BTLLasso (version 0.1-1)

singlepaths: Plot parameter paths for BTLLasso

Description

Plots single paths for every parameter of a BTLLasso object or a cv.BTLLasso object. In contrast, to paths, one plot per covariate is created, every single parameter 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

singlepaths(model, colors = NULL, equal.ranges = FALSE, subs = NULL)

Arguments

model

BTLLasso or cv.BTLLasso object

colors

Optional. If specified, vector with length equal to the number of objects. Each object can be represented by another color.

equal.ranges

Should all single plots (for different covariates) have equal ranges on the y-axes. FALSE by default.

subs

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

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, paths

Examples

# 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 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 parameter paths, together with cv-optimal model
singlepaths(m.cv, subs = subs)

par(op)