rd_vcov

Extract the random effects variance covariance matrix
Returns the posterior mean of the variance-covariance matrix/matrices of
the random effects in a fitted JointAI object.

Joint analysis and imputation of incomplete data in the Bayesian
framework, using (generalized) linear (mixed) models and extensions there of,
survival models, or joint models for longitudinal and survival data, as
described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>.
Incomplete covariates, if present, are automatically imputed.
The package performs some preprocessing of the data and creates a 'JAGS'
model, which will then automatically be passed to 'JAGS'
<https://mcmc-jags.sourceforge.io/> with the help of
the package 'rjags'.

Nicole Erler

JointAI

Joint Analysis and Imputation of Incomplete Data

Nicole S. Erler

rd_vcov function

<dl><dt>object</dt>
<dd>object inheriting from class 'JointAI'</dd>
<dt>outcome</dt>
<dd>optional; vector of integers giving the indices of the
outcomes for which the random effects variance-covariance
matrix/matrices should be returned.</dd>
<dt>start</dt>
<dd>the first iteration of interest
(see <code>window.mcmc</code>)</dd>
<dt>end</dt>
<dd>the last iteration of interest
(see <code>window.mcmc</code>)</dd>
<dt>thin</dt>
<dd>thinning interval (integer; see <code>window.mcmc</code>).
For example, <code>thin = 1</code> (default) will keep the MCMC samples
from all iterations; <code>thin = 5</code> would only keep every 5th
iteration.</dd>
<dt>exclude_chains</dt>
<dd>optional vector of the index numbers of chains that
should be excluded</dd>
<dt>mess</dt>
<dd>logical; should messages be given? Default is
<code>TRUE</code>.</dd>
<dt>warn</dt>
<dd>logical; should warnings be given? Default is
<code>TRUE</code>.</dd></dl>

Arguments

Extract the random effects variance covariance matrix
Returns the posterior mean of the variance-covariance matrix/matrices of
the random effects in a fitted JointAI object. — rd_vcov

<dl>

<dt>object</dt>
<dd>object inheriting from class 'JointAI'</dd>


<dt>outcome</dt>
<dd>optional; vector of integers giving the indices of the
outcomes for which the random effects variance-covariance
matrix/matrices should be returned.</dd>


<dt>start</dt>
<dd>the first iteration of interest
(see <code>window.mcmc</code>)</dd>


<dt>end</dt>
<dd>the last iteration of interest
(see <code>window.mcmc</code>)</dd>


<dt>thin</dt>
<dd>thinning interval (integer; see <code>window.mcmc</code>).
For example, <code>thin = 1</code> (default) will keep the MCMC samples
from all iterations; <code>thin = 5</code> would only keep every 5th
iteration.</dd>


<dt>exclude_chains</dt>
<dd>optional vector of the index numbers of chains that
should be excluded</dd>


<dt>mess</dt>
<dd>logical; should messages be given? Default is
<code>TRUE</code>.</dd>


<dt>warn</dt>
<dd>logical; should warnings be given? Default is
<code>TRUE</code>.</dd>

</dl>

Extract the random effects variance covariance matrix
Returns the posterior mean of the variance-covariance matrix/matrices of
the random effects in a fitted JointAI object.

rd_vcov: Extract the random effects variance covariance matrix Returns the posterior mean of the variance-covariance matrix/matrices of the random effects in a fitted JointAI object.

Description

Usage

Arguments