longDF

datasets

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

longDF function

A simulated data frame with 329 rows and 21 variables with data from
100 subjects:<dl>
<dt>C1</dt>
<dd>continuous, complete baseline variable</dd><dt>C2</dt>
<dd>continuous, incomplete baseline variable</dd><dt>B1</dt>
<dd>binary, complete baseline variable</dd><dt>B2</dt>
<dd>binary, incomplete baseline variable</dd><dt>M1</dt>
<dd>unordered factor; complete baseline variable</dd><dt>M2</dt>
<dd>unordered factor; incomplete baseline variable</dd><dt>O1</dt>
<dd>ordered factor; complete baseline variable</dd><dt>O2</dt>
<dd>ordered factor; incomplete baseline variable</dd><dt>P1</dt>
<dd>count variable; complete baseline variable</dd><dt>P2</dt>
<dd>count variable; incomplete baseline variable</dd><dt>c1</dt>
<dd>continuous, complete longitudinal variable</dd><dt>c2</dt>
<dd>continuous incomplete longitudinal variable</dd><dt>b1</dt>
<dd>binary, complete longitudinal variable</dd><dt>b2</dt>
<dd>binary incomplete longitudinal variable</dd><dt>o1</dt>
<dd>ordered factor; complete longitudinal variable</dd><dt>o2</dt>
<dd>ordered factor; incomplete longitudinal variable</dd><dt>p1</dt>
<dd>count variable; complete longitudinal variable</dd><dt>p2</dt>
<dd>count variable; incomplete longitudinal variable</dd><dt>id</dt>
<dd>id (grouping) variable</dd><dt>time</dt>
<dd>continuous complete longitudinal variable</dd><dt>y</dt>
<dd>continuous, longitudinal (outcome) variable</dd>
</dl>

Format

Longitudinal example dataset — longDF

A simulated data frame with 329 rows and 21 variables with data from
100 subjects:<dl>
<dt>C1</dt>
<dd>continuous, complete baseline variable</dd>

<dt>C2</dt>
<dd>continuous, incomplete baseline variable</dd>

<dt>B1</dt>
<dd>binary, complete baseline variable</dd>

<dt>B2</dt>
<dd>binary, incomplete baseline variable</dd>

<dt>M1</dt>
<dd>unordered factor; complete baseline variable</dd>

<dt>M2</dt>
<dd>unordered factor; incomplete baseline variable</dd>

<dt>O1</dt>
<dd>ordered factor; complete baseline variable</dd>

<dt>O2</dt>
<dd>ordered factor; incomplete baseline variable</dd>

<dt>P1</dt>
<dd>count variable; complete baseline variable</dd>

<dt>P2</dt>
<dd>count variable; incomplete baseline variable</dd>

<dt>c1</dt>
<dd>continuous, complete longitudinal variable</dd>

<dt>c2</dt>
<dd>continuous incomplete longitudinal variable</dd>

<dt>b1</dt>
<dd>binary, complete longitudinal variable</dd>

<dt>b2</dt>
<dd>binary incomplete longitudinal variable</dd>

<dt>o1</dt>
<dd>ordered factor; complete longitudinal variable</dd>

<dt>o2</dt>
<dd>ordered factor; incomplete longitudinal variable</dd>

<dt>p1</dt>
<dd>count variable; complete longitudinal variable</dd>

<dt>p2</dt>
<dd>count variable; incomplete longitudinal variable</dd>

<dt>id</dt>
<dd>id (grouping) variable</dd>

<dt>time</dt>
<dd>continuous complete longitudinal variable</dd>

<dt>y</dt>
<dd>continuous, longitudinal (outcome) variable</dd>


</dl>

longDF: Longitudinal example dataset

Description

Usage

Arguments

Format