Check Argument alpha

Description

Check Argument alpha

Usage

check_alpha(alpha)

Arguments

Check Arguments honesty, honesty.fraction and inference

Description

Check Arguments honesty, honesty.fraction and inference

Usage

check_honesty_inference(honesty, honesty.fraction, inference)

Arguments

Check Argument max.depth

Description

Check Argument max.depth

Usage

check_maxdepth(max.depth)

Arguments

Check Argument min.node.size

Description

Check Argument min.node.size

Usage

check_minnodesize(min.node.size)

Arguments

Check Argument mtry

Description

Check Argument mtry

Usage

check_mtry(mtry, nv)

Arguments

Value

Appropriate value of mtry.

Check Argument n.trees

Description

Check Argument n.trees

Usage

check_ntrees(n.trees)

Arguments

Check Argument sample.fraction

Description

Check Argument sample.fraction

Usage

check_samplefraction(sample.fraction)

Arguments

Check Arguments x and y

Description

Check Arguments x and y

Usage

check_x_y(x, y)

Arguments

Honest Sample Split

Description

Randomly spits the sample into a training sample and an honest sample.

Usage

class_honest_split(data, honesty.fraction = 0.5)

Arguments

Details

class_honest_split looks for balanced splits, i.e., splits such as all the outcome's classes are represented in both the training and the honest sample. After 100 trials, the program throws an error.

Value

List with elements:

Forest In-Sample Honest Weights

Description

Computes forest in-sample honest weights for an ocf.forest object.

Usage

forest_weights_fitted(forest, honest_sample, train_sample)

Arguments

Details

forest must have been grown using only the training sample.

Value

Matrix of in-sample honest weights.

Forest In-Sample Honest Weights

Description

Computes forest in-sample honest weights for a ocf.forest object relative to the m-th class.

Usage

forest_weights_fitted_cpp(
  leaf_IDs_train_list,
  leaf_IDs_honest_list,
  leaf_size_honest_list
)

Arguments

Forest Out-of-Sample Honest Weights

Description

Computes forest out-of-sample honest weights for a ocf.forest object relative to the m-th class.

Usage

forest_weights_predicted_cpp(
  leaf_IDs_test_list,
  leaf_IDs_honest_list,
  leaf_size_honest_list,
  w
)

Arguments

Generate Ordered Data

Description

Generate a synthetic data set with an ordered non-numeric outcome, together with conditional probabilities and covariates' marginal effects.

Usage

generate_ordered_data(n)

Arguments

Details

First, a latent outcome is generated as follows:

Y_i^* = g ( X_i ) + \epsilon_i

with:

g ( X_i ) = X_i^T \beta

X_i := (X_{i, 1}, X_{i, 2}, X_{i, 3}, X_{i, 4}, X_{i, 5}, X_{i, 6})

X_{i, 1}, X_{i, 3}, X_{i, 5} \sim \mathcal{N} \left( 0, 1 \right)

X_{i, 2}, X_{i, 4}, X_{i, 6} \sim \textit{Bernoulli} \left( 0, 1 \right)

\beta = \left( 1, 1, 1/2, 1/2, 0, 0 \right)

\epsilon_i \sim logistic (0, 1)

Second, the observed outcomes are obtained by discretizing the latent outcome into three classes using uniformly spaced threshold parameters.

Third, the conditional probabilities and the covariates' marginal effects at the mean are generated using standard textbook formulas. Marginal effects are approximated using a sample of 1,000,000 observations.

Value

A list storing a data frame with the observed data, a matrix of true conditional probabilities, and a matrix of true marginal effects at the mean of the covariates.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(1000)

head(data$true_probs)
data$me_at_mean

sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)
  

Honest In-Sample Predictions

Description

Computes honest in-sample predictions for an ocf.forest object.

Usage

honest_fitted(forest, train_sample, honest_sample, y_m_honest, y_m_1_honest)

Arguments

Details

forest must have been grown using only the training sample. honest_fitted replaces the leaf estimates using the outcome from the honest sample (using the prediction method of ocf).

Value

In-sample honest predictions.

Honest In-Sample Predictions

Description

Computes honest in-sample predictions for a ocf.forest object relative to the desired class.

Usage

honest_fitted_cpp(
  unique_leaves_honest,
  y_m,
  y_m_1,
  honest_leaves,
  train_leaves
)

Arguments

Honest Out-of-Sample Predictions

Description

Computes honest out-of-sample predictions for an ocf.forest object.

Usage

honest_predictions(
  forest,
  honest_sample,
  test_sample,
  y_m_honest,
  y_m_1_honest
)

Arguments

Details

honest_predictions replaces the leaf estimates of forest using the outcome from the associated honest sample (using the prediction method of ocf). The honest sample must not have been used to build the trees.

