check_hmc_diagn

Check that:<ol>
<li>There are no divergent iterations.</li>
<li>The Bayesian Fraction of Missing Information (BFMI) is sufficiently low.</li>
<li>The number of iterations that saturated the max treedepth is zero.</li>
</ol>Please see <code>rstan::check_hmc_diagnostics()</code> for details.

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_hmc_diagn function

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

Arguments

Check that:<ol>
<li>There are no divergent iterations.</li>
<li>The Bayesian Fraction of Missing Information (BFMI) is sufficiently low.</li>
<li>The number of iterations that saturated the max treedepth is zero.</li>
</ol>

Please see <code>rstan::check_hmc_diagnostics()</code> for details.

Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn

<dl>

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

</dl>

check_hmc_diagn: Diagnostics of the MCMC based on HMC-related measures.

Description

Usage

Value

Arguments