plot.gg_varpro: Plot a 'gg_varpro' variable importance object

In ggRandomForests: Visually Exploring Random Forests

Plot a gg_varpro variable importance object

Description

Draws a horizontal boxplot of the per-tree importance z-scores, or of the raw importances if you asked for those. Set faithful = TRUE at extract time and the per-tree points are scattered over the box; for a classification forest, conditional = TRUE splits the plot into one facet per class.

Usage

## S3 method for class 'gg_varpro'
plot(x, type, ...)

Arguments

x	A gg_varpro object from gg_varpro.
type	Character; the display scale. Leave it off and it is read from provenance$local.std: "z" when local.std = TRUE (the default), "raw" when local.std = FALSE. Asking for a scale that the extract step did not prepare raises an error.
...	Not currently used.

Details

Boxplot geometry: the hinges are the 15th and 85th percentiles of the per-tree z-distribution, and the whiskers run to the 5th and 95th. This is not a Tukey boxplot, and the plot carries a caption that says so.

faithful = TRUE: the per-tree values are jittered over the box as semi-transparent points, on the same scale as the box itself (z when local.std = TRUE, raw when local.std = FALSE). The box is drawn faint to let the points show through, and a white-outlined dot marks the mean.

conditional = TRUE: the class-conditional importances ($conditional) are shown as a faceted bar chart (facet_wrap(~class, nrow = 1)). Variables keep the sort order set by the unconditional median z in $stats, so the facets line up.

Value

A ggplot object.

Reading the boxplot

Variables are sorted top to bottom by descending median per-tree importance, so the eye lands on the most important variable first. For each variable the box spans the 15th to 85th percentile of the per-tree scores, the centre line is the median, and the whiskers run out to the 5th and 95th percentile, not the usual Tukey 1.5 IQR whiskers. The dashed vertical line is the selection cutoff (default 0.79). On the default z-score axis (local.std = TRUE) that line is a z; on the raw-importance axis (local.std = FALSE, type = "raw") it is the same numeric value but in raw-importance units. Boxes whose aggregate value sits above the line are coloured blue and flagged selected = TRUE, the rest are grey. A selected variable with a tight, high box is a variable the forest agrees on across trees. A selected variable with a wide box that straddles the cutoff is one to look at twice before relying on it.

With faithful = TRUE the box is drawn faint and the per-tree values are jittered over it as semi-transparent points, on the same scale as the box (z when local.std = TRUE, raw otherwise). A white-outlined dot marks the mean. Use this view when you want to see how individual trees voted rather than just the summary.

For a classification forest with conditional = TRUE the plot splits into one facet per class. Variables keep the unconditional sort order, so the rows line up across facets and you can read across to see which class a variable is informative for.

What this tells you

Take the variables above the cutoff as your candidate set. Use the width of the box and the per-tree overlay to gauge confidence: a narrow box well above the cutoff is a confident pick, a wide box that crosses it is a coin flip you should not lean on. For classification, conditional importance tells you which variables drive which class; a variable that is unconditionally important but only important for one class out of several is still useful, just useful for a narrower question.

See Also

gg_varpro

Examples

set.seed(42)
vp <- varPro::varpro(mpg ~ ., data = mtcars, ntree = 50)
plot(gg_varpro(vp))
plot(gg_varpro(vp, faithful = TRUE))

ggRandomForests documentation built on June 13, 2026, 5:07 p.m.