R: Predict with a local linear forest

predict.ll_regression_forest {grf} | R Documentation

## Predict with a local linear forest

### Description

Gets estimates of E[Y|X=x] using a trained regression forest.

### Usage

    ## S3 method for class 'll_regression_forest'
    predict(
      object,
      newdata = NULL,
      linear.correction.variables = NULL,
      ll.lambda = NULL,
      ll.weight.penalty = FALSE,
      num.threads = NULL,
      estimate.variance = FALSE,
      ...
    )

### Arguments

`object` | The trained forest.
`newdata` | Points at which predictions should be made. If NULL, makes out-of-bag predictions on the training set instead (i.e., provides predictions at Xi using only trees that did not use the i-th training example). Note that this matrix should have the number of columns as the training matrix, and that the columns must appear in the same order.
`linear.correction.variables` | Optional subset of indexes for variables to be used in local linear prediction. If left NULL, all variables are used. We run a locally weighted linear regression on the included variables. Please note that this is a beta feature still in development, and may slow down prediction considerably. Defaults to NULL.
`ll.lambda` | Ridge penalty for local linear predictions. Defaults to NULL and will be cross-validated.
`ll.weight.penalty` | Option to standardize ridge penalty by covariance (TRUE), or penalize all covariates equally (FALSE). Defaults to FALSE.
`num.threads` | Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.
`estimate.variance` | Whether variance estimates for hattau(x) are desired (for confidence intervals).
`...` | Additional arguments (currently ignored).

### Value

A vector of predictions.

### Examples

    # Train the forest.
    n <- 50
    p <- 5
    X <- matrix(rnorm(n * p), n, p)
    Y <- X[, 1] * rnorm(n)
    forest <- ll_regression_forest(X, Y)

    # Predict using the forest.
    X.test <- matrix(0, 101, p)
    X.test[, 1] <- seq(-2, 2, length.out = 101)
    predictions <- predict(forest, X.test)

    # Predict on out-of-bag training samples.
    predictions.oob <- predict(forest)

[Package grf version 2.1.0 Index]