Extract ordination scores and loadings from a fitted multivariate model

Usage

extract_ordination(
  fit,
  level = "unit",
  component = c("total", "innovation", "mean")
)

Arguments

fit: A fitted multivariate model returned by gllvmTMB(). Admitted engine = "julia" bridge fits expose raw unit-tier loadings and scores. Gaussian bridge fits with latent(..., lv = ~ x) also expose retained "mean" and "innovation" score components. Within-unit, structured-tier, and rotated ordinations remain gated for Julia bridge extractors.
level: "unit" (between-unit) or "unit_obs" (within-unit). Deprecated aliases "B" and "W" are still accepted with a warning.
component: Score component to return. "total" returns the latent score entering the linear predictor. "innovation" returns the zero-mean latent innovation. "mean" returns the predictor-informed score mean and is non-zero only for latent(..., lv = ~ x) fits.

Value

A list with scores (units or within-unit observations in rows, latent axes in columns) and loadings (traits in rows, axes in columns).

Examples

if (FALSE) { # \dontrun{
  sim <- simulate_site_trait(
    n_sites = 20, n_species = 6, n_traits = 4,
    mean_species_per_site = 4, seed = 1
  )
  fit <- gllvmTMB(
    value ~ 0 + trait +
            latent(0 + trait | site, d = 2),
    data  = sim$data,
    trait = "trait",
    unit  = "site"
  )
  ord <- extract_ordination(fit, level = "unit")
  head(ord$scores)
  ord$loadings
} # }