Value

Out-of-sample honest predictions.

Honest Out-of-Sample Predictions

Description

Computes honest out-of-sample predictions for a ocf.forest object relative to the desired class.

Usage

honest_predictions_cpp(
  unique_leaves_honest,
  y_m,
  y_m_1,
  honest_leaves,
  test_leaves
)

Arguments

Marginal Effects for Ordered Correlation Forest

Description

Nonparametric estimation of marginal effects using an ocf object.

Usage

marginal_effects(
  object,
  data = NULL,
  these_covariates = NULL,
  eval = "atmean",
  bandwitdh = 0.1,
  inference = FALSE
)

Arguments

Details

marginal_effects can estimate mean marginal effects, marginal effects at the mean, or marginal effects at the median, according to the eval argument.

If these_covariates is NULL (the default), the routine assumes that covariates with with at most ten unique values are categorical and treats the remaining covariates as continuous.

Value

Object of class ocf.marginal.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")

print(me)
print(me, latex = TRUE)
plot(me)

## Compute standard errors. This requires honest forests.
honest_forests <- ocf(Y, X, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)

print(honest_me, latex = TRUE)
plot(honest_me)

## Subset covariates and select covariates' types.
my_covariates <- list("x1" = "continuous", "x2" = "discrete", "x4" = "discrete")
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE,
                              these_covariates = my_covariates)
print(honest_me)
plot(honest_me)

Accuracy Measures for Ordered Probability Predictions

Description

Accuracy measures for evaluating ordered probability predictions.

Usage

mean_squared_error(y, predictions, use.true = FALSE)

mean_absolute_error(y, predictions, use.true = FALSE)

mean_ranked_score(y, predictions, use.true = FALSE)

classification_error(y, predictions)

Arguments

Details

MSE, MAE, and RPS

When calling one of mean_squared_error, mean_absolute_error, or mean_ranked_score, predictions must be a matrix of predicted class probabilities, with as many rows as observations in y and as many columns as classes of y.

If use.true == FALSE, the mean squared error (MSE), the mean absolute error (MAE), and the mean ranked probability score (RPS) are computed as follows:

MSE = \frac{1}{n} \sum_{i = 1}^n \sum_{m = 1}^M (1 (Y_i = m) - \hat{p}_m (x))^2

MAE = \frac{1}{n} \sum_{i = 1}^n \sum_{m = 1}^M |1 (Y_i = m) - \hat{p}_m (x)|

RPS = \frac{1}{n} \sum_{i = 1}^n \frac{1}{M - 1} \sum_{m = 1}^M (1 (Y_i \leq m) - \hat{p}_m^* (x))^2

If use.true == TRUE, the MSE, the MAE, and the RPS are computed as follows (useful for simulation studies):

MSE = \frac{1}{n} \sum_{i = 1}^n \sum_{m = 1}^M (p_m (x) - \hat{p}_m (x))^2

MSE = \frac{1}{n} \sum_{i = 1}^n \sum_{m = 1}^M |p_m (x) - \hat{p}_m (x)|

RPS = \frac{1}{n} \sum_{i = 1}^n \frac{1}{M - 1} \sum_{m = 1}^M (p_m^* (x) - \hat{p}_m^* (x))^2

where:

p_m (x) = P(Y_i = m | X_i = x)

p_m^* (x) = P(Y_i \leq m | X_i = x)

Classification error

When calling classification_error, predictions must be a vector of predicted class labels.

Classification error (CE) is computed as follows:

CE = \frac{1}{n} \sum_{i = 1}^n 1 (Y_i \neq \hat{Y}_i)

where Y_i are the observed class labels.

Value

The MSE, the MAE, the RPS, or the CE of the method.

Author(s)

Riccardo Di Francesco

See Also

mean_ranked_score

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## 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 ocf on training sample.
forests <- ocf(Y_tr, X_tr)

## Accuracy measures on test sample.
predictions <- predict(forests, X_test)

mean_squared_error(Y_test, predictions$probabilities)
mean_ranked_score(Y_test, predictions$probabilities)
classification_error(Y_test, predictions$classification)

Multinomial Machine Learning

Description

Estimation strategy to estimate conditional choice probabilities for ordered non-numeric outcomes.

Usage

multinomial_ml(Y = NULL, X = NULL, learner = "forest", scale = TRUE)

Arguments

Details

Multinomial machine learning expresses conditional choice probabilities as expectations of binary variables:

p_m \left( X_i \right) = \mathbb{E} \left[ 1 \left( Y_i = m \right) | X_i \right]

This allows us to estimate each expectation separately using any regression algorithm to get an estimate of conditional probabilities.

multinomial_ml combines this strategy with either regression forests or penalized logistic regressions with an L1 penalty, according to the user-specified parameter learner.

If learner == "l1", the penalty parameters are chosen via 10-fold cross-validation and model.matrix is used to handle non-numeric covariates. Additionally, if scale == TRUE, the covariates are scaled to have zero mean and unit variance.

Value

Object of class mml.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ordered_ml, ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## 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 multinomial machine learning on training sample using two different learners.
multinomial_forest <- multinomial_ml(Y_tr, X_tr, learner = "forest")
multinomial_l1 <- multinomial_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(multinomial_forest, X_test)
predictions_l1 <- predict(multinomial_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

Ordered Correlation Forest

Description

Nonparametric estimator for ordered non-numeric outcomes. The estimator modifies a standard random forest splitting criterion to build a collection of forests, each estimating the conditional probability of a single class.

Usage

ocf(
  Y = NULL,
  X = NULL,
  honesty = FALSE,
  honesty.fraction = 0.5,
  inference = FALSE,
  alpha = 0.2,
  n.trees = 2000,
  mtry = ceiling(sqrt(ncol(X))),
  min.node.size = 5,
  max.depth = 0,
  replace = FALSE,
  sample.fraction = ifelse(replace, 1, 0.5),
  n.threads = 1
)

Arguments

Value

Object of class ocf.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

marginal_effects

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## 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 ocf on training sample.
forests <- ocf(Y_tr, X_tr)

## We have compatibility with generic S3-methods.
print(forests)
summary(forests)
predictions <- predict(forests, X_test)
head(predictions$probabilities)
table(Y_test, predictions$classification)

## Compute standard errors. This requires honest forests.
honest_forests <- ocf(Y_tr, X_tr, honesty = TRUE, inference = TRUE)
head(honest_forests$predictions$standard.errors)

## Marginal effects.
me <- marginal_effects(forests, eval = "atmean")
print(me)
print(me, latex = TRUE)
plot(me)

## Compute standard errors. This requires honest forests.
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
print(honest_me, latex = TRUE)
plot(honest_me)

Ordered Machine Learning

Description

Estimation strategy to estimate conditional choice probabilities for ordered non-numeric outcomes.

Usage

ordered_ml(Y = NULL, X = NULL, learner = "forest", scale = TRUE)

Arguments

Details

Ordered machine learning expresses conditional choice probabilities as the difference between the cumulative probabilities of two adjacent classes, which in turn can be expressed as conditional expectations of binary variables:

p_m \left( X_i \right) = \mathbb{E} \left[ 1 \left( Y_i \leq m \right) | X_i \right] - \mathbb{E} \left[ 1 \left( Y_i \leq m - 1 \right) | X_i \right]

Then we can separately estimate each expectation using any regression algorithm and pick the difference between the m-th and the (m-1)-th estimated surfaces to estimate conditional probabilities.

ordered_ml combines this strategy with either regression forests or penalized logistic regressions with an L1 penalty, according to the user-specified parameter learner.

If learner == "forest", then the orf function is called from an external package, as this estimator has already been proposed by Lechner and Okasa (2019).

If learner == "l1", the penalty parameters are chosen via 10-fold cross-validation and model.matrix is used to handle non-numeric covariates. Additionally, if scale == TRUE, the covariates are scaled to have zero mean and unit variance.

Value

Object of class oml.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

multinomial_ml, ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## 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 ordered machine learning on training sample using two different learners.
ordered_forest <- ordered_ml(Y_tr, X_tr, learner = "forest")
ordered_l1 <- ordered_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(ordered_forest, X_test)
predictions_l1 <- predict(ordered_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

Plot Method for ocf.marginal Objects

Description

Plots an ocf.marginal object.

Usage

## S3 method for class 'ocf.marginal'
plot(x, ...)

Arguments

Details

If standard errors have been estimated, 95% confidence intervals are shown.

Value

Plots an ocf.marginal object.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf, marginal_effects.

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")
plot(me)

## Add standard errors.
honest_forests <- ocf(Y, X, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
plot(honest_me)

Prediction Method for mml Objects

Description

Prediction method for class mml.

Usage

## S3 method for class 'mml'
predict(object, data = NULL, ...)

Arguments

Details

If object$learner == "l1", then model.matrix is used to handle non-numeric covariates. If we also have object$scaling == TRUE, then data is scaled to have zero mean and unit variance.

Value

Matrix of predictions.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

multinomial_ml, ordered_ml

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## 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 multinomial machine learning on training sample using two different learners.
multinomial_forest <- multinomial_ml(Y_tr, X_tr, learner = "forest")
multinomial_l1 <- multinomial_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(multinomial_forest, X_test)
predictions_l1 <- predict(multinomial_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

Prediction Method for ocf Objects

Description

Prediction method for class ocf.

