choose_default_model

Choose default analysis model based on outcome and data level

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 S. Erler

JointAI

Joint Analysis and Imputation of Incomplete Data

choose_default_model function

<dl><dt>outcome</dt>
<dd>outcome variable</dd>
<dt>lvl</dt>
<dd>hierarchical level of the outcome variable</dd>
<dt>max_lvl</dt>
<dd>maximum hierarchical level in the data</dd></dl>

Arguments

Choose default analysis model based on outcome and data level — choose_default_model

<dl>

<dt>outcome</dt>
<dd>outcome variable</dd>


<dt>lvl</dt>
<dd>hierarchical level of the outcome variable</dd>


<dt>max_lvl</dt>
<dd>maximum hierarchical level in the data</dd>

</dl>

choose_default_model: Choose default analysis model based on outcome and data level

Description

Usage

Value

Arguments