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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.
You can install the development version of etree from GitHub with:
# install.packages("devtools")
devtools::install_github("ricgbl/etree")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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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