knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
Visualize simple 2-D decision tree partitions in R. The parttree package is optimised to work with ggplot2, although it can be used to visualize tree partitions with base R graphics too.
This package is not yet on CRAN, but can be installed from GitHub with:
# install.packages("remotes") remotes::install_github("grantmcdermott/parttree")
The parttree homepage
includes an introductory vignette and detailed documentation. But here's a
quickstart example using the
dataset that comes bundled with the rpart package. In this case, we are
interested in predicting kyphosis recovery after spinal surgery, as a function
of 1) the number of topmost vertebra that were operated, and 2) patient age.
The key visualization layer below---provided by this package---is
library(rpart) # For the dataset and fitting decisions trees library(parttree) # This package (will automatically load ggplot2 too) fit = rpart(Kyphosis ~ Start + Age, data = kyphosis) ggplot(kyphosis, aes(x = Start, y = Age)) + geom_parttree(data = fit, alpha = 0.1, aes(fill = Kyphosis)) + # <-- key layer geom_point(aes(col = Kyphosis)) + labs( x = "No. of topmost vertebra operated on", y = "Patient age (months)", caption = "Note: Points denote observations. Shading denotes model predictions." ) + theme_minimal()
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