drop_levels

CHECKS if there are empty factor levels in any of the variables used in the
model.

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

drop_levels function

<dl><dt>data</dt>
<dd>a <code>data.frame</code></dd>
<dt>allvars</dt>
<dd>a character vector (of all variable names used in the model)</dd>
<dt>warn</dt>
<dd>logical, if <code>TRUE</code> warnings are printed</dd></dl>

Arguments

Check for empty factor levels — drop_levels

<dl>

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


<dt>allvars</dt>
<dd>a character vector (of all variable names used in the model)</dd>


<dt>warn</dt>
<dd>logical, if <code>TRUE</code> warnings are printed</dd>

</dl>

drop_levels: Check for empty factor levels

Description

Usage

Value

Arguments