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
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).
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
Rtools collection is required too.
The examples below demonstrate the basic functionality of the
## 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, newdata = NULL, eval = "mean", window = 0.1, inference = FALSE)
For a more detailed examples see the package vignette.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.