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Bootstrap a psychometric network

Source: R/bootstrap.R
net_boot.Rd

Resamples observations with replacement, re-estimates the network on each resample, and summarizes the sampling distribution of every edge weight and node centrality (mean, percentile confidence interval, and edge inclusion proportion). An edge is flagged significant when its percentile interval excludes zero. The raw per-resample draws are stored on the returned object for use by difference_test().

Usage

net_boot(
  data,
  method = "glasso",
  n_boot = 1000L,
  ci = 0.95,
  measures = c("strength", "expected_influence"),
  centrality_fn = NULL,
  predictability = FALSE,
  threshold = FALSE,
  diff_test = FALSE,
  p_adjust = "none",
  labels = NULL,
  cores = NULL,
  engine = NULL,
  ...
)

Arguments

data

Numeric data frame or matrix (rows = observations).

method

Estimator (see psychnet()). Default "glasso".

n_boot

Number of bootstrap resamples. Default 1000.

ci

Confidence level for percentile intervals. Default 0.95.

measures

Centrality measures to bootstrap. Defaults to the two recommended for psychometric networks ("strength", "expected_influence"); "betweenness"/"closeness" and custom measures (via centrality_fn) are also accepted. See net_centralities().

centrality_fn

Optional function supplying any non-built-in measures (see net_centralities()).

predictability

Logical; if TRUE and the estimator returns a precision matrix (GGM family), bootstrap node predictability (R^2) and report its interval. Default FALSE.

threshold

Logical; if TRUE, also return the observed network with every edge whose bootstrap interval includes zero set to zero ($thresholded). Default FALSE.

diff_test

Logical; if TRUE, also return two-sided bootstrap difference p-value matrices for edges ($edge_diff_p, NULL past 500 edges) and for each centrality measure ($centrality_diff_p). Default FALSE.

p_adjust

Multiple-comparison adjustment applied to the difference p-value matrices (any stats::p.adjust method). Default "none".

labels

Optional node labels.

cores

Number of CPU cores for the resample loop. NULL (default) uses two thirds of the detected cores; 1 forces a serial run. Parallelism uses forking (parallel::mclapply) and falls back to serial on Windows. Because every resample index is drawn in the parent process before any fitting, the result is identical for any number of cores and reproducible from set.seed().

engine

Optional estimator engine forwarded to each resample fit (e.g. "base"/"glasso" for glasso, "base"/"glmnet" for ising/mgm). NULL (default) uses the estimator's own default.

...

Passed to the estimator.

Value

An object of class psychnet_bootstrap: tidy $edges (with a significant flag) and $centrality data frames, the observed network in $observed, raw resample draws in $edge_boot, $str_boot, $ei_boot, and the general $centrality_boot (named list, one matrix per measure). Optional $predictability, $thresholded, $edge_diff_p, $centrality_diff_p, plus $lambda_path/$lambda_selected when the estimator reports them.

Examples

set.seed(1)
x <- matrix(stats::rnorm(150 * 5), 150, 5) %*% chol(0.4^abs(outer(1:5, 1:5, "-")))
colnames(x) <- paste0("V", 1:5)
bs <- net_boot(x, n_boot = 50, cores = 1)
as.data.frame(bs)
#>    from to  observed         mean       lower      upper prop_nonzero
#> 1    V1 V2 0.2647959  0.216657987  0.06460790 0.34930531         0.98
#> 2    V1 V3 0.0000000  0.019208250  0.00000000 0.11096685         0.24
#> 3    V2 V3 0.3114876  0.270731621  0.14521228 0.39115437         1.00
#> 4    V1 V4 0.0000000 -0.024222579 -0.18536844 0.00000000         0.18
#> 5    V2 V4 0.0000000  0.017061134  0.00000000 0.11199844         0.22
#> 6    V3 V4 0.2936317  0.262160092  0.06928587 0.42053424         0.98
#> 7    V1 V5 0.0000000  0.002383375 -0.08504584 0.09770776         0.16
#> 8    V2 V5 0.0000000  0.004825769  0.00000000 0.07379984         0.12
#> 9    V3 V5 0.0000000  0.015522783  0.00000000 0.13699210         0.24
#> 10   V4 V5 0.2807738  0.265568744  0.13497982 0.38097285         1.00
#>    significant
#> 1         TRUE
#> 2        FALSE
#> 3         TRUE
#> 4        FALSE
#> 5        FALSE
#> 6         TRUE
#> 7        FALSE
#> 8        FALSE
#> 9        FALSE
#> 10        TRUE