plot_varimp: Variable importance bar plot of a sparse group boosting model

In sgboost: Sparse-Group Boosting

Variable importance bar plot of a sparse group boosting model

Description

Visualizes the variable importance of a sparse-group boosting model. Color indicates if a predictor is an individual variable or a group.

Usage

plot_varimp(
  sgb_model,
  prop = 0,
  n_predictors = 30,
  max_char_length = 15,
  base_size = 8
)

Arguments

sgb_model	Model of type mboost to plot the variable importance.
prop	Numeric value indicating the minimal importance a predictor/baselearner has to have. Default value is zero, meaning all predictors are plotted. By increasing prop the number of plotted variables can be reduced. One can also use 'n_predictors' for limiting the number of variables to be plotted directly.
n_predictors	The maximum number of predictors to be plotted. Default is 30. Alternative to 'prop'.
max_char_length	The maximum character length of a predictor to be printed. Default is 15. For larger groups or long variable names one may adjust this number to differentiate variables from groups.
base_size	The base_size argument to be passed to the ggplot2 theme ggplot2::theme_bw to be used to control the overall size of the figure. Default value is 8.

Details

Note that aggregated group and individual variable importance printed in the legend is based only on the plotted variables and not on all variables that were selected in the sparse-group boosting model.

Value

object of type ggplot2.

See Also

get_varimp which this function uses.

Examples

library(mboost)
library(dplyr)
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))
sgb_model <- mboost(formula = sgb_formula, data = df)
sgb_varimp <- plot_varimp(sgb_model)

sgboost documentation built on April 3, 2025, 10:12 p.m.