plot.gg_partial_rfsrc: Plot a 'gg_partial_rfsrc' object

In ggRandomForests: Visually Exploring Random Forests

Plot a gg_partial_rfsrc object

Description

Renders the partial dependence curves from gg_partial_rfsrc as a ggplot2 figure. The layout adapts automatically to what the object contains.

Usage

## S3 method for class 'gg_partial_rfsrc'
plot(x, ...)

Arguments

x	A gg_partial_rfsrc object.
...	Not currently used.

Details

For a standard regression or classification forest, continuous predictors are drawn as line plots and categorical predictors as bar charts, both faceted by variable name – the same arrangement as plot.gg_partial.

For a survival forest, each call to partial.rfsrc returns a predicted quantity (survival probability, cumulative hazard function, or mortality) at one or more chosen time horizons. When a time column is present in the data, each horizon becomes a separate coloured curve over the predictor's value, still faceted by variable. The y-axis label (“Predicted Survival”, “Predicted CHF”, or “Predicted Mortality”) tracks the partial.type attribute set by gg_partial_rfsrc().

For a two-variable interaction surface (when xvar2.name was supplied to gg_partial_rfsrc), the secondary variable's levels become separate coloured lines, faceted by the primary predictor.

Value

A ggplot (or patchwork) object. When both continuous and categorical variables are present the two panels are combined vertically via patchwork::wrap_plots().

See Also

gg_partial_rfsrc, plot.gg_partial

Examples

## ------------------------------------------------------------
## Regression forest -- one continuous curve per variable
## ------------------------------------------------------------
set.seed(42)
airq <- na.omit(airquality)
rfsrc_airq <- randomForestSRC::rfsrc(Ozone ~ ., data = airq, ntree = 50)

pd <- gg_partial_rfsrc(rfsrc_airq, xvar.names = c("Wind", "Temp"),
                       n_eval = 10)
plot(pd)


## ------------------------------------------------------------
## Survival forest -- one curve per requested time horizon,
## faceted by variable. Y-axis label tracks `partial.type`.
## ------------------------------------------------------------
# randomForestSRC's formula parser requires the unqualified Surv() symbol;
# it Depends on `survival`, so Surv is on the search path once
# randomForestSRC is loaded.
data(veteran, package = "randomForestSRC")
set.seed(42)
rfsrc_v <- randomForestSRC::rfsrc(Surv(time, status) ~ .,
                                  data = veteran, ntree = 50)
ti  <- rfsrc_v$time.interest
t30 <- ti[which.min(abs(ti - 30))]
t90 <- ti[which.min(abs(ti - 90))]

# Default partial.type = "surv" -> y-axis "Predicted Survival"
pd_s <- gg_partial_rfsrc(rfsrc_v, xvar.names = "age",
                         partial.time = c(t30, t90), n_eval = 8)
plot(pd_s)

# partial.type = "chf" -> y-axis "Predicted CHF"
pd_c <- gg_partial_rfsrc(rfsrc_v, xvar.names = "age",
                         partial.time = c(t30, t90),
                         partial.type = "chf", n_eval = 8)
plot(pd_c)

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