nct: Network Comparison Test

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Network Comparison Test

Description

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".

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).

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.

Examples

## Not run: 
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

## End(Not run)

Nestimate documentation built on April 20, 2026, 5:06 p.m.