LDATree

LDATree is an R modeling package for fitting classification trees. If you are unfamiliar with classification trees, here is a tutorial about the traditional CART and its R implementation rpart.

Overview

Compared to other similar trees, LDATree sets itself apart in the following ways:

• It applies the idea of LDA (Linear Discriminant Analysis) when selecting variables, finding splits, and fitting models in terminal nodes.

• It addresses certain limitations of the R implementation of LDA (MASS::lda), such as handling missing values, dealing with more features than samples, and constant values within groups.

• Re-implement LDA using the Generalized Singular Value Decomposition (GSVD), LDATree offers quick response, particularly with large datasets.

• The package also includes several visualization tools to provide deeper insights into the data.

Installation

install.packages("LDATree")

Usage

To build an LDATree:

library(LDATree)
set.seed(456)
fit <- Treee(Species~., data = iris)

To plot the LDATree:

# View the overall tree.
plot(fit)

# Three types of individual plots
# 1. Scatter plot on first two LD scores
plot(fit, data = iris, node = 1)

# 2. Density plot on the first LD score
plot(fit, data = iris, node = 3)

# 3. A message
plot(fit, data = iris, node = 5)
#> [1] "Every observation in this node is predicted to be virginica"

To make predictions:

# Prediction only.
predictions <- predict(fit, iris)
head(predictions)
#> [1] "setosa" "setosa" "setosa" "setosa" "setosa" "setosa"

# A more informative prediction
predictions <- predict(fit, iris, type = "all")
head(predictions)
#>   response node setosa   versicolor virginica
#> 1   setosa    3      1 9.281826e-27         0
#> 2   setosa    3      1 3.107853e-22         0
#> 3   setosa    3      1 1.049363e-24         0
#> 4   setosa    3      1 9.134151e-22         0
#> 5   setosa    3      1 1.672418e-27         0
#> 6   setosa    3      1 1.808762e-24         0

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub