```
data(iris)
### glmboost with multiple variables and intercept
iris$setosa <- factor(iris$Species == "setosa")
iris_glm <- glmboost(setosa ~ 1 + Sepal.Width + Sepal.Length + Petal.Width +
Petal.Length,
data = iris, control = boost_control(mstop = 50),
family = Binomial(link = c("logit")))
varimp(iris_glm)
### importance plot with four bars only
plot(varimp(iris_glm), nbars = 4)
### gamboost with multiple variables
iris_gam <- gamboost(Sepal.Width ~
bols(Sepal.Length, by = setosa) +
bbs(Sepal.Length, by = setosa, center = TRUE) +
bols(Petal.Width) +
bbs(Petal.Width, center = TRUE) +
bols(Petal.Length) +
bbs(Petal.Length, center = TRUE),
data = iris)
varimp(iris_gam)
### stacked importance plot with base-learners in rev. alphabetical order
plot(varimp(iris_gam), blorder = "rev_alphabetical")
### similar ggplot
if (FALSE) {
library(ggplot2)
ggplot(data.frame(varimp(iris_gam)), aes(variable, reduction, fill = blearner)) +
geom_bar(stat = "identity") + coord_flip() }
```

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