Local linear forest tuning
Finds the optimal ridge penalty for local linear causal prediction.
tune_ll_causal_forest( forest, linear.correction.variables = NULL, ll.weight.penalty = FALSE, num.threads = NULL, lambda.path = NULL )
forest |
The forest used for prediction. |
linear.correction.variables |
Variables to use for local linear prediction. If left null, all variables are used. Default is NULL. |
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. |
lambda.path |
Optional list of lambdas to use for cross-validation. |
A list of lambdas tried, corresponding errors, and optimal ridge penalty lambda.
# Find the optimal tuning parameters.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
forest <- causal_forest(X, Y, W)
tuned.lambda <- tune_ll_causal_forest(forest)
# Use this parameter to predict from a local linear causal forest.
predictions <- predict(forest, linear.correction.variables = 1:p,
ll.lambda = tuned.lambda$lambda.min)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.