Creates an autocorrelation matrix of order one with parameters specified.

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

AR1 function

<dl> <dt>x</dt>
<dd>vector of the variable to define the factor levels for the AR1 covariance structure.</dd> <dt>rho</dt>
<dd>rho value for the matrix.</dd></dl>

Arguments

Autocorrelation matrix of order 1. — AR1

<dl>

 <dt>x</dt>
<dd>vector of the variable to define the factor levels for the AR1 covariance structure.</dd>

 <dt>rho</dt>
<dd>rho value for the matrix.</dd>

</dl>

AR1: Autocorrelation matrix of order 1.

Description

Usage

Value

Arguments

Details

References

See Also

Examples