paste_scale

Create the scaling in a data element of a linear predictor

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 Erler

JointAI

Joint Analysis and Imputation of Incomplete Data

Nicole S. Erler

paste_scale function

<dl><dt>x</dt>
<dd>a character string</dd>
<dt>row</dt>
<dd>integer; indicating the row of <code>scalemat</code> to be used</dd>
<dt>scalemat</dt>
<dd>character string; name of the matrix containing the scaling
information (e.g., "spM_lvlone"). This matrix is assumed to
have columns "center" and "scale".</dd></dl>

Arguments

Create the scaling in a data element of a linear predictor — paste_scale

<dl>

<dt>x</dt>
<dd>a character string</dd>


<dt>row</dt>
<dd>integer; indicating the row of <code>scalemat</code> to be used</dd>


<dt>scalemat</dt>
<dd>character string; name of the matrix containing the scaling
information (e.g., "spM_lvlone"). This matrix is assumed to
have columns "center" and "scale".</dd>

</dl>

paste_scale: Create the scaling in a data element of a linear predictor

Description

Usage

Value

Arguments