Skip to contents

Nonparanormal graphical model (huge)

Source: R/huge.R
huge_network.Rd

Estimates a Gaussian graphical model after a rank-based nonparanormal transform that relaxes the multivariate-normal assumption, then selects the L1 penalty by EBIC and refits to the certified optimum. Equivalent in purpose to huge::huge() (nonparanormal) / bootnet's "huge" default, but pure base R and self-certified via glasso_kkt() on the transformed correlation.

Usage

huge_network(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  npn = c("shrinkage", "truncation", "skeptic"),
  gamma = 0.5,
  nlambda = 100L,
  lambda_min_ratio = 0.01,
  threshold = 0,
  na_method = c("pairwise", "listwise"),
  native = TRUE,
  labels = NULL
)

Arguments

data

Numeric data frame or matrix (rows = observations). Optional if cor_matrix is supplied (then the transform is skipped).

cor_matrix

Optional pre-transformed correlation matrix; if given, n is required, data and npn are ignored.

n

Sample size (required when cor_matrix is supplied).

npn

Nonparanormal transform: "shrinkage" (default), "truncation", or "skeptic" (Spearman).

gamma

EBIC hyperparameter. Default 0.5.

nlambda

Number of penalties on the path. Default 100.

lambda_min_ratio

Smallest penalty as a fraction of the largest.

threshold

Partial correlations with absolute value below this are zeroed. Default 0.

na_method

Missing-data handling when data is supplied: "pairwise" (default, with the nonparanormal transform applied per column over observed values) or "listwise". See ebic_glasso().

native

Solver switch for the glasso path: TRUE (default) uses the pure-R solver; FALSE delegates to the glasso Fortran package (in Suggests). See ebic_glasso().

labels

Optional node labels.

Value

A psychnet object whose $weights is the partial-correlation matrix, with $precision, $lambda, $gamma, $cor_matrix (the transformed correlation), $npn, $ebic, and $kkt.

Examples

set.seed(1)
x <- matrix(stats::rnorm(300 * 5), 300, 5)
x <- exp(x %*% chol(0.4^abs(outer(1:5, 1:5, "-"))))   # break normality
huge_network(x)
#> <psychnet> huge network
#>   nodes: 5   edges: 5   (undirected)
#>   lambda: 0.06688   gamma: 0.5
#>   optimality (KKT residual): 5.30e-13