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Exploring Random Forests with ggRandomForests

John Ehrlinger

2026-04-28

The ggRandomForests package extracts tidy data objects from either randomForestSRC or randomForest fits and feeds them into familiar ggplot2 workflows. This vignette highlights the most common objects— gg_error, gg_variable, and gg_vimp—along with a small helper for building balanced conditioning intervals.

Error trajectories with gg_error()

library(randomForest)
set.seed(42)
rf_iris <- randomForest(Species ~ ., data = iris, ntree = 200, keep.forest = TRUE)
err_df <- ggRandomForests::gg_error(rf_iris, training = TRUE)
head(err_df)
         OOB setosa versicolor  virginica ntree      train
1 0.06349206      0 0.08695652 0.13333333     1 0.02666667
2 0.04255319      0 0.03225806 0.10714286     2 0.02000000
3 0.04761905      0 0.05714286 0.09375000     3 0.02666667
4 0.04098361      0 0.07500000 0.05263158     4 0.02000000
5 0.05426357      0 0.06976744 0.10256410     5 0.01333333
6 0.05970149      0 0.08888889 0.09756098     6 0.01333333

The gg_error() object stores the cumulative OOB error rate for each outcome column plus the ntree counter. When training = TRUE, the function reconstructs the original model frame and appends the in-bag error trajectory (train). Plotting overlays both curves by default:

plot(err_df)

Marginal dependence via gg_variable()

set.seed(99)
boston <- MASS::Boston
rf_boston <- randomForest(medv ~ ., data = boston, ntree = 150)
var_df <- ggRandomForests::gg_variable(rf_boston)
str(var_df[, c("lstat", "yhat")])
Classes 'gg_variable', 'regression' and 'data.frame':   506 obs. of  2 variables:
 $ lstat: num  4.98 9.14 4.03 2.94 5.33 ...
 $ yhat : num  29.2 22.5 35.1 36.4 33.4 ...

Because the original training data are recovered from the model call, gg_variable() works even when the forest was trained within helper functions or against a subset() expression. The output keeps the raw predictors plus either a continuous yhat column (regression) or per-class probabilities (yhat.<class> for classification). Plotting a single variable is straightforward:

plot(var_df, xvar = "lstat")
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Survival forests can request multiple horizons using the time argument; non-OOB predictions are available by setting oob = FALSE.

Variable importance with gg_vimp()

vimp_df <- ggRandomForests::gg_vimp(rf_boston)
head(vimp_df)
# A tibble: 6 × 4
  vars    set     vimp positive
  <fct>   <chr>  <dbl> <lgl>   
1 lstat   vimp  13005. TRUE    
2 rm      vimp  11662. TRUE    
3 dis     vimp   2849. TRUE    
4 indus   vimp   2751. TRUE    
5 ptratio vimp   2698. TRUE    
6 crim    vimp   2646. TRUE    
plot(vimp_df)

If a randomForest object lacks stored importance scores, gg_vimp() tries to compute them on the fly. When the forest truly cannot provide the information (for example when importance = FALSE and the predictors are no longer accessible), the function emits a warning and returns NA placeholders so plots still render.

Balanced conditioning cuts with quantile_pts()

rm_breaks <- ggRandomForests::quantile_pts(boston$rm, groups = 6, intervals = TRUE)
rm_groups <- cut(boston$rm, breaks = rm_breaks)
table(rm_groups)
rm_groups
(3.56,5.76] (5.76,5.99] (5.99,6.21] (6.21,6.44] (6.44,6.85] (6.85,8.78] 
         85          84          84          85          84          84

The helper wraps stats::quantile() to produce evenly populated strata that drop directly into cut() when building coplots or facet labels.

Next steps

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.