thetaC

an optional matrix for initial values of the variance components.

theta

<code>usc</code> creates an unstructured covariance structure for specific levels of the random effect to be used 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>). 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 f records) or using the mixed-model-equations-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 in sommer.

Giovanny Covarrubias-Pazaran

sommer

Solving Mixed Model Equations in R

usc function

<dl> <dt>x</dt>
<dd>vector of observations for the 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

unstructured covariance structure — usc

<dl>

 <dt>x</dt>
<dd>vector of observations for the 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>

usc: unstructured covariance structure

Description

Usage

Value

Arguments

Author

References

See Also

Examples