<p>vector of observations for the random effect.</p>

<p><code>us</code> creates an unstructured covariance structure for specific levels of the random effect.</p>

Structural multivariate-univariate linear mixed model solver for multiple random effects allowing the specification of variance-covariance structures for random effects and allowing the fit of heterogeneous and special variance models (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). ML/REML estimates can be obtained using the Direct-Inversion Newton-Raphson, Direct-Inversion Average Information and Efficient Mixed Model Association algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) to include multiple relationship matrices or other covariance structures. Spatial models can be fitted using the two-dimensional spline functionality in sommer.

us: unstructured covariance structure

Description

Usage

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See Also

Examples