User-supplied relatedness structured-effect marker

relmat() marks planned syntax for a validated user-supplied relatedness matrix. It is the lower-level route for dependence structures that are not best named as animal(), phylo(), or spatial(): for example a genomic relationship matrix, a laboratory relatedness kernel, or a precision matrix built outside drmTMB and checked by the analyst.

Usage

relmat(term, K = NULL, Q = NULL)

Arguments

term

Structured random-effect term, such as 1 | id.

K

Planned known relatedness or covariance matrix.

Q

Planned known precision or inverse covariance matrix.

Value

A formula marker; never evaluated by users.

Details

Use K for a covariance or relatedness matrix and Q for an inverse covariance or precision matrix. This marker is parsed and documented, but does not fit a model yet. It is intentionally separate from meta_V(), which adds known sampling covariance among observations, and from residual rho12, which models within-observation bivariate residual correlation.

Examples

# Planned: a genomic relatedness matrix for among-line genetic variance.
bf(seed_mass ~ temperature + relmat(1 | line, K = G),
  sigma ~ temperature
)
#> <drm_formula>
#> seed_mass ~ temperature + relmat(1 | line, K = G)
#> sigma ~ temperature

# Planned: a user-built sparse precision for another dependence structure.
bf(growth ~ treatment + relmat(1 | plot, Q = Q_plot),
  sigma ~ treatment
)
#> <drm_formula>
#> growth ~ treatment + relmat(1 | plot, Q = Q_plot)
#> sigma ~ treatment