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Local-Global sparse inverse covariance (LoGo)

Source: R/logo.R
logo_network.Rd

Estimates a sparse Gaussian graphical model whose conditional-independence structure is the chordal TMFG: the precision is the closed-form Gaussian Markov random field on that graph (Barfuss et al. 2016). Equivalent in purpose to NetworkToolbox::LoGo() / bootnet's "LoGo" default, pure base R and self-certified via ggm_support_kkt() (the precision reproduces S exactly on the TMFG support).

Usage

logo_network(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  threshold = 0,
  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 correlation matrix; if given, n is required.

n

Sample size (recorded on the result; required with cor_matrix).

cor_method

Correlation when data is supplied: "pearson" (default), "spearman", "kendall", or "auto" (polychoric/polyserial; see cor_auto()).

threshold

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

na_method

Missing-data handling when data is supplied: "pairwise" (default) or "listwise". See ebic_glasso().

labels

Optional node labels.

Value

A psychnet object whose $weights is the partial-correlation matrix, with $precision, $support (the TMFG graph), $cor_matrix, and $kkt.

Examples

set.seed(1)
x <- matrix(stats::rnorm(300 * 6), 300, 6)
logo_network(x)
#> <psychnet> logo network
#>   nodes: 6   edges: 12   (undirected)
#>   optimality (KKT residual): 4.44e-16