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ocf (version 1.0.3)

predict.ocf: Prediction Method for ocf Objects

Description

Prediction method for class ocf.

Usage

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

Value

Desired predictions.

Arguments

object

An ocf object.

data

Data set of class data.frame. It must contain at least the same covariates used to train the forests. If data is NULL, then object$full_data is used.

type

Type of prediction. Either "response" or "terminalNodes".

...

Further arguments passed to or from other methods.

Author

Riccardo Di Francesco

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.

References

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

See Also

ocf, marginal_effects

Examples

Run this code
## 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.

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