bootstrap_network: Bootstrap a Network Estimate

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

Bootstrap a Network Estimate

Description

Non-parametric bootstrap for any network estimated by build_network. Works with all built-in methods (transition and association) as well as custom registered estimators.

For transition methods ("relative", "frequency", "co_occurrence"), uses a fast pre-computation strategy: per-sequence count matrices are computed once, and each bootstrap iteration only resamples sequences via colSums (C-level) plus lightweight post-processing. Data must be in wide format for transition bootstrap; use convert_sequence_format to convert long-format data first.

For association methods ("cor", "pcor", "glasso", and custom estimators), the full estimator is called on resampled rows each iteration.

If a transition network contains only one sequence, the function warns that such a network is not recommended for bootstrap or other confirmatory testing.

Usage

bootstrap_network(
  x,
  iter = 1000L,
  ci_level = 0.05,
  inference = "stability",
  consistency_range = c(0.75, 1.25),
  edge_threshold = NULL,
  seed = NULL,
  boundary = c("inclusive", "strict"),
  ci_method = c("percentile", "basic")
)

Arguments

x	A netobject from build_network. The data, method, params, scaling, threshold, and level are all extracted from this object.
iter	Integer. Number of bootstrap iterations (default: 1000).
ci_level	Numeric. Significance level for CIs and p-values (default: 0.05).
inference	Character. "stability" (default) tests whether bootstrap replicates fall within a multiplicative consistency range around the original weight. "threshold" tests whether replicates exceed a fixed edge threshold.
consistency_range	Numeric vector of length 2. Multiplicative bounds for stability inference (default: c(0.75, 1.25)).
edge_threshold	Numeric or NULL. Fixed threshold for inference = "threshold". If NULL, defaults to the 10th percentile of absolute original edge weights.
seed	Integer or NULL. RNG seed for reproducibility.
boundary	Character. Comparison rule when computing the consistency-range p-value. "inclusive" (default, tna-compatible) counts iterations that meet the bound (\le / \ge); "strict" counts only iterations strictly outside (< / >).
ci_method	Character. Method for the edge-weight confidence intervals. "percentile" (default) uses the empirical bootstrap quantiles (Efron). "basic" reflects those quantiles around the observed weight, (2\hat{\theta} - q_{1-\alpha/2}, 2\hat{\theta} - q_{\alpha/2}) (Davison & Hinkley 1997, eq. 5.6), which corrects first-order bootstrap bias but can produce bounds outside the natural weight range near boundaries (e.g., below 0 for transition probabilities close to 0).

Value

An object of class "net_bootstrap" containing:

original

The original netobject.

mean

Bootstrap mean weight matrix.

sd

Bootstrap SD matrix.

p_values

P-value matrix.

significant

Original weights where p < ci_level, else 0.

ci_lower

Lower CI bound matrix.

ci_upper

Upper CI bound matrix.

cr_lower

Consistency range lower bound (stability only).

cr_upper

Consistency range upper bound (stability only).

summary

Long-format data frame of edge-level statistics.

model

Pruned netobject (non-significant edges zeroed).

method, params, iter, ci_level, inference

Bootstrap config.

consistency_range, edge_threshold

Inference parameters.

See Also

certainty for the closed-form Bayesian counterpart (same result layout, no resampling); build_network, print.net_bootstrap, summary.net_bootstrap

Examples

net <- build_network(data.frame(V1 = c("A","B","C"), V2 = c("B","C","A")),
  method = "relative")
boot <- bootstrap_network(net, iter = 10)

seqs <- data.frame(
  V1 = sample(LETTERS[1:4], 30, TRUE), V2 = sample(LETTERS[1:4], 30, TRUE),
  V3 = sample(LETTERS[1:4], 30, TRUE), V4 = sample(LETTERS[1:4], 30, TRUE)
)
net <- build_network(seqs, method = "relative")
boot <- bootstrap_network(net, iter = 100)
print(boot)
summary(boot)

Nestimate documentation built on July 11, 2026, 1:09 a.m.