extract_id

Internal function, used in <code>divide_matrices()</code>, <code>get_models()</code>,
various help functions, <code><a href="/link/predict()?package=JointAI&version=1.0.6" data-mini-rdoc="JointAI::predict()">predict()</a></code> (2022-02-06)

internal

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

extract_id function

<dl><dt>random</dt>
<dd>a one-sided random effects formula or a list of such formulas</dd>
<dt>warn</dt>
<dd>logical; should warnings be printed?</dd></dl>

Arguments

Internal function, used in <code>divide_matrices()</code>, <code>get_models()</code>,
various help functions, <code><a href='https://rdrr.io/r/stats/predict.html'>predict()</a></code> (2022-02-06)

Extract all id variables from a list of random effects formulas — extract_id

<dl>

<dt>random</dt>
<dd>a one-sided random effects formula or a list of such formulas</dd>


<dt>warn</dt>
<dd>logical; should warnings be printed?</dd>

</dl>

extract_id: Extract all id variables from a list of random effects formulas

Description

Usage

Arguments