cross_corr

cross_corr_plot

These functions compute the cross-correlations of the MCMC samples in an
JointAI object via <code>coda::crosscorr()</code> and plot them using either the
corrplot package or <code>coda::crosscorr.plot()</code>.

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 S. Erler

JointAI

Joint Analysis and Imputation of Incomplete Data

cross_corr function

<dl><dt>object</dt>
<dd>an object of class JointAI</dd>
<dt>outcome</dt>
<dd>integer; index of the outcome model for which the
correlations should be plotted</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>type</dt>
<dd>character; type of plot to be produced. Either "corrplot"
(default) or "coda".</dd></dl>

Arguments

Cross-correlation of MCMC samples — cross_corr

<dl>

<dt>object</dt>
<dd>an object of class JointAI</dd>


<dt>outcome</dt>
<dd>integer; index of the outcome model for which the
correlations should be plotted</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>type</dt>
<dd>character; type of plot to be produced. Either "corrplot"
(default) or "coda".</dd>

</dl>

cross_corr: Cross-correlation of MCMC samples

Description

Usage

Value

Arguments

Examples