```
data("bodyfat", package = "TH.data")
set.seed(290875)
### model conditional expectation of DEXfat given
model <- mboost(DEXfat ~
bols(age) + ### a linear function of age
btree(hipcirc, waistcirc) + ### a smooth non-linear interaction of
### hip and waist circumference
bbs(kneebreadth), ### a smooth function of kneebreadth
data = bodyfat, control = boost_control(mstop = 100))
### 10-fold cv for assessing `optimal' number of boosting iterations
cvm <- cvrisk(model, papply = lapply,
folds = cv(model.weights(model), type = "kfold"))
### probably needs larger initial mstop but the
### CRAN team is picky about running times for examples
plot(cvm)
### restrict model to mstop(cvm)
model[mstop(cvm), return = FALSE]
mstop(model)
### plot age and kneebreadth
layout(matrix(1:2, nc = 2))
plot(model, which = c("age", "kneebreadth"))
### plot interaction of hip and waist circumference
attach(bodyfat)
nd <- expand.grid(hipcirc = h <- seq(from = min(hipcirc),
to = max(hipcirc),
length = 100),
waistcirc = w <- seq(from = min(waistcirc),
to = max(waistcirc),
length = 100))
plot(model, which = 2, newdata = nd)
detach(bodyfat)
### customized plot
layout(1)
pr <- predict(model, which = "hip", newdata = nd)
persp(x = h, y = w, z = matrix(pr, nrow = 100, ncol = 100))
```

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