BTLLasso-package {BTLLasso}

BTLLasso

Description

Performs BTLLasso, a method to model heterogeneity in paired comparison data. Different types of covariates are allowd to have an influence on the attractivity/strength of the objects. Covariates can be subject-specific, object-specific or subject-object-specific. L1 penalties are used to reduce the complexity of the model by enforcing clusters of equal effects or by elimination of irrelevant covariates. Several additional functions are provided, such as cross-validation, bootstrap intervals, and plot functions.

Author(s)

Gunther Schauberger
[e-mail]

References

Schauberger, Gunther and Tutz, Gerhard (2019): BTLLasso - A Common Framework and Software Package for the Inclusion and Selection of Covariates in Bradley-Terry Models, Journal of Statistical Software, 88(9), 1-29, doi:10.18637/jss.v088.i09

Schauberger, Gunther and Tutz, Gerhard (2017): Subject-specific modelling of paired comparison data: A lasso-type penalty approach, Statistical Modelling, 17(3), 223 - 243

Schauberger, Gunther, Groll Andreas and Tutz, Gerhard (2018): Analysis of the importance of on-field covariates in the German Bundesliga, Journal of Applied Statistics, 45(9), 1561 - 1578

See Also

BTLLasso, cv.BTLLasso

Examples

## Not run: 
op <- par(no.readonly = TRUE)

##############################
##### Example with simulated data set containing X, Z1 and Z2
##############################
data(SimData)

## 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, control = ctrl)
m.sim

par(xpd = TRUE)
plot(m.sim)


## Cross-validate BTLLasso model for tuning parameters lambda
set.seed(1860)
m.sim.cv <- cv.BTLLasso(Y = SimData$Y, X = SimData$X, Z1 = SimData$Z1,
                        Z2 = SimData$Z2, control = ctrl)
m.sim.cv
coef(m.sim.cv)
logLik(m.sim.cv)

head(predict(m.sim.cv, type="response"))
head(predict(m.sim.cv, type="trait"))

plot(m.sim.cv, plots_per_page = 4)


## Example for bootstrap intervals for illustration only
## Don't calculate bootstrap intervals with B = 20!!!!
set.seed(1860)
m.sim.boot <- boot.BTLLasso(m.sim.cv, B = 20, cores = 20)
m.sim.boot
plot(m.sim.boot, plots_per_page = 4)


##############################
##### 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)')

## Cross-validate BTLLasso model
m.gles.cv <- cv.BTLLasso(Y = Y, X = X, Z1 = Z1)
m.gles.cv

coef(m.gles.cv)
logLik(m.gles.cv)

head(predict(m.gles.cv, type="response"))
head(predict(m.gles.cv, type="trait"))

par(xpd = TRUE, mar = c(5,4,4,6))
plot(m.gles.cv, subs.X = subs.X, plots_per_page = 4, which = 2:5)
paths(m.gles.cv, y.axis = 'L2')


##############################
##### Example with Bundesliga data set
##############################
data(Buli1516)

Y <- Buli1516$Y5

Z1 <- scale(Buli1516$Z1, scale = FALSE)

ctrl.buli <- ctrl.BTLLasso(object.order.effect = TRUE, 
                           name.order = "Home", 
                           penalize.order.effect.diffs = TRUE, 
                           penalize.order.effect.absolute = FALSE,
                           order.center = TRUE, lambda2 = 1e-2)

set.seed(1860)
m.buli <- cv.BTLLasso(Y = Y, Z1 = Z1, control = ctrl.buli)
m.buli

par(xpd = TRUE, mar = c(5,4,4,6))
plot(m.buli)


##############################
##### Example with Topmodel data set
##############################
data("Topmodel2007", package = "psychotree")

Y.models <- response.BTLLasso(Topmodel2007$preference)
X.models <- scale(model.matrix(preference~., data = Topmodel2007)[,-1])
rownames(X.models) <- paste0("Subject",1:nrow(X.models))
colnames(X.models) <- c("Gender","Age","KnowShow","WatchShow","WatchFinal")

set.seed(5)
m.models <- cv.BTLLasso(Y = Y.models, X = X.models)
plot(m.models, plots_per_page = 6)

par(op)

## End(Not run)