plot.gg_partialpro: Plot a 'gg_partialpro' object

In ggRandomForests: Visually Exploring Random Forests

Plot a gg_partialpro object

Description

Produces ggplot2 partial dependence curves from the named list returned by gg_partialpro, which wraps varpro::partialpro output.

Usage

## S3 method for class 'gg_partialpro'
plot(x, type = c("parametric", "nonparametric", "causal"), ...)

Arguments

x	A gg_partialpro object.
type	Character vector; one or more of "parametric", "nonparametric", "causal". Defaults to all three.
...	Not currently used.

Details

Each variable produces up to three effect curves: parametric, nonparametric, and causal. The type argument controls which are shown.

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_partialpro

Examples

## ggRandomForests does not depend on the varpro package; we construct a
## minimal mock of the partialpro() output so the example runs everywhere.
set.seed(42)
n_obs <- 30
n_pts <- 15
mock_data <- list(
  age = list(
    xvirtual    = seq(30, 80, length.out = n_pts),
    xorg        = sample(seq(30, 80, by = 5), n_obs, replace = TRUE),
    yhat.par    = matrix(rnorm(n_obs * n_pts), nrow = n_obs),
    yhat.nonpar = matrix(rnorm(n_obs * n_pts), nrow = n_obs),
    yhat.causal = matrix(rnorm(n_obs * n_pts), nrow = n_obs)
  ),
  sex = list(
    xvirtual    = c(0, 1),
    xorg        = sample(c(0, 1), n_obs, replace = TRUE),
    yhat.par    = matrix(rnorm(n_obs * 2), nrow = n_obs),
    yhat.nonpar = matrix(rnorm(n_obs * 2), nrow = n_obs),
    yhat.causal = matrix(rnorm(n_obs * 2), nrow = n_obs)
  )
)

pp <- gg_partialpro(mock_data)

# Returns a single ggplot/patchwork figure combining continuous and
# categorical panels when both variable types are present.
plot(pp)

# Restrict to one or two effect types
plot(pp, type = "nonparametric")
plot(pp, type = c("parametric", "causal"))

ggRandomForests documentation built on May 12, 2026, 5:07 p.m.