permutation: Permutation Test for Network Comparison

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

Permutation Test for Network Comparison

Description

Compares two networks estimated by build_network using a permutation test. Works with all built-in methods (transition and association) as well as custom registered estimators. The test shuffles which observations belong to which group, re-estimates networks, and tests whether observed edge-wise differences exceed chance.

For transition methods ("relative", "frequency", "co_occurrence"), uses a fast pre-computation strategy: per-sequence count matrices are computed once, and each permutation iteration only shuffles group labels and computes group-wise colSums.

For association methods ("cor", "pcor", "glasso", and custom estimators), the full estimator is called on each permuted group split.

Usage

permutation(
  x,
  y = NULL,
  iter = 1000L,
  alpha = 0.05,
  paired = FALSE,
  adjust = "none",
  nlambda = 50L,
  seed = NULL
)

Arguments

x	A netobject (from build_network).
y	A netobject (from build_network). Must use the same method and have the same nodes as x.
iter	Integer. Number of permutation iterations (default: 1000).
alpha	Numeric. Significance level (default: 0.05).
paired	Logical. If TRUE, permute within pairs (requires equal number of observations in x and y). Default: FALSE.
adjust	Character. p-value adjustment method passed to p.adjust (default: "none"). Common choices: "holm", "BH", "bonferroni".
nlambda	Integer. Number of lambda values for the glassopath regularisation path (only used when method = "glasso"). Higher values give finer lambda resolution at the cost of speed. Default: 50.
seed	Integer or NULL. RNG seed for reproducibility.

Value

An object of class "net_permutation" containing:

x

The first netobject.

y

The second netobject.

diff

Observed difference matrix (x - y).

diff_sig

Observed difference where p < alpha, else 0.

p_values

P-value matrix (adjusted if adjust != "none").

effect_size

Effect size matrix (observed diff / SD of permutation diffs).

summary

Long-format data frame of edge-level results.

method

The network estimation method.

iter

Number of permutation iterations.

alpha

Significance level used.

paired

Whether paired permutation was used.

adjust

p-value adjustment method used.

See Also

build_network, bootstrap_network, print.net_permutation, summary.net_permutation

Examples

s1 <- data.frame(V1 = c("A","B","C"), V2 = c("B","C","A"))
s2 <- data.frame(V1 = c("A","C","B"), V2 = c("C","B","A"))
n1 <- build_network(s1, method = "relative")
n2 <- build_network(s2, method = "relative")
perm <- permutation(n1, n2, iter = 10)

set.seed(1)
d1 <- data.frame(V1 = sample(LETTERS[1:4], 20, TRUE),
                 V2 = sample(LETTERS[1:4], 20, TRUE),
                 V3 = sample(LETTERS[1:4], 20, TRUE))
d2 <- data.frame(V1 = sample(LETTERS[1:4], 20, TRUE),
                 V2 = sample(LETTERS[1:4], 20, TRUE),
                 V3 = sample(LETTERS[1:4], 20, TRUE))
net1 <- build_network(d1, method = "relative")
net2 <- build_network(d2, method = "relative")
perm <- permutation(net1, net2, iter = 100, seed = 42)
print(perm)
summary(perm)

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