README.md

R-package 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 $df(\lambda^{(g)}) = 1- \alpha$.

Installation

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

# install.packages("devtools")
devtools::install_github("FabianObster/sgboost")

Example

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

library(sgboost)
library(dplyr)
library(mboost)

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

sgb_model <- mboost(formula = sgb_formula, data = df)
plot_path(sgb_model)