https://github.com/okasag/orf

orf: R package
https://github.com/okasag/orf

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orf: R package

• Host: GitHub
• URL: https://github.com/okasag/orf
• Owner: okasag
• Created: 2019-02-02T16:26:08.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-07-26T17:13:36.000Z (over 2 years ago)
• Last Synced: 2024-11-28T17:16:05.760Z (14 days ago)
• Language: R
• Size: 452 KB
• Stars: 12
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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• jimsghstars - okasag/orf - orf: R package (R)

README

        
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# orf: ordered random forests

Welcome to the repository of the `R` package `orf` for random forest estimation of

the ordered choice models.

## 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:

```r

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.

```r

## 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. 

- 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.