plot.gg_ivarpro: Plot a 'gg_ivarpro' object

In ggRandomForests: Visually Exploring Random Forests

Plot a gg_ivarpro object

Description

Branches on the presence of which_obs provenance and the class column. Distribution view: jittered points showing per-observation local importances per variable. Per-observation view: horizontal bar chart of one observation's local importances across variables. Classification: faceted by class unless which_class collapses to a single class.

Usage

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

Arguments

x	A gg_ivarpro object from gg_ivarpro().
...	Not currently used.

Value

A ggplot object.

Reading the chart

Each point in the distribution view is one observation's local importance for that variable. Variables are sorted by descending ⁠mean(|local_imp|)⁠. The cutoff line picks the variables whose local importance is, on average, large enough to flag. For a classification fit, every facet shares the same row order so you can read across.

For a classification fit, variables are sorted by descending ⁠mean(|local_imp|)⁠ across all (obs, class) rows and that ordering is shared across every facet, so rows line up between classes for visual comparison. Each facet has its own cutoff line.

The per-observation view (which_obs) is a horizontal bar chart of one observation's local importances; bars below the cutoff are grey, above are blue. The visual resembles a SHAP waterfall, but the values are release-rule contributions: scaled per-rule contrasts on observed data, not Shapley values and not permutation-based.

See Also

gg_ivarpro().

Examples

if (requireNamespace("varPro", quietly = TRUE) &&
    requireNamespace("MASS", quietly = TRUE)) {
  set.seed(1)
  v <- varPro::varpro(medv ~ ., data = MASS::Boston, ntree = 50)
  plot(gg_ivarpro(v))
}

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