Plot a gg_vimp object, extracted variable importance of a rfsrc object

Draws a horizontal bar chart of the VIMP scores extracted by gg_vimp. Each bar represents one predictor; bar length is proportional to its permutation VIMP, the average rise in OOB prediction error when that predictor's OOB values are randomly shuffled. Predictors are sorted in descending order of importance so the most influential variables appear at the top.

Usage

# S3 method for class 'gg_vimp'
plot(x, relative = FALSE, lbls, labels = NULL, ...)

Arguments

x: gg_vimp object created from a rfsrc object
relative: If TRUE, plot relative VIMP: each variable's VIMP divided by the largest VIMP in its set, so the top variable reads 1 (for classification, the top variable within each class). A set with no positive VIMP is divided by its largest absolute VIMP instead, and an all-zero set stays at zero. Defaults to FALSE, raw VIMP.
lbls: Deprecated as of v4.0.0; use labels. A named character vector of alternative variable labels.
labels: Optional variable labels for the variable axis. One of: a named character vector (c(bpd_last = "BP Diastole")); a labelled data frame, whose attr(col, "label") values are read; or a two-column key/label data frame. Variables with no label keep their raw name. Defaults to NULL (raw names).
...: optional arguments passed to gg_vimp if necessary

Value

ggplot object

Details

Bars are colored by the positive flag: a bar at or below zero (non-positive VIMP) is color-coded differently to flag predictors that hurt OOB accuracy when their signal is removed, usually a sign of collinearity or a very noisy variable. In a well-behaved forest most bars are positive; the color distinction matters when a handful are not.

References

Breiman L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

Ishwaran H, Kogalur U (2026). Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). R package version 3.6.2. https://cran.r-project.org/package=randomForestSRC

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
rfsrc_iris <- randomForestSRC::rfsrc(Species ~ ., data = iris, ntree = 50)
gg_dta <- gg_vimp(rfsrc_iris)
#> Warning: rfsrc object does not contain VIMP information. Calculating...
plot(gg_dta)


## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------
## -------- air quality data
rfsrc_airq <- randomForestSRC::rfsrc(Ozone ~ ., airquality, ntree = 50)
gg_dta <- gg_vimp(rfsrc_airq)
#> Warning: rfsrc object does not contain VIMP information. Calculating...
plot(gg_dta)