plot.gg_rfsrc: Predicted response plot from a 'gg_rfsrc' object.

In ggRandomForests: Visually Exploring Random Forests

Predicted response plot from a gg_rfsrc object.

Description

Visualizes the ensemble predictions extracted by gg_rfsrc. By default those are out-of-bag (OOB) predictions – the forest's built-in cross-validation estimate, averaging only over the trees that left a given observation out of their bootstrap sample.

Usage

## S3 method for class 'gg_rfsrc'
plot(x, notch = TRUE, ...)

Arguments

x	A gg_rfsrc object, or a raw rfsrc object (which will be passed through gg_rfsrc automatically before plotting).
notch	Logical; whether to draw notched boxplots for regression and classification forests (default TRUE). Set notch = FALSE to suppress notches when sample sizes are too small for reliable confidence intervals on the median.
...	Additional arguments forwarded to the underlying ggplot2 geometry calls. Commonly useful arguments include: alpha Numeric in [0,1]; point/ribbon transparency. For survival plots with confidence bands the ribbon alpha is automatically halved relative to the value supplied here. size Point or line size passed to geom_jitter, geom_step, etc. Arguments that control gg_rfsrc (e.g. conf.int, surv_type, by) should be applied when constructing the gg_rfsrc object before calling plot().

Details

The geometry adapts to the forest family. For regression or classification, you get a jitter-and-boxplot: every observation is a dot placed at its OOB predicted value, coloured by observed response, with a notched boxplot overlaid to show the central tendency and spread. For a survival forest, each observation contributes one ensemble survival curve (or CHF / mortality, whichever surv_type was chosen in gg_rfsrc); the bundle of step functions shows the spread of predicted risk across the cohort. Pass by to gg_rfsrc beforehand to colour curves by a predictor group, or conf.int to replace the individual curves with a mean curve and bootstrap ribbon.

Value

A ggplot object. The plot appearance depends on the forest family stored in x:

Regression ("regr")

Jitter + notched boxplot of OOB predicted values. If a group column is present the x-axis shows each group label; otherwise observations are collapsed to a single x-position.

Classification ("class")

Binary: jitter + notched boxplot of the predicted class probability. Multi-class: jitter plot with one panel per class (class probabilities in long form).

Survival ("surv")

Step curves of the ensemble survival function. When gg_rfsrc was called with conf.int, a shaded ribbon is added. When called with by, curves are coloured by group.

References

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

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

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

See Also

gg_rfsrc rfsrc randomForest

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
# Build a small classification forest (ntree=50 keeps example fast)
set.seed(42)
rfsrc_iris <- randomForestSRC::rfsrc(Species ~ ., data = iris, ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_iris)

plot(gg_dta)

## ------------------------------------------------------------
## Regression example
## ------------------------------------------------------------
## -------- air quality data
# na.action = "na.impute" handles missing Ozone / Solar.R values
set.seed(42)
rfsrc_airq <- randomForestSRC::rfsrc(Ozone ~ ., data = airquality,
                    na.action = "na.impute", ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_airq)

plot(gg_dta)

## -------- mtcars data
set.seed(42)
rfsrc_mtcars <- randomForestSRC::rfsrc(mpg ~ ., data = mtcars, ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_mtcars)

plot(gg_dta)

## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
## -------- veteran data
## randomized trial of two treatment regimens for lung cancer
data(veteran, package = "randomForestSRC")
set.seed(42)
rfsrc_veteran <- randomForestSRC::rfsrc(Surv(time, status) ~ ., data = veteran, ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_veteran)
plot(gg_dta)

# With 95% pointwise bootstrap confidence bands
gg_dta <- gg_rfsrc(rfsrc_veteran, conf.int = .95)
plot(gg_dta)

# Stratified by treatment arm
gg_dta <- gg_rfsrc(rfsrc_veteran, by = "trt")
plot(gg_dta)

## -------- pbc data (larger dataset -- skipped on CRAN)

data(pbc, package = "randomForestSRC")
# For whatever reason, the age variable is in days; convert to years
for (ind in seq_len(dim(pbc)[2])) {
  if (!is.factor(pbc[, ind])) {
    if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) {
      if (sum(range(pbc[, ind], na.rm = TRUE) == c(0, 1)) == 2) {
        pbc[, ind] <- as.logical(pbc[, ind])
      }
    }
  } else {
    if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) {
      if (sum(sort(unique(pbc[, ind])) == c(0, 1)) == 2) {
        pbc[, ind] <- as.logical(pbc[, ind])
      }
      if (sum(sort(unique(pbc[, ind])) == c(FALSE, TRUE)) == 2) {
        pbc[, ind] <- as.logical(pbc[, ind])
      }
    }
  }
  if (!is.logical(pbc[, ind]) &
    length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 5) {
    pbc[, ind] <- factor(pbc[, ind])
  }
}
# Convert age from days to years
pbc$age <- pbc$age / 364.24
pbc$years <- pbc$days / 364.24
pbc <- pbc[, -which(colnames(pbc) == "days")]
pbc$treatment <- as.numeric(pbc$treatment)
pbc$treatment[which(pbc$treatment == 1)] <- "DPCA"
pbc$treatment[which(pbc$treatment == 2)] <- "placebo"
pbc$treatment <- factor(pbc$treatment)
# Remove test-set patients (those with no assigned treatment)
dta_train <- pbc[-which(is.na(pbc$treatment)), ]

set.seed(42)
rfsrc_pbc <- randomForestSRC::rfsrc(
  Surv(years, status) ~ .,
  dta_train,
  nsplit = 10,
  na.action = "na.impute",
  forest = TRUE,
  importance = TRUE,
  save.memory = TRUE
)

gg_dta <- gg_rfsrc(rfsrc_pbc)
plot(gg_dta)

gg_dta <- gg_rfsrc(rfsrc_pbc, conf.int = .95)
plot(gg_dta)

gg_dta <- gg_rfsrc(rfsrc_pbc, by = "treatment")
plot(gg_dta)

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