plot.gg_partial_rfsrc: Plot a 'gg_partial_rfsrc' object

In ggRandomForests: Visually Exploring Random Forests

Plot a gg_partial_rfsrc object

Description

Produces ggplot2 partial dependence curves from the named list returned by gg_partial_rfsrc.

Usage

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

Arguments

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

Details

For standard (non-survival) forests: continuous predictors are line plots, categorical predictors are bar charts, both faceted by variable name.

For survival forests (when a time column is present): each evaluation time point is a separate curve over the predictor's value, faceted by variable name. The y-axis label adapts to the partial.type stored on the object (“Predicted Survival”, “Predicted CHF”, or “Predicted Mortality”).

For two-variable surface plots (when a grp column is present): each group level is a separate line, faceted by primary predictor name.

Value

A single ggplot object, or a named list with elements continuous and categorical when both types are present.

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 May 2, 2026, 5:06 p.m.