Compute Leave-One-Out (LOO) predictions and parameter changes for a NeRF+ model

This function computes leave-one-out (LOO) predictions and parameter changes for a fitted NeRF+ model. For each sample, it computes the model's predictions and parameter estimates when that sample is left out of the training data. This is useful for assessing model stability and identifying influential observations.

Usage

get_loo(
  object,
  x,
  x_embed = NULL,
  y,
  A = NULL,
  xtest = NULL,
  xtest_embed = NULL,
  ytest = NULL,
  A_full = NULL,
  nodeids = NULL,
  nodeids_test = NULL,
  metric = NULL,
  return_all = FALSE
)

Arguments

object

A fitted NeRF+ model object.

x

A data frame or matrix containing the training data.

x_embed

An optional data frame or matrix of network embeddings corresponding to the training samples. Only needed if training embeddings were manually inputted.

y

A vector of responses for the training data.

A

An adjacency matrix representing the network structure for the training nodes.

xtest

An optional data frame or matrix containing the test data.

xtest_embed

An optional data frame or matrix of network embeddings corresponding to the test samples.

ytest

An optional vector of responses for the test data.

A_full

An optional adjacency matrix representing the network structure for the full set of nodes (training + testing nodes in that order).

nodeids

An optional vector of node IDs of length n. If provided, node IDs indicate the rows of A corresponding to each sample. If not provided, the rows of A are assumed to be in the same order as the rows of x and y.

nodeids_test

An optional vector of node IDs for the test data.

metric

An optional function to compute the metric used for evaluating prediction changes. If NULL, defaults to RMSE for linear models and AUROC for logistic models.

return_all

Logical indicating whether to return all intermediate results for each tree in the forest. Defaults to FALSE.

Value

A list containing:

loo_params

A list of LOO parameter estimates for each tree.

change_alphas

Matrix of changes in alpha parameters when each sample is left out.

mean_change_alphas

Mean change in alpha parameters across all trees.

mean_change_betas

Mean change in beta parameters across all trees.

loo_preds

Matrix of LOO predictions, where each column corresponds to predictions when a different sample is left out.

loo_preds_test

Matrix of LOO predictions on test data (if provided).

mean_change_error_test

Mean change in prediction error on test data across all trees (if provided).

mean_change_preds_test

Mean change in predictions on test data across all trees (if provided).

Examples

# \donttest{
data(example_data)
nerfplus_out <- nerfplus(
  x = example_data$x, y = example_data$y, A = example_data$A,
  lambda_netcoh = 1,
  lambda_embed = 0.1,
  lambda_raw = 2,
  lambda_stump = 3,
  family = "linear", embedding = "laplacian", sample_split = "none"
)
loo_out <- get_loo(
  nerfplus_out,
  x = example_data$x, y = example_data$y, A = example_data$A,
  xtest = example_data$xtest, ytest = example_data$ytest,
  A_full = example_data$A_full
)
# }