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EBIC-regularized Gaussian graphical model (graphical lasso)

Source: R/glasso.R
ebic_glasso.Rd

Selects an L1 penalty by the extended BIC (Foygel & Drton 2010) over a log-spaced path, then refits the chosen penalty to machine precision so the returned network is the certified global optimum of the convex objective. Equivalent in purpose to qgraph::EBICglasso() / bootnet's "EBICglasso" default, but pure base R and self-certified (see glasso_kkt()).

Usage

ebic_glasso(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  gamma = 0.5,
  nlambda = 100L,
  lambda_min_ratio = 0.01,
  threshold = 0,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  na_method = c("pairwise", "listwise"),
  native = TRUE,
  labels = NULL
)

Arguments

data

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

cor_matrix

Optional correlation matrix; if given, n is required and data is ignored.

n

Sample size (required when cor_matrix is supplied).

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. Default 0.01.

threshold

Partial correlations with absolute value below this are set to zero. Default 0.

cor_method

Correlation used when data is supplied: "pearson" (default), "spearman", "kendall", or "auto" (polychoric/polyserial for ordinal items, the qgraph::cor_auto / bootnet default). See cor_auto().

na_method

Missing-data handling when data is supplied: "pairwise" (default, pairwise-complete correlations + nearest-PD projection) or "listwise" (drop incomplete rows). Identical for complete data.

native

Solver switch. TRUE (default) uses psychnet's own pure-R, dependency-free, self-certified solver. FALSE delegates each fixed-penalty solve to the established glasso Fortran package (in Suggests) for speed and byte-identical glasso/qgraph output, at its looser convergence (the reported $kkt then shows glasso's tolerance rather than ~1e-11).

labels

Optional node labels.

Value

A psychnet object whose $weights is the partial-correlation matrix, with $precision, $lambda, $gamma, $cor_matrix, $ebic, $native, and $kkt (the stationarity residual of the returned network).

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
fit <- ebic_glasso(cor_matrix = S, n = 250)
fit
#> <psychnet> glasso network
#>   nodes: 6   edges: 5   (undirected)
#>   lambda: 0.004   gamma: 0.5
#>   optimality (KKT residual): 1.10e-10
as.data.frame(fit)
#>   from to    weight
#> 1   V1 V2 0.3681824
#> 2   V2 V3 0.3423189
#> 3   V3 V4 0.3423189
#> 4   V4 V5 0.3423189
#> 5   V5 V6 0.3681824