check_classes

Runs a check that all variables that are used in the model are of a known
class (numeric, ordered, factor, logical, integer) so that type-appropriate
models can be specified.
Note: This function does not check the type of grouping variables, which
may be character strings.

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

check_classes function

<dl><dt>data</dt>
<dd>a <code>data.frame</code></dd>
<dt>fixed</dt>
<dd>a <code>formula</code></dd>
<dt>random</dt>
<dd>a <code>formula</code></dd>
<dt>auxvars</dt>
<dd>a one-sided <code>formula</code></dd>
<dt>timevar</dt>
<dd>a character string (name of the time variable, used in joint
models)</dd>
<dt>mess</dt>
<dd>logical, if <code>TRUE</code> messages are printed</dd></dl>

Arguments

Check classes of all variables used in the model — check_classes

<dl>

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


<dt>fixed</dt>
<dd>a <code>formula</code></dd>


<dt>random</dt>
<dd>a <code>formula</code></dd>


<dt>auxvars</dt>
<dd>a one-sided <code>formula</code></dd>


<dt>timevar</dt>
<dd>a character string (name of the time variable, used in joint
models)</dd>


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

</dl>

check_classes: Check classes of all variables used in the model

Description

Usage

Value

Arguments

Details