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Compute feature importances for a NeRF+ model

get_feature_importances.Rd

This function computes global and local feature importances given a fitted NeRF+ model.

Usage

get_feature_importances(
  object,
  x,
  x_embed = NULL,
  y = NULL,
  A_full,
  nodeids = NULL,
  method = c("permute", "mdi+", "local"),
  B = 10,
  metric = NULL
)

Arguments

object

A fitted NeRF+ model object.

x

A data frame or matrix used to compute the feature importances.

x_embed

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

y

A vector of responses.

A_full

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

nodeids

(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.

method

A character string indicating the method to use for computing feature importances. Options are:

  • "permute": Permutation-based global feature importance.

  • "mdi+": MDI+ global feature importance.

  • "local": Local feature importance.

B

Number of samples for permutation-based feature importance. Ignored if method is not "permute".

metric

A function to compute the metric used for global feature importances. Defaults to R-squared for regression and AUROC for classification. Ignored if method is "local".

Value

If method is "permute" or "mdi+", a tibble with two columns:

  • var: The name of the feature.

  • importance: The computed feature importance score. If method is "local", an n x p

Examples

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