Extract a report-ready table of Sigma or correlation entries

extract_Sigma() returns matrices because that is the most convenient interactive representation. extract_Sigma_table() returns the same covariance target as one row per matrix entry so articles, tables, and plot helpers can work without hand-indexing matrices. It can also turn a bootstrap_Sigma() result into the same row schema with bootstrap interval columns filled in.

Usage

extract_Sigma_table(
  fit,
  level = "unit",
  part = c("total", "shared", "unique"),
  measure = c("covariance", "correlation"),
  entries = c("unique", "all", "upper", "lower", "offdiag", "diag"),
  link_residual = c("auto", "none")
)

Arguments

fit

A fit returned by gllvmTMB(), an admitted engine = "julia" bridge fit, or a bootstrap_Sigma() result.

level

Character vector of covariance levels, or "all" for every level present in the fit. Canonical levels are "unit", "unit_obs", "cluster", "cluster2", "phy", and "spatial"; named kernel_*() tiers are accepted by their fitted name; legacy aliases "B", "W", and "spde" are accepted. For gllvmTMB_julia objects, only "unit" is currently routed; "all" maps to that tier.

part

One of "total" (default), "shared", or "unique", passed to extract_Sigma().

measure

One of "covariance" (default) or "correlation". Correlation tables are available only for part = "total" because the report-ready correlation target is based on the full Lambda Lambda^T + Psi covariance.

entries

Which symmetric matrix entries to return. "unique" (default) returns the diagonal plus upper triangle, one row per unique estimand. "all" returns every cell, useful for heatmaps. "upper", "lower", "offdiag", and "diag" return the corresponding subsets.

link_residual

Passed to extract_Sigma(). "auto" (default) adds family/link implicit residual variances to non-Gaussian trait diagonals; "none" returns only the fitted model-implied covariance.

Value

A data frame with one row per requested entry and stable columns: estimand, trait_i, trait_j, integer indices i and j, level, component, matrix, estimate, lower, upper, interval_method, interval_status, scale, diagonal, and triangle. Interval columns are NA with interval_method = "none" because this helper is point-estimate only.

Details

The helper reshapes the same covariance and correlation matrices returned by extract_Sigma(). A bootstrap_Sigma() object can supply interval columns. Julia bridge fits currently expose only point estimates for the ordinary unit tier. The helper does not create profile or Wald intervals.

The table is a point-estimate view over extract_Sigma(). It does not compute confidence intervals from a fitted model directly. Use extract_correlations() when you need pairwise correlation intervals, or bootstrap_Sigma() followed by extract_Sigma_table() when you need bootstrap intervals for Sigma or correlation matrix entries.

See also

extract_Sigma() for the underlying matrix extractor; extract_correlations() for pairwise correlation intervals; bootstrap_Sigma() for bootstrap uncertainty.

Examples

if (FALSE) { # \dontrun{
fit <- gllvmTMB(
  value ~ 0 + trait +
          latent(0 + trait | unit, d = 2),
  data  = df,
  trait = "trait",
  unit  = "unit"
)
extract_Sigma_table(fit, level = "unit")
extract_Sigma_table(fit, level = "unit", measure = "correlation")
extract_Sigma_table(fit, level = c("unit", "unit_obs"), entries = "all")
boot <- bootstrap_Sigma(fit, n_boot = 50, level = "unit",
                        what = "Sigma", progress = FALSE)
extract_Sigma_table(boot, level = "unit", entries = "upper")
} # }