BTLLasso (version 0.1-5)

ci.BTLLasso: Plot confidence intervals for BTLLasso

Description

Plots confidence intervals for every single coefficient. Confidence intervals are separated by covariates, every covariate is plotted separately. Confidence intervals are based on bootstrap, performed by boot.BTLLasso.

Usage

ci.BTLLasso(object, rescale = FALSE, plot.X = TRUE, plot.Z1 = TRUE,
  plot.Z2 = TRUE, plot.intercepts = TRUE, plot.order.effects = TRUE,
  include.zero = TRUE, columns = NULL, subs.X = NULL, subs.Z1 = NULL)

Arguments

object

boot.BTLLasso object

rescale

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

plot.X

Should confidence intervals for variables in X (if present) be plotted?

plot.Z1

Should confidence intervals for variables in Z1 (if present) be plotted?

plot.Z2

Should confidence intervals for variables in Z2 (if present) be plotted?

plot.intercepts

Should confidence intervals for intercepts be plotted separately?

plot.order.effects

Should confidence intervals for order effects be plotted separately?

include.zero

Should all plots contain zero?

columns

Optional argument for the number of columns in the plot.

subs.X

Optional vector of subtitles for variables in X. Can be used to note the encoding of the single covariates, especially for dummy variables.

subs.Z1

Optional vector of subtitles for variables in Z1. Can be used to note the encoding of the single covariates, especially for dummy variables.

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

See Also

boot.BTLLasso, BTLLasso, cv.BTLLasso

Examples

## 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)
## ---------------------------------------------