BTLLasso (version 0.1-5)

SimData: Simulated Data Set for illustration

Description

This data set is a simulated data set including all possible types of covariates (X, Z1 and Z2) and is intended to serve for illustration purpose. The data set contains paired comparisons between four objects with five different response categories from 200 subjects.

Format

A list containing simulated data for 200 observations. The list contains both information on the response (paired comparisons) and different covariates.

Y

A response.BTLLasso object with simulated responses including

• response: Ordinal paired comparison response vector
• first.object: Vector containing the first-named object per paired comparison
• second.object: Vector containing the second-named object per paired comparison
• subject: Vector containing a subject identifier per paired comparison

X

Matrix containing both subject-specific covariates

• X_var1
• X_var2

Z1

Matrix containing both subject-object-specific covariates

• Z1_var1
• Z1_var2

Z2

Matrix containing both object-specific covariates

• Z2_var1
• Z2_var2

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, x.axis = "loglambda")
# 
# ## 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, x.axis = "loglambda", 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, x.axis = "loglambda", 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, x.axis = "loglambda", subs.X = subs.X)
## ---------------------------------------------