gg_rfsrc.rfsrc: Predicted response data object

In ggRandomForests: Visually Exploring Random Forests

Predicted response data object

Description

Every tree in a random forest makes its own prediction, and the forest's "ensemble" prediction is the average across all trees. The out-of-bag (OOB) variant averages only over the trees that did not include a given observation in their bootstrap sample – a built-in cross-validation estimate that requires no held-out test set. gg_rfsrc pulls those ensemble predictions out of the fitted forest and arranges them for plotting with plot.gg_rfsrc.

Usage

## S3 method for class 'rfsrc'
gg_rfsrc(object, oob = TRUE, by, ...)

Arguments

object	A fitted rfsrc or randomForest object.
oob	Logical; if TRUE (default) return out-of-bag predictions. Set to FALSE to use full in-bag (training) predictions. Forced to FALSE automatically for predict.rfsrc objects, which carry no OOB estimates.
by	Optional stratifying variable. Either a character column name present in the training data, or a vector/factor of the same length as the training set. When supplied, a group column is added to the returned data and bootstrap CI bands (survival) are computed per group. Omit or leave missing to return an unstratified result.
...	Additional arguments controlling output for specific forest families: surv_type Character; one of "surv" (default), "chf", or "mortality" for survival forests. conf.int Numeric coverage probability (e.g. 0.95) to request bootstrap pointwise confidence bands for survival forests. Triggers wide-format output with lower, upper, median, and mean columns. bs.sample Integer; number of bootstrap resamples when conf.int is set. Defaults to the number of observations.

Details

The structure of the returned data depends on the forest family. For a regression forest you get a scatter of OOB predicted values against the observed response. For classification you get predicted class probabilities alongside the observed class label. For a survival forest you get the ensemble survival function (or cumulative hazard, or mortality, controlled by surv_type) at each unique event time – one curve per observation – which together trace the range of predicted risk in the cohort. Pass conf.int to add pointwise bootstrap confidence bands around the mean survival curve, or by to stratify all of the above by a predictor group.

For survival forests, use the surv_type argument ("surv", "chf", or "mortality") to select the predicted quantity. Bootstrap confidence bands are requested by passing conf.int (e.g. conf.int = 0.95); the number of resamples is controlled by bs.sample.

Value

A gg_rfsrc object (a classed data.frame) whose structure depends on the forest family:

regression

Columns yhat and the response name; optionally a group column when by is supplied.

classification

One column per class with predicted probabilities; a y column with observed class labels; optionally group.

survival (no CI / grouping)

Long-format with columns variable (event time), value (survival probability), obs_id, and event.

survival (with conf.int or by)

Wide-format with pointwise bootstrap CI columns (lower, upper, median, mean) per time point; a group column when by is supplied.

The object carries class attributes for the forest family so that plot.gg_rfsrc dispatches correctly.

See Also

plot.gg_rfsrc, rfsrc, gg_survival

Examples

## ------------------------------------------------------------
## classification example (small, runs on CRAN)
## ------------------------------------------------------------
## -------- iris data
set.seed(42)
rfsrc_iris <- randomForestSRC::rfsrc(Species ~ ., data = iris, ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_iris)
plot(gg_dta)


## ------------------------------------------------------------
## Additional regression / survival examples are guarded with
## \donttest because the cumulative example time exceeds the
## 10-second CRAN budget. Run locally with `R CMD check --run-donttest`
## (or `devtools::check(run_dont_test = TRUE)`) to exercise them.
## ------------------------------------------------------------

## -------- air quality data (regression)
rfsrc_airq <- randomForestSRC::rfsrc(Ozone ~ ., data = airquality,
                    na.action = "na.impute", ntree = 50)
plot(gg_rfsrc(rfsrc_airq))

## -------- Boston data (rfsrc + randomForest)
if (requireNamespace("MASS", quietly = TRUE)) {
  data(Boston, package = "MASS")
  Boston$chas <- as.logical(Boston$chas)
  rfsrc_boston <- randomForestSRC::rfsrc(medv ~ ., data = Boston, ntree = 50,
                        forest = TRUE, importance = TRUE,
                        tree.err = TRUE, save.memory = TRUE)
  plot(gg_rfsrc(rfsrc_boston))

  rf_boston <- randomForest::randomForest(medv ~ ., data = Boston,
                                          ntree = 50)
  plot(gg_rfsrc(rf_boston))
}

## -------- mtcars data
rfsrc_mtcars <- randomForestSRC::rfsrc(mpg ~ ., data = mtcars, ntree = 50)
plot(gg_rfsrc(rfsrc_mtcars))

## -------- veteran data (survival; with CI and group-by)
data(veteran, package = "randomForestSRC")
rfsrc_veteran <- randomForestSRC::rfsrc(Surv(time, status) ~ ., data = veteran,
                       ntree = 50)
plot(gg_rfsrc(rfsrc_veteran))
plot(gg_rfsrc(rfsrc_veteran, conf.int = .95))
plot(gg_rfsrc(rfsrc_veteran, by = "trt"))

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