```
data("bodyfat", package = "TH.data")
### fit linear model to data
model <- glmboost(DEXfat ~ ., data = bodyfat, center = TRUE)
### AIC-based selection of number of boosting iterations
maic <- AIC(model)
maic
### inspect coefficient path and AIC-based stopping criterion
par(mai = par("mai") * c(1, 1, 1, 1.8))
plot(model)
abline(v = mstop(maic), col = "lightgray")
### 10-fold cross-validation
cv10f <- cv(model.weights(model), type = "kfold")
cvm <- cvrisk(model, folds = cv10f, papply = lapply)
print(cvm)
mstop(cvm)
plot(cvm)
### 25 bootstrap iterations (manually)
set.seed(290875)
n <- nrow(bodyfat)
bs25 <- rmultinom(25, n, rep(1, n)/n)
cvm <- cvrisk(model, folds = bs25, papply = lapply)
print(cvm)
mstop(cvm)
plot(cvm)
### same by default
set.seed(290875)
cvrisk(model, papply = lapply)
### 25 bootstrap iterations (using cv)
set.seed(290875)
bs25_2 <- cv(model.weights(model), type="bootstrap")
all(bs25 == bs25_2)
if (FALSE) {
############################################################
## Do not run this example automatically as it takes
## some time (~ 5 seconds depending on the system)
### trees
blackbox <- blackboost(DEXfat ~ ., data = bodyfat)
cvtree <- cvrisk(blackbox, papply = lapply)
plot(cvtree)
## End(Not run this automatically)
}
### cvrisk in parallel modes:
if (FALSE) {
## at least not automatically
## parallel::mclapply() which is used here for parallelization only runs
## on unix systems (here we use 2 cores)
cvrisk(model, mc.cores = 2)
## infrastructure needs to be set up in advance
cl <- makeCluster(25) # e.g. to run cvrisk on 25 nodes via PVM
myApply <- function(X, FUN, ...) {
myFun <- function(...) {
library("mboost") # load mboost on nodes
FUN(...)
}
## further set up steps as required
parLapply(cl = cl, X, myFun, ...)
}
cvrisk(model, papply = myApply)
stopCluster(cl)
}
```

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