get_bootstrap_stack

Function creates a <code>Stack()</code> object and populated the stack with bootstrap
samples based upon <code>method$n_samples</code>

Implements standard and reference based multiple imputation methods for continuous
longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
this package supports deterministic conditional mean imputation and jackknifing as described
in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.

Isaac Gravestock

rbmi

Reference Based Multiple Imputation

Craig Gower-Page

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

get_bootstrap_stack function

<dl><dt>longdata</dt>
<dd>A <code>longDataConstructor()</code> object</dd>
<dt>method</dt>
<dd>A <code>method</code> object</dd>
<dt>stack</dt>
<dd>A <code>Stack()</code> object (this is only exposed for unit testing purposes)</dd></dl>

Arguments

Creates a stack object populated with bootstrapped samples — get_bootstrap_stack

<dl>

<dt>longdata</dt>
<dd>A <code>longDataConstructor()</code> object</dd>


<dt>method</dt>
<dd>A <code>method</code> object</dd>


<dt>stack</dt>
<dd>A <code>Stack()</code> object (this is only exposed for unit testing purposes)</dd>

</dl>

get_bootstrap_stack: Creates a stack object populated with bootstrapped samples

Description

Usage

Arguments