Exploring Random Forests with ggRandomForests

John Ehrlinger

2026-05-12

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)
<gg_error>  from randomForest  |  family: classification  |  ntree: 200  |  n: 150

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)
<gg_vimp>  from randomForest  |  family: regression  |  ntree: 150  |  n: 506  |  variables: 6
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