check_mcmc

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 

check_mcmc function

<dl><dt>stan_fit</dt>
<dd>A <code>stanfit</code> object.</dd>
<dt>n_draws</dt>
<dd>Number of MCMC draws.</dd>
<dt>threshold_lowESS</dt>
<dd>A number in <code>[0,1]</code> indicating the minimum acceptable
value of the relative ESS. See details.</dd></dl>

Arguments

Diagnostics of the MCMC — check_mcmc

<dl>

<dt>stan_fit</dt>
<dd>A <code>stanfit</code> object.</dd>


<dt>n_draws</dt>
<dd>Number of MCMC draws.</dd>


<dt>threshold_lowESS</dt>
<dd>A number in <code>[0,1]</code> indicating the minimum acceptable
value of the relative ESS. See details.</dd>

</dl>

Diagnostics of the MCMC

check_mcmc: Diagnostics of the MCMC

Description

Usage

Value

Arguments

Details