paste_scaling

Identifies if a data element of a linear predictor should be scaled (based
on whether scaling parameters are given) and then calls <code>paste_scale()</code>.

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_scaling function

<dl><dt>x</dt>
<dd>vector of character strings; to be scaled, typically matrix columns</dd>
<dt>rows</dt>
<dd>integer vector; row numbers of the matrix containing the scaling
information</dd>
<dt>scale_pars</dt>
<dd>matrix containing the scaling information, with columns
"center" and "scale"</dd>
<dt>scalemat</dt>
<dd>the name of the scaling matrix in the JAGS model
(e.g. "spM_id")</dd></dl>

Arguments

Wrap a data element of a linear predictor in scaling syntax — paste_scaling

<dl>

<dt>x</dt>
<dd>vector of character strings; to be scaled, typically matrix columns</dd>


<dt>rows</dt>
<dd>integer vector; row numbers of the matrix containing the scaling
information</dd>


<dt>scale_pars</dt>
<dd>matrix containing the scaling information, with columns
"center" and "scale"</dd>


<dt>scalemat</dt>
<dd>the name of the scaling matrix in the JAGS model
(e.g. "spM_id")</dd>

</dl>

paste_scaling: Wrap a data element of a linear predictor in scaling syntax

Description

Usage

Arguments

Details