predict.ocf {ocf}

Prediction Method for ocf Objects

Description

Prediction method for class ocf.

Usage

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

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.

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

See Also

ocf, marginal_effects

Examples

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

library(orf)
data(odata)
odata <- odata[1:100, ] # Subset to reduce elapsed time.

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