print.imputation

Print <code>imputation</code> object

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 

print.imputation function

<dl><dt>x</dt>
<dd>An <code>imputation</code> object generated by <code>impute()</code>.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Print imputation object — print.imputation

<dl>

<dt>x</dt>
<dd>An <code>imputation</code> object generated by <code>impute()</code>.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

print.imputation: Print `imputation` object

Description

Usage

Arguments