Hello,

While using the mboost package,the code below implements three different base-learners in the same model:

```
model <- mboost(DEXfat ~
bols(age) + ### a linear function of age
btree(hipcirc, waistcirc) + ### a non-linear interaction of hip and waist circumference
bbs(kneebreadth), ### a smooth function of kneebreadth
data = bodyfat, control = boost_control(mstop = 100))
```

I understand that DeXfat is predicted based on three different functions(bols,btree,bbs).

What is the purpose of combining three different models and I also could not understand how to interpret the output of such a model.

Also after creating the model,to visualize the output:

```
cvm <- cvrisk(model, papply = lapply)
### 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"))
```

In this output,what is the f-partial in the y-axis??

I know these questions might be quite basic,but can somebody please help me understand this!