rd_vcov

object inheriting from class 'JointAI'

object

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

outcome

the first iteration of interest
(see <code><a rd-options="coda" href="/link/window.mcmc?package=JointAI&version=1.0.2&to=coda" data-mini-rdoc="coda::window.mcmc">window.mcmc</a></code>)

start

the last iteration of interest
(see <code><a rd-options="coda" href="/link/window.mcmc?package=JointAI&version=1.0.2&to=coda" data-mini-rdoc="coda::window.mcmc">window.mcmc</a></code>)

thinning interval (integer; see <code><a rd-options="coda" href="/link/window.mcmc?package=JointAI&version=1.0.2&to=coda" data-mini-rdoc="coda::window.mcmc">window.mcmc</a></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.

thin

optional vector of the index numbers of chains that
should be excluded

exclude_chains

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

mess

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

warn

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.
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'
<http://mcmc-jags.sourceforge.net/> with the help of
the package 'rjags'.

Nicole Erler

JointAI

Joint Analysis and Imputation of Incomplete Data

Nicole S. Erler

rd_vcov function

the first iteration of interest
(see <code><a rd-options='coda' href='window.mcmc'>window.mcmc</a></code>)

the last iteration of interest
(see <code><a rd-options='coda' href='window.mcmc'>window.mcmc</a></code>)

thinning interval (integer; see <code><a rd-options='coda' href='window.mcmc'>window.mcmc</a></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.

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.

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