get_nranef

Extract the number of random effects

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

get_nranef function

<dl><dt>idvar</dt>
<dd>vector of the names of all id variables</dd>
<dt>random</dt>
<dd>a random effect formula or list of random effects formulas</dd>
<dt>data</dt>
<dd>a <code>data.frame</code></dd></dl>

Arguments

Extract the number of random effects — get_nranef

<dl>

<dt>idvar</dt>
<dd>vector of the names of all id variables</dd>


<dt>random</dt>
<dd>a random effect formula or list of random effects formulas</dd>


<dt>data</dt>
<dd>a <code>data.frame</code></dd>

</dl>

get_nranef: Extract the number of random effects

Description

Usage

Value

Arguments