Quantitative-genetic formula markers — qg_effect

Usage

permanent(formula, ...)

common_env(formula, ...)

maternal_genetic(formula, pedigree = NULL, ...)

maternal_env(formula, ...)

paternal_genetic(formula, pedigree = NULL, ...)

paternal_env(formula, ...)

cytoplasmic(formula, ...)

imprinting(formula, pedigree = NULL, parent = c("maternal", "paternal"), ...)

dominance(formula, pedigree = NULL, D = NULL, Dinv = NULL, ...)

epistasis(formula, pedigree = NULL, E = NULL, Einv = NULL, ...)

relmat(formula, K = NULL, Kinv = NULL, Q = NULL, ...)

precision(formula, Q = NULL, ...)

group(formula, ...)

unknown_parent_group(formula, ...)

metafounder(formula, pedigree = NULL, group = NULL, Gamma = NULL, ...)

inbreeding(formula, ...)

Arguments

formula: A random-effect expression such as 1 | id.
...: Reserved for future syntax.
pedigree: A pedigree data frame for future parental and relationship effects.
parent: Planned parent-of-origin side for imprinting effects.
D, Dinv: Dominance relationship or precision matrices.
E, Einv: Epistatic relationship or precision matrices.
K, Kinv, Q: User-supplied relationship or precision matrices.
group: Animal-to-metafounder group assignment, aligned to the normalized pedigree IDs for the opt-in supplied-variance metafounder() bridge.
Gamma: A supplied metafounder relationship matrix (an m-by-m covariance over the m metafounder pseudo-populations; Legarra et al. 2015). Supplied, not estimated; used by the opt-in supplied-variance metafounder() bridge.

Value

NULL, invisibly. Calls are interpreted by hsquared() when they appear inside model formulas.

Details

These functions provide readable formula vocabulary for standard quantitative-genetic, parental, inheritance, and custom-kernel models. Called directly they are inert (they return NULL); they take meaning only inside an hsquared() formula. permanent(), common_env(), and maternal_genetic() now fit through an opt-in, experimental engine path (engine = "julia", REML-only, not the default, mirroring a partial validation gate) as the second random effect alongside animal(). metafounder() now fits through an opt-in, supplied-variance, experimental engine path (engine = "julia", target = "metafounder"); Gamma and the variance components are supplied, not estimated. relmat() and precision() now fit through an opt-in, EXPERIMENTAL supplied-relationship primary engine path (engine = "julia", REML-only, not the default, mirroring a partial validation gate): relmat(1 | id, K = K) supplies a dense symmetric positive-definite relationship/covariance matrix K (the parser marshals the inverse Kinv = solve(K); relmat() also accepts a directly supplied inverse via Kinv =/Q =, no solve), and precision(1 | id, Q = Q) supplies the precision (inverse) directly (Kinv = also accepted); both fit through the SAME supplied-relationship-inverse path as genomic()'s Ginv, and the supplied matrix is provenance, not an estimate. The remaining markers (paternal/maternal-environment, dominance, epistasis, cytoplasmic, imprinting, genetic groups / unknown-parent-groups, and inbreeding) are still inert syntax reservations that the parser rejects with a planned-not-implemented message.

Some markers use generic names (e.g. group(), inbreeding()). They are formula-only tokens detected by call head and are not meant to be called directly, so attaching hsquared alongside a package that exports a same-named function (e.g. pedigreemm::inbreeding()) may print a masking message; this is expected and harmless.

inbreeding() is a reserved future effect, not an accessor for inbreeding coefficients. There is no R function that returns the relationship matrix A, A^{-1}, or F used by a fit (payload$Ainv stays NULL; Julia builds the inverse). Compare those objects against nadiv or pedigreemm through JuliaCall with HSquared.additive_relationship(), HSquared.pedigree_inverse(), and HSquared.inbreeding_coefficients(). The fitting-models article shows the call.