`sgboost`

Implements the sparse-group boosting in to be used conjunction with the R-package `mboost`

. A formula object defining group base learners and individual base learners is used in the fitting process. Regularization is based on the degrees of freedom of individual baselearners \(df(\lambda)\) and the ones of group baselearners \(df(\lambda^{(g)})\), such that \(df(\lambda) = \alpha\) and \(df(\lambda^{(g)}) = 1- \alpha\).

You can install the development version of sgboost from GitHub with:

This is a basic example which shows you how to solve a common problem:

For a data.frame `df`

and a group structure `group_df`

, this example fits a sparse-group boosting model and plots the coefficient path:

```
library(sgboost)
set.seed(1)
df <- data.frame(
x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100),
x4 = rnorm(100), x5 = runif(100)
)
df <- df %>%
mutate_all(function(x) {
as.numeric(scale(x))
})
df$y <- df$x1 + df$x4 + df$x5
group_df <- data.frame(
group_name = c(1, 1, 1, 2, 2),
var_name = c("x1", "x2", "x3", "x4", "x5")
)
sgb_formula <- as.formula(create_formula(alpha = 0.3, group_df = group_df))
#> Warning in create_formula(alpha = 0.3, group_df = group_df): there is a group containing only one variable.
#> It will be treated as individual variable and as group
```