morf-short-tutorial.R
In morf: Modified Ordered Random Forest

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(morf)

## ----data-generation----------------------------------------------------------
## Load data from orf package.
set.seed(1986)

library(orf)
data(odata)

y <- as.numeric(odata[, 1])
X <- as.matrix(odata[, -1])

## ----adaptive-morf------------------------------------------------------------
## Training-test split.
train_idx <- sample(seq_len(length(y)), floor(length(y) * 0.5))

y_tr <- y[train_idx]
X_tr <- X[train_idx, ]

y_test <- y[-train_idx]
X_test <- X[-train_idx, ]

## Fit morf on training sample. Use default settings.
forests <- morf(y_tr, X_tr)

## Summary of data and tuning parameters.
summary(forests)

## Out-of-sample predictions.
predictions <- predict(forests, X_test)

head(predictions$probabilities)

table(y_test, predictions$classification)

## ----honest-morf--------------------------------------------------------------
## Honest forests.
honest_forests <- morf(y_tr, X_tr, honesty = TRUE)
honest_predictions <- predict(honest_forests, X_test)

## Compare predictions with adaptive fit.
cbind(head(predictions$probabilities), head(honest_predictions$probabilities))

## ----honest-morf-inference----------------------------------------------------
## Compute standard errors.
honest_forests <- morf(y_tr, X_tr, honesty = TRUE, inference = TRUE, n.threads = 0) # Zero corresponds to the number of CPUs available.
head(honest_forests$predictions$standard.errors)

## ----adaptive-me--------------------------------------------------------------
## Fit morf on training sample. Use large number of trees.
forests <- morf(y_tr, X_tr, n.trees = 4000)

## Marginal effects at the mean on test sample.
me_atmean <- marginal_effects(forests, data = X_test, eval = "atmean")
summary(me_atmean)

## ----honest-me----------------------------------------------------------------
##Honest forests.
honest_forests <- morf(y_tr, X_tr, n.trees = 4000, honesty = TRUE) # Notice we do not need inference here!

## Compute standard errors.
honest_me_atmean <- marginal_effects(honest_forests, data = X_test , eval = "atmean", inference = TRUE)
honest_me_atmean$standard.errors

honest_me_atmean$p.values # These are not corrected for multiple hypotheses testing!

## LATEX.
print(honest_me_atmean, latex = TRUE)