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!