print.boot.BTLLasso: Print function for boot.BTLLasso objects

Description

Prints the most important output of boot.BTLLasso objects.

Usage

# S3 method for boot.BTLLasso
print(x, rescale = FALSE, ...)

Arguments

boot.BTLLasso object

rescale

Should the parameter estimates be rescaled for plotting? Only applies if scale = TRUE was specified in BTLLasso or cv.BTLLasso.

…

possible further arguments for print command

Value

boot.BTLLasso object

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

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

Run this code


## Not run: ------------------------------------
# ##### Example with simulated data set containing X, Z1 and Z2
# data(SimData)
# 
# ## Specify tuning parameters
# lambda <- exp(seq(log(151), log(1.05), length = 30)) - 1
# 
# ## Specify control argument
# ## -> allow for object-specific order effects and penalize intercepts
# ctrl <- ctrl.BTLLasso(penalize.intercepts = TRUE, object.order.effect = TRUE,
#                       penalize.order.effect.diffs = TRUE)
# 
# ## Simple BTLLasso model for tuning parameters lambda
# m.sim <- BTLLasso(Y = SimData$Y, X = SimData$X, Z1 = SimData$Z1, 
#                   Z2 = SimData$Z2, lambda = lambda, control = ctrl)
# print(m.sim)
# 
# singlepaths(m.sim)
# 
# ## Cross-validate BTLLasso model for tuning parameters lambda
# set.seed(5)
# m.sim.cv <- cv.BTLLasso(Y = SimData$Y, X = SimData$X, Z1 = SimData$Z1, 
#                         Z2 = SimData$Z2, lambda = lambda, control = ctrl)
# print(m.sim.cv)
# 
# singlepaths(m.sim.cv, plot.order.effect = FALSE, 
#             plot.intercepts = FALSE, plot.Z2 = FALSE)
# paths(m.sim.cv, y.axis = 'L2')
# 
# ## Example for bootstrap confidence intervals for illustration only
# ## Don't calculate bootstrap confidence intervals with B = 10!!!!
# set.seed(5)
# m.sim.boot <- boot.BTLLasso(m.sim.cv, B = 10, cores = 10)
# print(m.sim.boot)
# ci.BTLLasso(m.sim.boot)
# 
# 
# ##### Example with small version from GLES data set
# 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, 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)
## ---------------------------------------------