residuals.mmes

<code>residuals</code> method for class <code>"mmes"</code>.

models

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

residuals.mmes function

<dl> <dt>object</dt>
<dd>an object of class <code>"mmes"</code></dd> <dt>...</dt>
<dd>Further arguments to be passed</dd></dl>

Arguments

Author

Residuals form a GLMM fitted with mmes — residuals.mmes

<dl>

 <dt>object</dt>
<dd>an object of class <code>"mmes"</code></dd>

 <dt>...</dt>
<dd>Further arguments to be passed</dd>

</dl>

residuals.mmes: Residuals form a GLMM fitted with mmes

Description

Usage

Value

Arguments

Author

See Also