covc

<code>covc</code> 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 <code>mmec</code> solver.

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.

Giovanny Covarrubias-Pazaran

sommer

Solving Mixed Model Equations in R

covc function

<dl> <dt>ran1</dt>
<dd>the random call of the first random effect.</dd> <dt>ran2</dt>
<dd>the random call of the first random effect.</dd> <dt>thetaC</dt>
<dd>an optional matrix for constraints in the variance components.</dd> <dt>theta</dt>
<dd>an optional matrix for initial values of the variance components.</dd></dl>

Arguments

Author

covariance between random effects — covc

<dl>

 <dt>ran1</dt>
<dd>the random call of the first random effect.</dd>

 <dt>ran2</dt>
<dd>the random call of the first random effect.</dd>

 <dt>thetaC</dt>
<dd>an optional matrix for constraints in the variance components.</dd>

 <dt>theta</dt>
<dd>an optional matrix for initial values of the variance components.</dd>

</dl>

covc: covariance between random effects

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples