Partial correlation network — pcor

Conditional (full-order) association network: each edge is the correlation between two variables with all others partialled out, obtained from the inverse correlation matrix. Equivalent to bootnet's "pcor" default.

Usage

pcor_network(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  threshold = 0,
  alpha = NULL,
  adjust = "none",
  na_method = c("pairwise", "listwise"),
  labels = NULL
)

Arguments

data: Numeric data frame or matrix (rows = observations). Optional if cor_matrix is supplied.
cor_matrix: Optional precomputed correlation matrix; if given, data is ignored and n is required when alpha is used.
n: Sample size (needed for significance testing when cor_matrix is supplied).
cor_method: Correlation method: "pearson" (default), "spearman", "kendall", or "auto" (polychoric/polyserial for ordinal items, the qgraph::cor_auto default; see cor_auto()).
threshold: Correlations with absolute value below this are set to zero. Default 0.
alpha: Significance level; if set, correlations not significant at alpha are zeroed. NULL (default) keeps every edge.
adjust: Multiple-comparison adjustment for the edge p-values (any stats::p.adjust method). Default "none".
na_method: Missing-data handling: "pairwise" (default) uses pairwise-complete correlations projected to the nearest positive-definite matrix; "listwise" drops rows with any NA. Identical when data is complete.
labels: Optional node labels.

Value

A psychnet object whose $weights is the thresholded partial-correlation matrix, with $precision, $cor_matrix (and $p_values when alpha is used).

Examples

x <- matrix(stats::rnorm(200 * 4), 200, 4)
pcor_network(x)
#> <psychnet> pcor network
#>   nodes: 4   edges: 6   (undirected)
pcor_network(x, alpha = 0.05, adjust = "holm")
#> <psychnet> pcor network
#>   nodes: 4   edges: 0   (undirected)