boot_glasso: Bootstrap for Regularized Partial Correlation Networks

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

Bootstrap for Regularized Partial Correlation Networks

Description

Fast, single-call bootstrap for EBICglasso partial correlation networks. Combines nonparametric edge/centrality bootstrap, case-dropping stability analysis, edge/centrality difference tests, predictability CIs, and thresholded network into one function. Designed as a faster alternative to bootnet with richer output.

Usage

boot_glasso(
  x,
  iter = 1000L,
  cs_iter = 500L,
  cs_drop = seq(0.1, 0.9, by = 0.1),
  alpha = 0.05,
  gamma = 0.5,
  nlambda = 100L,
  centrality = c("strength", "expected_influence", "betweenness", "closeness"),
  centrality_fn = NULL,
  cor_method = "pearson",
  ncores = 1L,
  seed = NULL
)

Arguments

x	A data frame, numeric matrix (observations x variables), or a netobject with method = "glasso".
iter	Integer. Number of nonparametric bootstrap iterations (default: 1000).
cs_iter	Integer. Number of case-dropping iterations per drop proportion (default: 500).
cs_drop	Numeric vector. Drop proportions for CS-coefficient computation (default: seq(0.1, 0.9, by = 0.1)).
alpha	Numeric. Significance level for CIs (default: 0.05).
gamma	Numeric. EBIC hyperparameter (default: 0.5).
nlambda	Integer. Number of lambda values in the regularization path (default: 100).
centrality	Character vector. Centrality measures to compute. Built-in: "strength", "expected_influence", "betweenness", "closeness". Custom measures beyond these require centrality_fn. Default: c("strength", "expected_influence", "betweenness", "closeness").
centrality_fn	Optional function. A custom centrality function that takes a weight matrix and returns a named list of centrality vectors. When NULL (default), only "strength" and "expected_influence" are computed via rowSums/ colSums. When provided, the function is called as centrality_fn(mat) and should return a named list (e.g., list(closeness = ..., betweenness = ...)).
cor_method	Character. Correlation method: "pearson" (default), "spearman", or "kendall".
ncores	Integer. Number of parallel cores for mclapply (default: 1, sequential).
seed	Integer or NULL. RNG seed for reproducibility.

Value

An object of class "boot_glasso" containing:

original_pcor

Original partial correlation matrix.

original_precision

Original precision matrix.

original_centrality

Named list of original centrality vectors.

original_predictability

Named numeric vector of node R-squared.

edge_ci

Data frame of edge CIs (edge, weight, ci_lower, ci_upper, inclusion).

edge_inclusion

Named numeric vector of edge inclusion probabilities.

thresholded_pcor

Partial correlation matrix with non-significant edges zeroed.

centrality_ci

Named list of data frames (node, value, ci_lower, ci_upper) per centrality measure.

cs_coefficient

Named numeric vector of CS-coefficients per centrality measure.

cs_data

Data frame of case-dropping correlations (drop_prop, measure, correlation).

edge_diff_p

Symmetric matrix of pairwise edge difference p-values.

centrality_diff_p

Named list of symmetric p-value matrices per centrality measure.

predictability_ci

Data frame of node predictability CIs (node, r2, ci_lower, ci_upper).

boot_edges

iter x n_edges matrix of bootstrap edge weights.

boot_centrality

Named list of iter x p bootstrap centrality matrices.

boot_predictability

iter x p matrix of bootstrap R-squared.

nodes

Character vector of node names.

n

Sample size.

p

Number of variables.

iter

Number of nonparametric iterations.

cs_iter

Number of case-dropping iterations.

cs_drop

Drop proportions used.

alpha

Significance level.

gamma

EBIC hyperparameter.

nlambda

Lambda path length.

centrality_measures

Character vector of centrality measures.

cor_method

Correlation method.

lambda_path

Lambda sequence used.

lambda_selected

Selected lambda for original data.

timing

Named numeric vector with timing in seconds.

See Also

build_network, bootstrap_network

Examples

set.seed(1)
dat <- as.data.frame(matrix(rnorm(60), ncol = 3))
net <- build_network(dat, method = "glasso")
bg <- boot_glasso(net, iter = 10, cs_iter = 5, centrality = "strength")

set.seed(42)
mat <- matrix(rnorm(60), ncol = 4)
colnames(mat) <- LETTERS[1:4]
net <- build_network(as.data.frame(mat), method = "glasso")
boot <- boot_glasso(net, iter = 100, cs_iter = 50, seed = 42,
  centrality = c("strength", "expected_influence"))
print(boot)
summary(boot, type = "edges")

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