Ordered Random Forests
check if Y is discrete
check evaluation for margins
check honesty
check honesty fraction
check importance
check inference
check latex
check min.node.size
check mtry
check newdata
check num.trees
check replace
check sample.fraction
check prediction type for predict.orf
check window size
check X data
check Y data
Get Forest Weights
Get Honest Predictions
Get honest predictions (C++)
Get ORF Variance
Get honest weights (C++)
honest sample split
Marginal Effects for the Ordered Forest
Marginal Effects
Formatted output for marginal effects with inference
Formatted latex output for marginal effects with inference
Mean Squared Error
orf: Ordered Random Forests
Ordered Forest Estimator
Plot of the Ordered Forest
Predict honest predictions (C++)
Predict ORF Variance
Predict honest weights (C++)
Prediction of the Ordered Forest
ORF Predictions for Marginal Effects
Predict Forest Weights
ORF Weight Predictions for Marginal Effects
Predict Honest Predictions
Print of the Ordered Forest Marginal Effects
Print of the Ordered Forest Prediction
Print of the Ordered Forest
Ranked Probability Score
Summary of the Ordered Forest Marginal Effects
Summary of the Ordered Forest Prediction
Summary of the Ordered Forest
An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) <arXiv:1907.02436>. 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) <arXiv:1508.04409>.