Usage

## S3 method for class 'ocf'
predict(object, data = NULL, type = "response", ...)

Arguments

Details

If type == "response", the routine returns the predicted conditional class probabilities and the predicted class labels. If forests are honest, the predicted probabilities are honest.

If type == "terminalNodes", the IDs of the terminal node in each tree for each observation in data are returned.

Value

Desired predictions.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf, marginal_effects

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## 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 ocf on training sample.
forests <- ocf(Y_tr, X_tr)

## Predict on test sample.
predictions <- predict(forests, X_test)
head(predictions$probabilities)
predictions$classification

## Get terminal nodes.
predictions <- predict(forests, X_test, type = "terminalNodes")
predictions$forest.1[1:10, 1:20] # Rows are observations, columns are forests.

Prediction Method for ocf.forest Objects

Description

Prediction method for class ocf.forest.

Usage

## S3 method for class 'ocf.forest'
predict(object, data, type = "response", ...)

Arguments

Details

If type === "response" (the default), the predicted conditional class probabilities are returned. If forests are honest, these predictions are honest.

If type == "terminalNodes", the IDs of the terminal node in each tree for each observation in data are returned.

Value

Prediction results.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf, marginal_effects

Prediction Method for oml Objects

Description

Prediction method for class oml.

Usage

## S3 method for class 'oml'
predict(object, data = NULL, ...)

Arguments

Details

If object$learner == "l1", then model.matrix is used to handle non-numeric covariates. If we also have object$scaling == TRUE, then data is scaled to have zero mean and unit variance.

Value

Matrix of predictions.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

multinomial_ml, ordered_ml

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## 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 ordered machine learning on training sample using two different learners.
ordered_forest <- ordered_ml(Y_tr, X_tr, learner = "forest")
ordered_l1 <- ordered_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(ordered_forest, X_test)
predictions_l1 <- predict(ordered_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

Forest Out-of-Sample Weights

Description

Computes forest out-of-sample honest weights for an ocf.forest object.

Usage

predict_forest_weights(forest, honest_sample, test_sample)

Arguments

Details

forest must have been grown using only the training sample.

Value

Matrix of out-of-sample honest weights.

Print Method for ocf Objects

Description

Prints an ocf object.

Usage

## S3 method for class 'ocf'
print(x, ...)

Arguments

Value

Prints an ocf object.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Print.
print(forests)

Print Method for ocf.marginal Objects

Description

Prints an ocf.marginal object.

Usage

## S3 method for class 'ocf.marginal'
print(x, latex = FALSE, ...)

Arguments

Details

Compilation of the LATEX code requires the following packages: booktabs, float, adjustbox. If standard errors have been estimated, they are printed in parenthesis below each point estimate.

Value

Prints an ocf.marginal object.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf, marginal_effects.

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")
print(me)
print(me, latex = TRUE)

## Add standard errors.
honest_forests <- ocf(Y, X, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
print(honest_me, latex = TRUE)

Renaming Variables for LATEX Usage

Description

Renames variables where the character "_" is used, which causes clashes in LATEX. Useful for the phased print method.

Usage

rename_latex(names)

Arguments

Value

The renamed string vector. Strings where "_" is not found are not modified by rename_latex.

Summary Method for ocf Objects

Description

Summarizes an ocf object.

Usage

## S3 method for class 'ocf'
summary(object, ...)

Arguments

Value

Summarizes an ocf object.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf, marginal_effects

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Summary.
summary(forests)

Summary Method for ocf.marginal Objects

Description

Summarizes an ocf.marginal object.

Usage

## S3 method for class 'ocf.marginal'
summary(object, latex = FALSE, ...)

Arguments

Details

Compilation of the LATEX code requires the following packages: booktabs, float, adjustbox. If standard errors have been estimated, they are printed in parenthesis below each point estimate.

Value

Summarizes an ocf.marginal object.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf, marginal_effects.

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")
summary(me)
summary(me, latex = TRUE)

## Add standard errors.
honest_forests <- ocf(Y, X, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
summary(honest_me, latex = TRUE)

Tree Information in Readable Format

Description

Extracts tree information from a ocf.forest object.

Usage

tree_info(object, tree = 1)

Arguments

Details

Nodes and variables IDs are 0-indexed, i.e., node 0 is the root node.

All values smaller than or equal to splitval go to the left and all values larger go to the right.

Value

A data.frame with the following columns:

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.

See Also

ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(1000)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Extract information from tenth tree of first forest.
info <- tree_info(forests$forests.info$forest.1, tree = 10)
head(info)