clinpubr (version 1.1.1)

importance_plot: Importance plot

Description

Creates an importance plot from a named vector of values.

Usage

importance_plot(
  x,
  x_lab = "Importance",
  top_n = NULL,
  color = c("#56B1F7", "#132B43"),
  show_legend = FALSE,
  split_at = NULL,
  show_labels = TRUE,
  digits = 2,
  nsmall = 3,
  scientific = TRUE,
  label_color = "black",
  label_size = 3,
  label_hjust = max(x)/10,
  save_plot = FALSE,
  filename = "importance.png"
)

Value

A ggplot object

Arguments

x

A named vector of values, typically importance scores from models.

x_lab

A character string for the x-axis label.

top_n

The number of top values to show. If NULL, all values are shown.

color

A length-2 vector of low and high colors, or a single color for the bars.

show_legend

A logical value indicating whether to show the legend.

split_at

The index at which to split the plot into two halves, usually used to illustrate variable selection. If NULL, no split is made.

show_labels

A logical value indicating whether to show the value labels on the bars.

digits, nsmall, scientific

Controls the formatting of labels. Passed to format().

label_color

The color of the labels.

label_size

The size of the labels.

label_hjust

The horizontal justification of the labels.

save_plot

A logical value indicating whether to save the plot.

filename

The filename to save the plot as.

Details

The importance plot is a bar plot that shows the importance of each variable in a model. The variables are sorted in descending order of importance, and the top_n variables are shown. If top_n is NULL, all variables are shown. The plot can be split into two halves at a specified index, which is useful for illustrating variable selection.

Examples

set.seed(1)
dummy_importance <- runif(20)^5
names(dummy_importance) <- paste0("var", 1:20)
importance_plot(dummy_importance, top_n = 15, split_at = 10, save_plot = FALSE)