plot.boot.BTLLasso {BTLLasso}

Plot bootstrap intervals for BTLLasso

Description

Plots bootstrap intervals for every single coefficient based on bootstrap estimates calculated by boot.BTLLasso. Bootstrap intervals are separated by covariates, every covariate is plotted separately.

Usage

## S3 method for class 'boot.BTLLasso'
plot(
  x,
  quantiles = c(0.025, 0.975),
  plots_per_page = 1,
  ask_new = TRUE,
  rescale = FALSE,
  which = "all",
  include.zero = TRUE,
  rows = NULL,
  subs.X = NULL,
  subs.Z1 = NULL,
  main.Z2 = "Obj-spec. Covariates",
  ...
)

Arguments

x

boot.BTLLasso object

quantiles

Which empirical quantiles of the bootstrap estimates should be plotted?

plots_per_page

Number of plots per page, internally specified by par(mfrow=...).

ask_new

If TRUE, the user is asked before each plot.

rescale

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

which

Integer vector to specify which parameters/variables to plot.

include.zero

Should all plots contain zero?

rows

Optional argument for the number of rows in the plot. Only applies if plots_per_page>1.

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.

main.Z2

Optional character containg main for plot containing intervals for Z2 parameters.

...

other parameters to be passed through to plot function.

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

boot.BTLLasso, 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)