covc: covariance between random effects

In sommer: Solving Mixed Model Equations in R

covariance between random effects

Description

covc merges the incidence matrices and covariance matrices of two random effects to fit an unstructured model between 2 different random effects to be fitted with the mmec solver.

Usage

  covc(ran1, ran2, thetaC=NULL, theta=NULL)

Arguments

ran1	the random call of the first random effect.
ran2	the random call of the first random effect.
thetaC	an optional matrix for constraints in the variance components.
theta	an optional matrix for initial values of the variance components.

Details

This implementation aims to fit models where covariance between random variables is expected to exist. For example, indirect genetic effects.

Value

$Z

a incidence matrix Z* = Z Gamma which is the original incidence matrix for the timevar multiplied by the loadings.

Author(s)

Giovanny Covarrubias-Pazaran

References

Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744

Bijma, P. (2014). The quantitative genetics of indirect genetic effects: a selective review of modelling issues. Heredity, 112(1), 61-69.

See Also

The function vsc to know how to use covc in the mmec solver.

Examples

data(DT_ige)
DT <- DT_ige
covRes <- with(DT, covc( vsc(isc(focal)) , vsc(isc(neighbour)) ) )
str(covRes)
# look at DT_ige help page to see how to fit an actual model

sommer documentation built on Nov. 13, 2023, 9:05 a.m.