Predict method for a NeRF+ model

Usage

# S3 method for class 'nerfplus'
predict(
  object,
  x,
  x_embed = NULL,
  A_full,
  nodeids = NULL,
  type = c("response", "alpha"),
  return_all = FALSE
)

Arguments

object: A fitted NeRF+ model object.
x: A data frame or matrix of new data for which predictions are to be made.
x_embed: Optional embedding data frame or matrix, whose rows are aligned with those in x. If provided, it will be used to augment the input x data. Only needed if training embeddings were manually inputted.
A_full: An adjacency matrix representing the network structure for the full set of nodes (training + testing nodes in that order). Note: if nrow(x) == nrow(A_full), then x is assumed to be the training data.
nodeids: (Optional) vector of node IDs of length equal to nrows in x. If provided, node IDs indicate the rows of A_full, corresponding to each sample. If not provided, the rows of A_full are assumed to be in the order of (x_train, x).
type: Type of prediction to return; one of "response" (default) or "alpha". If "response", the predicted values are returned. If "alpha", the estimated individual node effects are returned.
return_all: If TRUE, returns a list of predictions for each tree in the ensemble. If FALSE (default), returns the average prediction across all trees.

Value

If return_all = FALSE, this function returns a vector of predicted values (if type = "response") or estimated individual node effects (if type = "alpha"). If return_all = TRUE, this function returns a list of predicted values or estimated individual node effects, where each element in the list corresponds to a different tree in the ensemble.

Examples

data(example_data)
nerfplus_out <- nerfplus(
  x = example_data$x, y = example_data$y, A = example_data$A,
  lambda_netcoh = 1,
  lambda_embed = 0.1,
  lambda_raw = 2,
  lambda_stump = 3,
  family = "linear", embedding = "laplacian", sample_split = "none"
)
predicted_y <- predict(
  nerfplus_out, x = example_data$xtest, A_full = example_data$A_full,
  type = "response"
)
estimated_alphas <- predict(
  nerfplus_out, x = example_data$xtest, A_full = example_data$A_full,
  type = "alpha"
)