plot.parttree: Plot decision tree partitions

In parttree: Visualize Simple 2-D Decision Tree Partitions

Plot decision tree partitions

Description

Provides a plot method for parttree objects.

Usage

## S3 method for class 'parttree'
plot(
  x,
  raw = TRUE,
  border = "black",
  fill_alpha = 0.3,
  expand = TRUE,
  jitter = FALSE,
  add = FALSE,
  ...
)

Arguments

x	A parttree data frame.
raw	Logical. Should the raw (original) data points be plotted too? Default is TRUE.
border	Colour of the partition borders (edges). Default is "black". To remove the borders altogether, specify as NA.
fill_alpha	Numeric in the range ⁠[0,1]⁠. Alpha transparency of the filled partition rectangles. Default is 0.3.
expand	Logical. Should the partition limits be expanded to to meet the edge of the plot axes? Default is TRUE. If FALSE, then the partition limits will extend only until the range of the raw data.
jitter	Logical. Should the raw points be jittered? Default is FALSE. Only evaluated if raw = TRUE.
add	Logical. Add to an existing plot? Default is FALSE.
...	Additional arguments passed down to tinyplot.

Value

No return value, called for side effect of producing a plot.

No return value; called for its side effect of producing a plot.

Examples

library("parttree")

#
## rpart trees

library("rpart")
rp = rpart(Kyphosis ~ Start + Age, data = kyphosis)

# A parttree object is just a data frame with additional attributes
(rp_pt = parttree(rp))
attr(rp_pt, "parttree")

# simple plot
plot(rp_pt)

# removing the (recursive) partition borders helps to emphasise overall fit
plot(rp_pt, border = NA)

# customize further by passing extra options to (tiny)plot
plot(
   rp_pt,
   border  = NA,                                     # no partition borders
   pch     = 16,                                     # filled points
   alpha   = 0.6,                                    # point transparency
   grid    = TRUE,                                   # background grid
   palette = "classic",                              # new colour palette
   xlab    = "Topmost vertebra operated on",         # custom x title
   ylab    = "Patient age (months)",                 # custom y title
   main    = "Tree predictions: Kyphosis recurrence" # custom title
)

#
## conditional inference trees from partyit

library("partykit")
ct = ctree(Species ~ Petal.Length + Petal.Width, data = iris)
ct_pt = parttree(ct)
plot(ct_pt, pch = 19, palette = "okabe", main = "ctree predictions: iris species")

## rpart via partykit
rp2 = as.party(rp)
parttree(rp2)

#
## various front-end frameworks are also supported, e.g.

# tidymodels

# install.packages("parsnip")
library(parsnip)

decision_tree() |>
  set_engine("rpart") |>
  set_mode("classification") |>
  fit(Species ~ Petal.Length + Petal.Width, data=iris) |>
  parttree() |>
  plot(main = "This time brought to you via parsnip...")

# mlr3 (NB: use `keep_model = TRUE` for mlr3 learners)

# install.packages("mlr3")
library(mlr3)

task_iris = TaskClassif$new("iris", iris, target = "Species")
task_iris$formula(rhs = "Petal.Length + Petal.Width")
fit_iris = lrn("classif.rpart", keep_model = TRUE) # NB!
fit_iris$train(task_iris)
plot(parttree(fit_iris), main = "... and now mlr3")

parttree documentation built on April 4, 2025, 2:47 a.m.