null_moran: Moran spectral randomization null model

In neuromapr: Spatial Null Models and Transforms for Brain Map Comparison

Moran spectral randomization null model

Description

Generates spatially-constrained surrogate brain maps using Moran's eigenvector maps (MEMs) for spectral randomization.

Usage

null_moran(
  data,
  distmat,
  n_perm = 1000L,
  seed = NULL,
  procedure = c("pair", "singleton"),
  kernel = c("inverse_distance", "exponential", "gaussian", "bisquare"),
  tol = 1e-06
)

Arguments

data	Numeric vector of brain map values.
distmat	Distance matrix between parcels/vertices.
n_perm	Integer number of null permutations to generate.
seed	Optional integer seed for reproducibility.
procedure	Character, either "pair" (default, random 2D rotations of near-degenerate eigenvector pairs, matches neuromaps Python) or "singleton" (random sign flips of individual eigenvectors).
kernel	Weight matrix kernel: "inverse_distance" (default, matches neuromaps Python), "exponential", "gaussian", or "bisquare".
tol	Numeric tolerance for eigenvalue comparison.

Value

A null_distribution object.

References

Wagner HH, Dray S (2015) Methods in Ecology and Evolution 6:1169-1178. doi:10.1111/2041-210X.12407

Examples

data <- rnorm(50)
distmat <- as.matrix(dist(matrix(rnorm(100), 50, 2)))
nd <- null_moran(data, distmat, n_perm = 10L, seed = 1L)

neuromapr documentation built on Feb. 27, 2026, 5:08 p.m.