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orf: ordered random forests

Introduction

The R package orf is an implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019). The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the orf package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the ranger package (Wright & Ziegler, 2017).

Installation

In order to install the latest CRAN released version use:

install.packages("orf", dependencies = c("Imports", "Suggests"))

to make sure all the needed packages are installed as well. Note that if you install the package directly from the source a C++ compiler is required. For Windows users Rtools collection is required too.

Examples

The examples below demonstrate the basic functionality of the orf package.

## Ordered Forest
require(orf)

# load example data
data(odata)

# specify response and covariates
Y <- as.numeric(odata[, 1])
X <- as.matrix(odata[, -1])

# estimate Ordered Forest with default settings
orf_fit <- orf(X, Y, num.trees = 1000, mtry = 2, min.node.size = 5,
                     replace = FALSE, sample.fraction = 0.5,
                     honesty = TRUE, honesty.fraction = 0.5,
                     inference = FALSE, importance = FALSE)

# print output of the Ordered Forest estimation
print(orf_fit)

# show summary of the Ordered Forest estimation
summary(orf_fit, latex = FALSE)

# plot the estimated probability distributions
plot(orf_fit)

# predict with the estimated Ordered Forest
predict(orf_fit, newdata = NULL, type = "probs", inference = FALSE)

# estimate marginal effects of the Ordered Forest
margins(orf_fit, newdata = NULL, eval = "mean", window = 0.1, inference = FALSE)

For a more detailed examples see the package vignette.

References

• Lechner, M., & Okasa, G. (2019). Random Forest Estimation of the Ordered Choice Model. arXiv preprint arXiv:1907.02436. https://arxiv.org/abs/1907.02436
• Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 77:1-17. https://doi.org/10.18637/jss.v077.i01

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.