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BTLLasso (version 0.1-11)

Modelling Heterogeneity in Paired Comparison Data

Description

Performs 'BTLLasso' as described by Schauberger and Tutz (2019) and Schauberger and Tutz (2017) . BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.

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Version

Install

install.packages('BTLLasso')

Monthly Downloads

254

Version

0.1-11

License

GPL (>= 2)

Maintainer

Gunther Schauberger

Last Published

October 7th, 2020

Functions in BTLLasso (0.1-11)

print.cv.BTLLasso

Print function for cv.BTLLasso objects
Buli1718

Bundesliga Data 2017/18 (Buli1718)
Buli1617

Bundesliga Data 2016/17 (Buli1617)
print.BTLLasso

Print function for BTLLasso objects
predict.BTLLasso

Predict function for BTLLasso
Buli1516

Bundesliga Data 2015/16 (Buli1516)
Buli1415

Bundesliga Data 2014/15 (Buli1415)
paths

Plot covariate paths for BTLLasso
cv.BTLLasso

Cross-validation function for BTLLasso
response.BTLLasso

Create response object for BTLLasso
BuliResponse

Bundesliga Data Response Data (BuliResponse)
boot.BTLLasso

Bootstrap function for BTLLasso
plot.BTLLasso

Plot parameter paths for BTLLasso
GLES

German Longitudinal Election Study (GLES)
ctrl.BTLLasso

Control function for BTLLasso
plot.boot.BTLLasso

Plot bootstrap intervals for BTLLasso
BTLLasso

Function to perform BTLLasso
BTLLasso-package

BTLLasso
SimData

Simulated data set for illustration
GLESsmall

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

Print function for boot.BTLLasso objects