GLESsmall {BTLLasso} | R Documentation |

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.

- 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)

- 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.

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

```
## Not run:
op <- par(no.readonly = TRUE)
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)')
## Cross-validate BTLLasso model
m.gles.cv <- cv.BTLLasso(Y = Y, X = X, Z1 = Z1)
m.gles.cv
coef(m.gles.cv)
logLik(m.gles.cv)
head(predict(m.gles.cv, type="response"))
head(predict(m.gles.cv, type="trait"))
par(xpd = TRUE, mar = c(5,4,4,6))
plot(m.gles.cv, subs.X = subs.X, plots_per_page = 4, which = 2:5)
paths(m.gles.cv, y.axis = 'L2')
par(op)
## End(Not run)
```

[Package *BTLLasso* version 0.1-11 Index]