residuals.JointAI

Extract residuals from an object of class JointAI

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

residuals.JointAI function

<dl><dt>object</dt>
<dd>object inheriting from class 'JointAI'</dd>
<dt>type</dt>
<dd>type of residuals: <code>"deviance"</code>, <code>"response"</code>,
<code>"working"</code></dd>
<dt>warn</dt>
<dd>logical; should warnings be given? Default is
<code>TRUE</code>.</dd>
<dt>...</dt>
<dd>currently not used</dd></dl>

Arguments

Extract residuals from an object of class JointAI — residuals.JointAI

<dl>

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


<dt>type</dt>
<dd>type of residuals: <code>"deviance"</code>, <code>"response"</code>,
<code>"working"</code></dd>


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


<dt>...</dt>
<dd>currently not used</dd>

</dl>

residuals.JointAI: Extract residuals from an object of class JointAI

Description

Usage

Arguments

Examples