Predict with a local linear forest
Gets estimates of E[Y|X=x] using a trained regression forest.
## 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, ... )
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 |
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). |
A vector of predictions.
# 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)
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