Classification and Regression with Structured and Mixed-Type Data

Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon 'partykit' to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) .

etree

The goal of etree is to provide a friendly implementation of Energy Trees, a model for classification and regression with structured and mixed-type data. The package currently cover functions and graphs as structured covariates.

Installation

You can install the development version of etree from GitHub with:

# install.packages("devtools")
devtools::install_github("ricgbl/etree")

Example

This is a basic example which shows how to fit an Energy Tree for regression using a toy dataset with four covariates of different types: numeric, nominal, functional, and in the form of graphs.

library(etree)

# Covariates
nobs <- 100
cov_num <- rnorm(nobs)
cov_nom <- factor(rbinom(nobs, size = 1, prob = 0.5))
cov_gph <- lapply(1:nobs, function(j) igraph::sample_gnp(100, 0.2))
cov_fun <- fda.usc::rproc2fdata(nobs, seq(0, 1, len = 100), sigma = 1)
cov_list <- list(cov_num, cov_nom, cov_gph, cov_fun)
 
# Response variable
resp_reg <- cov_num ^ 2

# Energy Tree fit
etree_fit <- etree(response = resp_reg, 
                   covariates = cov_list)
#> Warning: executing %dopar% sequentially: no parallel backend registered
#> Warning in .create_newcov(covariates = covariates, response = response, : No
#> names available for covariates. Numbers are used instead.

Additional and more complex examples can be found in the package’s vignettes.