GLESsmall {BTLLasso}R Documentation

Subset of the GLES data set with 200 observations and 4 covariates.


This is a subset of the GLES data set from the German Longitudinal Election Study (GLES), see Rattinger et al. (2014). The subset contains only 200 of the 2003 observations and only a small part of the covariates. 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.


A list containing data from the German Longitudinal Election Study with 200 observations. The list contains both information on the response (paired comparisons) and different covariates.


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.


Matrix containing all eight person-specific covariates

  • Age: Age in years

  • Gender (0: male, 1: female)


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.

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



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

See Also



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


## extract data and center covariates for better interpretability
Y <- GLESsmall$Y
X <- scale(GLESsmall$X, scale = FALSE)
Z1 <- scale(GLESsmall$Z1, scale = FALSE)

## vector of subtitles, containing the coding of the X covariates
subs.X <- c('', 'female (1); male (0)')

## Cross-validate BTLLasso model <- cv.BTLLasso(Y = Y, X = X, Z1 = Z1)


head(predict(, type="response"))
head(predict(, type="trait"))

par(xpd = TRUE, mar = c(5,4,4,6))
plot(, subs.X = subs.X, plots_per_page = 4, which = 2:5)
paths(, y.axis = 'L2')


## End(Not run)

[Package BTLLasso version 0.1-13 Index]