split_imputations

Split a flat list of <code>imputation_single()</code> into multiple <code>imputation_df()</code>'s by ID

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 

split_imputations function

<dl><dt>list_of_singles</dt>
<dd>A list of <code>imputation_single()</code>'s</dd>
<dt>split_ids</dt>
<dd>A list with 1 element per required split. Each element
must contain a vector of "ID"'s which correspond to the <code>imputation_single()</code> ID's
that are required within that sample. The total number of ID's must by equal to the
length of <code>list_of_singles</code></dd></dl>

Arguments

Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations

<dl>

<dt>list_of_singles</dt>
<dd>A list of <code>imputation_single()</code>'s</dd>


<dt>split_ids</dt>
<dd>A list with 1 element per required split. Each element
must contain a vector of "ID"'s which correspond to the <code>imputation_single()</code> ID's
that are required within that sample. The total number of ID's must by equal to the
length of <code>list_of_singles</code></dd>

</dl>

split_imputations: Split a flat list of `imputation_single()` into multiple `imputation_df()`'s by ID

Description

Usage

Arguments

Details