BTLLasso (version 0.1-5)

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

Description

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.

Format

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.

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

X

Matrix containing all eight person-specific covariates

• Age: Age in years
• Gender (0: male, 1: female)

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

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 (2015): Modelling Heterogeneity in Paired Comparison Data - an L1 Penalty Approach with an Application to Party Preference Data, Department of Statistics, LMU Munich, Technical Report 183

Schauberger, Gunther, Groll Andreas and Tutz, Gerhard (2016): Modelling Football Results in the German Bundesliga Using Match-specific Covariates, Department of Statistics, LMU Munich, Technical Report 197

Examples

## Not run: ------------------------------------
# data(GLESsmall)
# 
# ## 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)")
# 
# ## vector of tuning parameters
# lambda <- exp(seq(log(61), log(1), length = 30)) - 1
# 
# 
# ## compute BTLLasso model 
# m.gles <- BTLLasso(Y = Y, X = X, Z1 = Z1, lambda = lambda)
# print(m.gles)
# 
# singlepaths(m.gles, x.axis = "loglambda", subs.X = subs.X)
# paths(m.gles, y.axis = "L2")
# 
# ## Cross-validate BTLLasso model 
# m.gles.cv <- cv.BTLLasso(Y = Y, X = X, Z1 = Z1, lambda = lambda)
# print(m.gles.cv)
# 
# singlepaths(m.gles.cv, subs.X = subs.X)
## ---------------------------------------------