Plot a network bootstrap

Visualises a net_boot() result. type = "edges" (default) draws the bootstrapped edge-weight confidence intervals sorted by the observed weight (bootnet's edge-accuracy plot); type = "centrality" draws the bootstrapped centrality intervals, one sorted panel per measure; type = "edge_diff" and type = "centrality_diff" draw the bootstrapped difference "significance box" matrix for edges or for one centrality; type = "predictability" draws the node predictability intervals (only when net_boot() was run with predictability = TRUE).

Usage

# S3 method for class 'psychnet_bootstrap'
plot(
  x,
  type = c("edges", "centrality", "edge_diff", "centrality_diff", "predictability"),
  measure = NULL,
  ...
)

Arguments

x

A psychnet_bootstrap object from net_boot().

type

One of "edges", "centrality", "edge_diff", "centrality_diff", "predictability".

measure

For "centrality"/"centrality_diff", which measure(s) to draw. Default: all bootstrapped measures ("centrality") or the first ("centrality_diff").

...

Unused.

Value

x, invisibly. Called for the plot it draws.

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)
# method = "pcor" keeps this example fast; the "glasso" default (and
# n_boot >= 1000) is what a real analysis should use.
bs <- net_boot(x, method = "pcor", n_boot = 50, cores = 1)
plot(bs)                       # edge-weight CIs

plot(bs, type = "centrality")  # centrality CIs