BTLLasso (version 0.1-2)

paths: Plot covariate paths for BTLLasso

Description

Plots paths for every covariate of a BTLLasso object or a cv.BTLLasso object. In contrast, to singlepaths, only one plot is created, every covariate is illustrated by one path. For cv.BTLLasso object, the optimal model according to the cross-validation is marked by a vertical dashed line.

Usage

paths(model)

Arguments

model

BTLLasso or cv.BTLLasso object

Details

Plots for BTLLasso and cv.BTLLasso objects only differ by the additional vertical line indicating the optimal model according to cross-validation.

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, singlepaths

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)
# 
# # 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)
# 
# par(op)
# ## End(Not run)