ctrl.BTLLasso {BTLLasso}  R Documentation 
Control parameters for different penalty terms and for tuning the fitting algorithm.
ctrl.BTLLasso( l.lambda = 30, log.lambda = TRUE, lambda.min = 0.05, adaptive = TRUE, scale = TRUE, norm = c("L1", "L2"), epsilon = 1e04, lambda2 = 1e04, c = 1e09, precision = 3, weight.penalties = TRUE, include.intercepts = TRUE, order.effect = FALSE, object.order.effect = FALSE, order.center = FALSE, name.order = "Order", penalize.intercepts = FALSE, penalize.X = TRUE, penalize.Z2 = FALSE, penalize.Z1.absolute = TRUE, penalize.Z1.diffs = TRUE, penalize.order.effect.absolute = TRUE, penalize.order.effect.diffs = FALSE )
l.lambda 
Number of tuning parameters. Applies only if 
log.lambda 
Should the grid of tuning parameters be created on a logarithmic scale
rather than equidistant. Applies only if 
lambda.min 
Minimal value for tuning parameter. Applies only if 
adaptive 
Should adaptive lasso be used? Default is TRUE. 
scale 
Should the covariates be scaled so that they are on comparable scales? Default is TRUE.
Variables will be properly scaled AND centered. Please note that results will refer to scaled covariates.
If 
norm 
Specifies the norm used in the penalty term. Currently, only 'L1' and 'L2' are possible. Default is to 'L1', only 'L1' allows for clustering and variable selection. 
epsilon 
Threshold value for convergence of the algorithm. 
lambda2 
Tuning parameter for ridge penalty on all coefficients. Should be small, only used to stabilize results. 
c 
Internal parameter for the quadratic approximation of the L1
penalty. Should be sufficiently small. For details see

precision 
Precision for final parameter estimates, specifies number of decimals. 
weight.penalties 
Should the penalties across the different model components
(i.e. intercepts, order effects, X, Z1, Z2) be weighted according to the number of
penalties included? Default is 
include.intercepts 
Should intercepts be included in the model? 
order.effect 
Should a global order effect (corresponding to home effect in sports applications) be included in the model? 
object.order.effect 
Should objectspecific order effects (corresponding to home effects in sports applications) be included in the model? 
order.center 
Should (in case of objectspecific order effects) the order effects be centered in the design matrix? Centering is equivalent to the coding scheme of effect coding instead of dummy coding. 
name.order 
How should the order effect(s) be called in plots or prints. 
penalize.intercepts 
Should intercepts be penalized? If 
penalize.X 
Should effects from X matrix be penalized? If 
penalize.Z2 
Should absolute values of effects from Z2 matrix be penalized? Can also be used with a character vector as input. Then, the character vector contains the names of the variables from Z2 whose parameters should be penalized. 
penalize.Z1.absolute 
Should absolute values of effects from Z1 matrix be penalized? Can also be used with a character vector as input. Then, the character vector contains the names of the variables from Z1 whose parameters should be penalized. 
penalize.Z1.diffs 
Should differences of effects from Z1 matrix be
penalized? If 
penalize.order.effect.absolute 
Should absolute values of order effect(s) be penalized?
Only relevant if either 
penalize.order.effect.diffs 
Should differences of order effects be
penalized? If 
Gunther Schauberger
gunther.schauberger@tum.de
Schauberger, Gunther and Tutz, Gerhard (2019): BTLLasso  A Common Framework and Software Package for the Inclusion and Selection of Covariates in BradleyTerry Models, Journal of Statistical Software, 88(9), 129, https://doi.org/10.18637/jss.v088.i09
Schauberger, Gunther and Tutz, Gerhard (2017): Subjectspecific modelling of paired comparison data: A lassotype penalty approach, Statistical Modelling, 17(3), 223  243
Schauberger, Gunther, Groll Andreas and Tutz, Gerhard (2018): Analysis of the importance of onfield covariates in the German Bundesliga, Journal of Applied Statistics, 45(9), 1561  1578
## Not run: op < par(no.readonly = TRUE) ############################## ##### Example with simulated data set containing X, Z1 and Z2 ############################## data(SimData) ## Specify control argument ## > allow for objectspecific 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) ## Crossvalidate 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$Z1, scale = FALSE) ## vector of subtitles, containing the coding of the X covariates subs.X < c('', 'female (1); male (0)') ## Crossvalidate BTLLasso model m.gles.cv < cv.BTLLasso(Y = Y, X = X, Z1 = Z1) m.gles.cv coef(m.gles.cv) logLik(m.gles.cv) head(predict(m.gles.cv, type="response")) head(predict(m.gles.cv, type="trait")) par(xpd = TRUE, mar = c(5,4,4,6)) plot(m.gles.cv, subs.X = subs.X, plots_per_page = 4, which = 2:5) paths(m.gles.cv, y.axis = 'L2') ############################## ##### Example with Bundesliga data set ############################## data(Buli1516) Y < Buli1516$Y5 Z1 < scale(Buli1516$Z1, scale = FALSE) ctrl.buli < ctrl.BTLLasso(object.order.effect = TRUE, name.order = "Home", penalize.order.effect.diffs = TRUE, penalize.order.effect.absolute = FALSE, order.center = TRUE, lambda2 = 1e2) set.seed(1860) m.buli < cv.BTLLasso(Y = Y, Z1 = Z1, control = ctrl.buli) m.buli par(xpd = TRUE, mar = c(5,4,4,6)) plot(m.buli) ############################## ##### Example with Topmodel data set ############################## data("Topmodel2007", package = "psychotree") Y.models < response.BTLLasso(Topmodel2007$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)