orf (version 0.1.4)

orf-package: orf: Ordered Random Forests

Description

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).

Author

Gabriel Okasa, Michael Lechner

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

• Goller, D., Knaus, M. C., Lechner, M., & Okasa, G. (2021). Predicting Match Outcomes in Football by an Ordered Forest Estimator. A Modern Guide to Sports Economics. Edward Elgar Publishing, 335-355. tools:::Rd_expr_doi("10.4337/9781789906530.00026")

• 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. tools:::Rd_expr_doi("10.18637/jss.v077.i01").

Examples

## 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)

# print output of the orf estimation
print(orf_fit)

# show summary of the orf estimation
summary(orf_fit)

# plot the estimated probability distributions
plot(orf_fit)

# predict with the estimated orf
predict(orf_fit)
# \donttest{
# estimate marginal effects of the orf
margins(orf_fit)
# }