plot.mmec

<code>plot</code> method for class <code>"mmec"</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

plot.mmec function

<dl> <dt>x</dt>
<dd>an object of class <code>"mmec"</code></dd> <dt>stnd</dt>
<dd>argument for ploting the residuals to know if they should be standarized.</dd> <dt>...</dt>
<dd>Further arguments to be passed</dd></dl>

Arguments

Giovanny Covarrubias <a href="/link/covarrubiasp%40wisc.edu?package=sommer&version=4.3.5" data-mini-rdoc="sommer::covarrubiasp@wisc.edu">covarrubiasp@wisc.edu</a>

Author

plot form a LMM plot with mmec — plot.mmec

<dl>

 <dt>x</dt>
<dd>an object of class <code>"mmec"</code></dd>

 <dt>stnd</dt>
<dd>argument for ploting the residuals to know if they should be standarized.</dd>

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

</dl>

Giovanny Covarrubias <a href='mailto:covarrubiasp@wisc.edu'>covarrubiasp@wisc.edu</a>

plot.mmec: plot form a LMM plot with mmec

Description

Usage

Value

Arguments

Author

See Also

Examples