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Preprocessing helper functions prior to fitting the RF in NeRF+ models

nerfplus_preprocessing.Rd

These functions fit or apply preprocessing steps prior to fitting RF in NeRF+ models. Specifically, this function standardizes the numeric features to have mean 0 and SD 1 (if standardize = TRUE) and augments the data with (standardized) network embeddings (if embedding is specified).

Usage

fit_pre_rf_preprocessing(
  x,
  A = NULL,
  standardize = TRUE,
  embedding = NULL,
  embedding_options = list(ndim = 2, regularization = 0.5, varimax = FALSE, center =
    FALSE, scale = FALSE),
  nodeids = NULL
)

apply_pre_rf_preprocessing(
  preprocess_fit,
  x,
  x_embed = NULL,
  A_full,
  nodeids = NULL
)

Arguments

x

A numeric matrix or data frame of predictors (features); size n x p. Should be centered so that each column has mean 0.

A

An adjacency matrix representing the network structure.

standardize

Logical indicating whether to standardize numeric features in x to have mean 0 and SD 1. Defaults to TRUE.

embedding

Embedding type(s), at least one of "adjacency", "laplacian", score", or NULL (i.e., do not include any network embedding features). Alternatively, can directly input an n x d matrix of network embedding features corresponding to x.

embedding_options

A list of options for the network embedding. Ignored if embedding = NULL. If provided, the list should contain the following components:

  • ndim: Number of dimensions in the embedding (default is 2).

  • regularization: Regularization parameter for the adjacency matrix (default is 0.5).

  • varimax: Whether to apply varimax rotation to the embedding (default is FALSE).

  • center: Whether to center the embedding so that each column has mean 0 (default is TRUE).

  • scale: Whether to scale the embedding so that first embedding component column has SD 1 (default is TRUE). All other embedding components are scaled, proportional to their eigenvalues.

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.

preprocess_fit

Output of fit_pre_rf_preprocessing() to be applied to new data.

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).

Value

For fit_pre_rf_preprocessing(), a list containing the following components:

  • x: The preprocessed data frame

  • standardize_x: Logical indicating whether the raw data was standardized

  • x_center_factors: Named numeric vector of means used for standardization

  • x_scale_factors: Named numeric vector of standard deviations used for standardization

  • embedding: The method used for embedding

  • embedding_fit: The output of the network embedding fit

  • embed_center_factors: Named numeric vector of means used for standardizing the embeddings

  • embed_scale_factors: Named numeric vector of standard deviations used for standardizing the embeddings

  • nodeids: The node IDs provided for the embeddings.

For apply_pre_rf_preprocessing(), a data frame with the preprocessed data.