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Independent per-trait animal-model random intercepts: animal_indep(0 + trait | id)

Source: R/animal-keyword.R
animal_indep.Rd

Per-trait animal-model random intercepts with no cross-trait covariance, using the bar-form syntax; the .indep marker disambiguates printing. Mathematical parallel to phylo_indep().

Usage

animal_indep(
  formula,
  pedigree = NULL,
  A = NULL,
  Ainv = NULL,
  common = FALSE,
  rho = 1
)

Arguments

formula

An lme4-bar formula of the form 0 + trait | id.

pedigree, A, Ainv

See animal_scalar().

common

FALSE (default) for a separate additive-genetic variance per trait; TRUE ties all traits to one shared additive-genetic variance (intercept-only). animal_indep(0 + trait | id, common = TRUE) is the canonical one-shared-variance spelling and fits the same model as the soft-deprecated animal_scalar(id).

rho

Source strength: a number in [0,1] fixes rho * K + (1-rho) * diag(diag(K)) on the legacy-resolved source scale. Omitted or explicit 1 preserves the existing model. NULL estimates strength for one Gaussian structured trait-intercept block with complete replicated multivariate observations and no competing ordinary covariance. Estimated latent terms require rank one and at least four traits. The same strength applies to the entire latent-plus-Psi covariance. This parameter is not a variance-share summary. Fixed attenuation and the admitted Gaussian estimator have implementation checks; recovery is regime-specific.

Value

See animal_scalar().

See also

phylo_indep().

Examples

if (FALSE) { # \dontrun{
# Independent per-trait animal-model intercepts via the bar form,
# passing the dense relatedness matrix A directly.
# Grounded in test-animal-keyword.R.
ped <- data.frame(
  id   = paste0("i", 1:12),
  sire = c(rep(NA, 4), rep(c("i1", "i2"), length.out = 8)),
  dam  = c(rep(NA, 4), rep(c("i3", "i4"), length.out = 8))
)
A <- pedigree_to_A(ped)
yvec <- as.numeric(MASS::mvrnorm(
  1, mu = rep(0, 2 * 12),
  Sigma = kronecker(diag(2), A) * 0.5 + diag(2 * 12) * 0.5
))
df <- data.frame(
  species = factor(rep(ped$id, each = 2), levels = ped$id),
  trait   = factor(rep(c("t1", "t2"), times = 12), levels = c("t1", "t2")),
  value   = yvec
)
fit <- gllvmTMB(
  value ~ 0 + trait + animal_indep(0 + trait | species, A = A),
  data = df, unit = "species", family = gaussian()
)
} # }