sample_re

Generates a sample of random effects from the specified covariance matrix.

Specification of generalised linear mixed models using the 'R6' object-orientated class system. The package provides classes 'Covariance', 'MeanFunction' and 'Model', which allow for flexible specification of generalised linear mixed models, as well as functionality to produce relevant matrices, values, and analyses.
See <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> for a detailed manual.

Sam Watson

glmmrBase

Specification of Generalised Linear Mixed Models

Yi Pan

sample_re function

<dl><dt>cov</dt>
<dd>An integer matrix with columns of block identifier, dimension of block, function definition, number of varaibles
in the argument to the funciton, and index of the parameters, respectively. Rows are specific functions of each block.</dd>
<dt>data</dt>
<dd>Vector of data. Created by flattening the matrices in column-major order of the data used in each block.</dd>
<dt>eff_range</dt>
<dd>Vector of values with the effective range parameters of the covariance functions, where required.</dd>
<dt>gamma</dt>
<dd>Vector of parameters used to generate the matrix D.</dd></dl>

Arguments

Generates a sample of random effects — sample_re

<dl>

<dt>cov</dt>
<dd>An integer matrix with columns of block identifier, dimension of block, function definition, number of varaibles
in the argument to the funciton, and index of the parameters, respectively. Rows are specific functions of each block.</dd>


<dt>data</dt>
<dd>Vector of data. Created by flattening the matrices in column-major order of the data used in each block.</dd>


<dt>eff_range</dt>
<dd>Vector of values with the effective range parameters of the covariance functions, where required.</dd>


<dt>gamma</dt>
<dd>Vector of parameters used to generate the matrix D.</dd>

</dl>

sample_re: Generates a sample of random effects

Description

Usage

Value

Arguments