Network Comparison Test — nct • Nestimate

Tests whether two networks estimated from independent samples differ at three levels: global strength (M-statistic), network structure (S-statistic, max absolute edge difference), and individual edges (E-statistic per edge). Inference is via permutation of group labels.

Usage

nct(
  data1,
  data2,
  iter = 1000L,
  gamma = 0.5,
  paired = FALSE,
  abs = TRUE,
  weighted = TRUE,
  p_adjust = "none"
)

Arguments

data1: A numeric matrix or data.frame of observations from group 1.
data2: A numeric matrix or data.frame of observations from group 2. Same number of columns as data1.
iter: Integer. Number of permutation iterations. Default 1000.
gamma: EBIC tuning parameter for glasso. Default 0.5.
paired: Logical. If TRUE, perform a paired permutation (within-subject swap). Default FALSE.
abs: Logical. If TRUE, compute global strength on absolute edge weights. Default TRUE.
weighted: Logical. If TRUE, use weighted networks for the tests. If FALSE, binarize before computing statistics. Default TRUE.
p_adjust: P-value adjustment method for the per-edge tests (any method in stats::p.adjust.methods). Default "none".

Value

A list of class net_nct with elements:

nw1, nw2: Estimated weighted adjacency matrices.
M: List with observed, perm, p_value for the global strength test.
S: Same structure for the maximum absolute edge difference.
E: Same structure for per-edge tests.
n_iter: Number of permutations.
paired: Whether a paired test was used.

Details

Implementation matches NetworkComparisonTest::NCT() with defaults abs = TRUE, weighted = TRUE, paired = FALSE at machine precision when the same seed is used. The network estimator is EBIC-selected glasso applied to a Pearson correlation matrix, with Matrix::nearPD symmetrization (matching NCT's NCT_estimator_GGM default).

Examples

if (FALSE) { # \dontrun{
set.seed(1)
x1 <- matrix(rnorm(200 * 5), 200, 5)
x2 <- matrix(rnorm(200 * 5), 200, 5)
colnames(x1) <- colnames(x2) <- paste0("V", 1:5)
res <- nct(x1, x2, iter = 100)
res$M$p_value
res$S$p_value
} # }