BTLLasso (version 0.1-2)

BTLLasso-package: BTLLasso

Description

Performs BTLLasso, a method to model heterogeneity in paired comparison data. Subject-specific covariates are allowd to have an influence on the attractivity/strength of the objects. An L1 penalty on the pairwise differences between the object-specific parameters allows for both clustering of object with regard to covariates and elimination of irrelevant covariates. Several additional functions are provided, such as cross-validation, bootstraped confidence intervals, and several plot functions.

References

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

See Also

BTLLasso, cv.BTLLasso

Examples

## Not run: 
# # load data set
# data(GLESsmall)
# 
# # define response and covariate matrix
# X <- scale(GLESsmall[, 11:14])
# Y <- as.matrix(GLESsmall[, 1:10])
# 
# # vector of subtitles, containing the coding of the single covariates
# subs <- c("(in years)","female (1); male (0)",
# "East Germany (1); West Germany (0)","(very) good (1); else (0)")
# 
# # vector of tuning parameters
# lambda <- exp(seq(log(31),log(1),length=50))-1
# 
# # compute BTLLasso model
# m <- BTLLasso(Y = Y, X = X, lambda = lambda)
# 
# op <- par(no.readonly = TRUE) 
# par(mar=c(5,4,4,8))
# 
# # plot covariate paths
# paths(m)
# 
# # plot parameter paths
# singlepaths(m, subs = subs)
# 
# # compute 10-fold cross-validation
# set.seed(5)
# m.cv <- cv.BTLLasso(Y = Y, X = X, folds = 10, lambda = lambda, cores = 10)
# 
# # plot covariate paths, together with cv-optimal model
# paths(m.cv)
# 
# # plot parameter paths, together with cv-optimal model
# singlepaths(m.cv, subs = subs)
# 
# # compute bootstrap confidence intervals
# m.boot <- boot.BTLLasso(m.cv, B = 100, cores = 25)
# 
# # plot bootstrap confidence intervals
# par(mar=c(5,5,4,3))
# ci.BTLLasso(m.boot, subs = subs)
# 
# par(op)
# ## End(Not run)