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Bootstrap for Regularized Partial Correlation Networks

Source: R/boot_glasso.R
boot_glasso.Rd

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. All four built-in measures ("strength", "expected_influence", "betweenness", "closeness") are computed internally with no extra dependencies and are always taken from the built-in path even if centrality_fn is supplied. Names that are not one of these four are valid only when a centrality_fn is supplied; that function is then responsible for returning them. 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), all four built-in measures are computed internally: "strength"/"expected_influence" via rowSums, and "betweenness"/"closeness" via an internal Floyd-Warshall shortest-path routine. When provided, the function is called as centrality_fn(mat) and is used only for requested measures that are not one of the four built-ins; it should return a named list (e.g., list(my_metric = ...)).

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")
# \donttest{
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)
#> GLASSO Bootstrap (100 iterations, 50 case-drop per proportion)
#>   Data: 15 x 4  |  Alpha: 0.05  |  Gamma: 0.50
#>   Edges: 0/6 significant (CI excludes zero)
#>   Mean inclusion probability: 0.32
#> 
#>   Centrality Stability (CS-coefficient):
#>     strength:              0.00 [Unstable]
#>     expected_influence:    0.00 [Unstable]
#> 
#>   Edge differences: 1/15 pairs significantly different
#>   Timing: 5.7s (bootstrap: 3.4s, case-drop: 2.2s)
summary(boot, type = "edges")
#>     edge weight    ci_lower  ci_upper inclusion
#> 1 A -- B      0 -0.15265878 0.3016131      0.24
#> 2 A -- C      0 -0.41694566 0.0000000      0.45
#> 3 B -- C      0 -0.37041116 0.2982661      0.43
#> 4 A -- D      0  0.00000000 0.3870411      0.41
#> 5 B -- D      0 -0.19093175 0.2384451      0.21
#> 6 C -- D      0 -0.01732718 0.2188424      0.18
# }