, 17(3), 223 - 243

Examples

## Not run: 
op <- par(no.readonly = TRUE)

data(GLES)
Y <- GLES$Y
X <- scale(GLES$X, scale = FALSE)

subs <- c("(in years)","female (1); male (0)","East Germany (1); West Germany (0)",
          "(very) good (1); else (0)", "Abitur/A levels (1); else (0)", 
          "currently unemployed (1); else (0)","at least once a month (1); else (0)",
          "yes (1); no (0)")

set.seed(5)
m.gles <- cv.BTLLasso(Y = Y, X = X, control = ctrl.BTLLasso(l.lambda = 50))

par(xpd = TRUE, mar = c(5,4,4,6))
plot(m.gles, subs.X = subs)

par(op)

## End(Not run)