convert_variables

Cleans up the data by<ul>
<li>changing <code>NaN</code> to <code>NA</code></li>
<li>converting continuous variables with just two values to factor</li>
<li>converting logical variables to a factor</li>
<li>cleaning factor labels (using <code><a href="/link/make.names()?package=JointAI&version=1.1.0" data-mini-rdoc="JointAI::make.names()">make.names()</a></code>)</li>
</ul>

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

convert_variables function

<dl><dt>data</dt>
<dd>a <code>data.frame</code></dd>
<dt>allvars</dt>
<dd>a character vector of the relevant variables in <code>data</code></dd>
<dt>mess</dt>
<dd>logical, if <code>TRUE</code> messages are printed</dd></dl>

Arguments

Cleans up the data by<ul>
<li>changing <code>NaN</code> to <code>NA</code></li>
<li>converting continuous variables with just two values to factor</li>
<li>converting logical variables to a factor</li>
<li>cleaning factor labels (using <code><a href='https://rdrr.io/r/base/make.names.html'>make.names()</a></code>)</li>
</ul>

Convert variables — convert_variables

<dl>

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


<dt>allvars</dt>
<dd>a character vector of the relevant variables in <code>data</code></dd>


<dt>mess</dt>
<dd>logical, if <code>TRUE</code> messages are printed</dd>

</dl>

convert_variables: Convert variables

Description

Usage

Value

Arguments

Details