This function just calls <code>ns()</code> from the
<a href="https://CRAN.R-project.org/package=splines">splines</a>
package.

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

ns function

<dl><dt>x</dt>
<dd>the predictor variable. Missing values are allowed.</dd>
<dt>df</dt>
<dd>degrees of freedom. One can supply <code>df</code> rather than
 knots; <code>ns()</code> then chooses <code>df - 1 - intercept</code> knots at
 suitably chosen quantiles of <code>x</code> (which will ignore missing
 values). The default, <code>df = NULL</code>, sets the number of
 inner knots as <code>length(knots)</code>.</dd>
<dt>knots</dt>
<dd>breakpoints that define the spline. The default is no
 knots; together with the natural boundary conditions this results in
 a basis for linear regression on <code>x</code>. Typical values are the
 mean or median for one knot, quantiles for more knots. See also
 <code>Boundary.knots</code>.</dd>
<dt>intercept</dt>
<dd>if <code>TRUE</code>, an intercept is included in the
 basis; default is <code>FALSE</code>.</dd>
<dt>Boundary.knots</dt>
<dd>boundary points at which to impose the natural
 boundary conditions and anchor the B-spline basis (default the range
 of the data). If both <code>knots</code> and <code>Boundary.knots</code> are
 supplied, the basis parameters do not depend on <code>x</code>. Data can
 extend beyond <code>Boundary.knots</code></dd></dl>

Arguments

This function just calls <code>ns()</code> from the
<a href='https://CRAN.R-project.org/package=splines'>splines</a>
package.

Generate a Basis Matrix for Natural Cubic Splines — ns

<dl>

<dt>x</dt>
<dd>the predictor variable. Missing values are allowed.</dd>


<dt>df</dt>
<dd>degrees of freedom. One can supply <code>df</code> rather than
 knots; <code>ns()</code> then chooses <code>df - 1 - intercept</code> knots at
 suitably chosen quantiles of <code>x</code> (which will ignore missing
 values). The default, <code>df = NULL</code>, sets the number of
 inner knots as <code>length(knots)</code>.</dd>


<dt>knots</dt>
<dd>breakpoints that define the spline. The default is no
 knots; together with the natural boundary conditions this results in
 a basis for linear regression on <code>x</code>. Typical values are the
 mean or median for one knot, quantiles for more knots. See also
 <code>Boundary.knots</code>.</dd>


<dt>intercept</dt>
<dd>if <code>TRUE</code>, an intercept is included in the
 basis; default is <code>FALSE</code>.</dd>


<dt>Boundary.knots</dt>
<dd>boundary points at which to impose the natural
 boundary conditions and anchor the B-spline basis (default the range
 of the data). If both <code>knots</code> and <code>Boundary.knots</code> are
 supplied, the basis parameters do not depend on <code>x</code>. Data can
 extend beyond <code>Boundary.knots</code></dd>

</dl>

ns: Generate a Basis Matrix for Natural Cubic Splines

Description

Usage

Arguments