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ggRandomForests: Visually Exploring Random Forests

ggRandomForests provides ggplot2-based diagnostic and exploration plots for random forests fit with randomForestSRC (>= 3.4.0) or randomForest. It separates data extraction from plotting so the intermediate tidy objects can be inspected, saved, or used for custom analyses.

Installation

# CRAN (stable)
install.packages("ggRandomForests")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("ehrlinger/ggRandomForests")

Quick start

library(randomForestSRC)
library(ggRandomForests)

# 1. Fit a forest (regression)
rf <- rfsrc(medv ~ ., data = MASS::Boston, importance = TRUE)

# 2. Check convergence: did the forest grow enough trees?
plot(gg_error(rf))

# 3. Rank predictors by importance
plot(gg_vimp(rf))

# 4. Marginal dependence for top variables
gg_v <- gg_variable(rf)
plot(gg_v, xvar = "lstat")
plot(gg_v, xvar = rf$xvar.names, panel = TRUE, se = FALSE)

# 5. Partial dependence for a single predictor
pv <- plot.variable(rf, xvar.names = "lstat", partial = TRUE, show.plots = FALSE)
pd <- gg_partial(pv)
plot(pd)

For survival forests, see the package vignette:

vignette("ggRandomForests")

Function reference

Function	Input	What you get
`gg_error()`	`rfsrc` / `randomForest`	OOB error vs. number of trees
`gg_vimp()`	`rfsrc` / `randomForest`	Variable importance ranking
`gg_rfsrc()`	`rfsrc` / `randomForest`	Predicted vs. observed values
`gg_variable()`	`rfsrc` / `randomForest`	Marginal dependence data frame
`gg_partial()`	`plot.variable` output	Partial dependence (continuous + categorical)
`gg_partial_rfsrc()`	`rfsrc` model	Partial dependence via `partial.rfsrc`
`gg_survival()`	`rfsrc` survival forest	Kaplan–Meier / Nelson–Aalen estimates
`gg_roc()`	`rfsrc` / `randomForest` (class)	ROC curve data

Each gg_* function has a corresponding plot() S3 method that returns a ggplot2 object, making it easy to apply additional ggplot2 layers or themes.

Why ggRandomForests?

Separation of data and figures. gg_* functions extract tidy data objects from the forest. plot() methods turn those into ggplot2 figures. You can inspect, save, or transform the data before plotting.
Self-contained objects. Each data object holds everything needed for its plot, so figures are reproducible without the original forest in memory.
Full ggplot2 composability. Every plot() method returns a ggplot object that accepts additional layers, scales, and themes.

Recent changes

See NEWS.md for the full changelog. Highlights since v2.4.0:

v2.6.1 Fix factor-level assignment in gg_partial for categorical variables.
v2.6.0 New plotting functions exported; test coverage raised to 83%; removed internal dependency on hvtiRutilities.
v2.5.0 New gg_partial_rfsrc() computes partial dependence directly from an rfsrc model without a separate plot.variable call; supports a grouping variable via xvar2.name.

References

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

Ishwaran H. and Kogalur U.B. randomForestSRC: Random Forests for Survival, Regression and Classification. R package version >= 3.4.0. https://cran.r-project.org/package=randomForestSRC

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

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist. 2(3), 841–860.

Liaw A. and Wiener M. (2002). Classification and Regression by randomForest. R News 2(3), 18–22.

Wickham H. (2009). ggplot2: Elegant Graphics for Data Analysis. Springer New York.

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.