plot.varimp: Plot Variable Importance

In grmtree: Recursive Partitioning for Graded Response Models

Plot Variable Importance

Description

Creates a bar plot of variable importance scores with options for both ggplot2 and base R graphics.

Usage

## S3 method for class 'varimp'
plot(x, top_n = NULL, use_ggplot = TRUE, ...)

Arguments

x	A varimp object from varimp().
top_n	Number of top variables to display (NULL for all).
use_ggplot	Logical indicating whether to use ggplot2 (if available).
...	Additional arguments passed to plotting functions.

Value

Invisibly returns the input object.

See Also

varimp calculates the variable importance for GRM Forest, grmforest for GRM Forests, grmforest.control creates a control object for grmforest, plot.grmtree creates plot for the grmtree object

Examples

library(grmtree)
library(hlt)
 data("asti", package = "hlt")
 asti$resp <- data.matrix(asti[, 1:4])

 # Fit forest with default parameters
 forest <- grmforest(resp ~ gender + group, data = asti)
 imp <- varimp(forest)
 plot(imp)
 plot(imp, top_n = 1) ## select top 1 importance variable
 plot(imp, use_ggplot = FALSE) # Use base R graphics

grmtree documentation built on Sept. 2, 2026, 1:07 a.m.