Unregularized Ising network for binary data — ising

Estimates an Ising model by unpenalized nodewise logistic regression, with optional Wald p-value edge pruning, combined by the AND (default) or OR rule. The unregularized counterpart of ising_fit(); self-certified by the maximum-likelihood score residual (see glm_lasso_kkt() at lambda = 0).

Usage

ising_sampler(
  data,
  rule = c("AND", "OR"),
  alpha = NULL,
  adjust = "none",
  min_sum = NULL,
  weights = NULL,
  na_method = c("pairwise", "listwise"),
  labels = NULL
)

Arguments

data: Binary (0/1) data frame or matrix (rows = observations).
rule: Edge-combination rule: "AND" (default) or "OR".
alpha: Significance level for Wald edge pruning; NULL (default) keeps every edge.
adjust: Multiple-comparison adjustment for the edge p-values (any stats::p.adjust method). Default "none".
min_sum: Minimum row sum-score; rows below it are dropped before fitting. NULL (default) keeps every row.
weights: Optional non-negative observation weights, one per retained row. NULL (default) is unweighted.
na_method: Missing-data handling: "pairwise" (default, mode-impute) or "listwise". See ising_fit().
labels: Optional node labels.

Value

A psychnet object whose $weights is the symmetric weight matrix, with $thresholds (node intercepts), $rule, $p_values, $nodewise (for net_predict()), and $kkt (worst nodewise score residual).

Examples

set.seed(1)
z <- matrix(stats::rnorm(500 * 2), 500, 2)
x <- cbind(z[, 1], z[, 1], z[, 2], z[, 2]) + matrix(stats::rnorm(500 * 4), 500)
b <- (x > 0) * 1L
colnames(b) <- paste0("V", 1:4)
ising_sampler(b)
#> <psychnet> ising_sampler network
#>   nodes: 4   edges: 6   (undirected)
#>   optimality (KKT residual): 7.03e-10