prepare_init_vals

This function is used by <code>complete_control_bayes()</code> when the <code>init</code> argument is set to <code>"mmrm"</code>.

internal

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>.

Craig Gower-Page

rbmi

Reference Based Multiple Imputation

Isaac Gravestock

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

prepare_init_vals function

<dl><dt>stan_data</dt>
<dd>A list containing the Stan data.</dd>
<dt>mmrm_initial</dt>
<dd>A list containing the initial values from the MMRM, including attribute <code>cov_param_names</code>
specifying the names of the covariance parameters.</dd>
<dt>chains</dt>
<dd>The number of chains.</dd>
<dt>covariance</dt>
<dd>A character string indicating the type of covariance structure.</dd>
<dt>prior_cov</dt>
<dd>A character string indicating the type of prior for the covariance parameters.</dd></dl>

Arguments

Preparation of Initial Values for MCMC Sampler — prepare_init_vals

<dl>

<dt>stan_data</dt>
<dd>A list containing the Stan data.</dd>


<dt>mmrm_initial</dt>
<dd>A list containing the initial values from the MMRM, including attribute <code>cov_param_names</code>
specifying the names of the covariance parameters.</dd>


<dt>chains</dt>
<dd>The number of chains.</dd>


<dt>covariance</dt>
<dd>A character string indicating the type of covariance structure.</dd>


<dt>prior_cov</dt>
<dd>A character string indicating the type of prior for the covariance parameters.</dd>

</dl>

prepare_init_vals: Preparation of Initial Values for MCMC Sampler

Description

Usage

Value

Arguments