mcmc_diagnostics

mcmc_diagnostics.dynamitefit

Prints HMC diagnostics and lists parameters with smallest effective sample
sizes and largest Rhat values. See <code>hmc_diagnostics()</code> and
<code>posterior::default_convergence_measures()</code> for details.

Easy-to-use and efficient interface for
Bayesian inference of complex panel (time series) data using dynamic
multivariate panel models by Helske and Tikka (2024)
<doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of
multiple measurements per individual, time-varying and time-invariant
effects, and a wide range of discrete and continuous distributions.
Estimation of these dynamic multivariate panel models is carried out via
'Stan'. For an in-depth tutorial of the package, see
(Tikka and Helske, 2024) <doi:10.48550/arXiv.2302.01607>.

Santtu Tikka

dynamite

Bayesian Modeling and Causal Inference for Multivariate
Longitudinal Data

Jouni Helske

Nicholas Clark

Lucy D'Agostino McGowan

mcmc_diagnostics function

<dl><dt>x</dt>
<dd>[<code>dynamitefit</code>] The model fit object.</dd>
<dt>...</dt>
<dd>Ignored.</dd>
<dt>n</dt>
<dd>[<code>integer(1)</code>] How many rows to print in
parameter-specific convergence measures. The default is 3. Should be a
positive (unrestricted) integer.</dd></dl>

Arguments

Diagnostic Values of a dynamite Model — mcmc_diagnostics

<dl>

<dt>x</dt>
<dd>[<code>dynamitefit</code>] The model fit object.</dd>


<dt>...</dt>
<dd>Ignored.</dd>


<dt>n</dt>
<dd>[<code>integer(1)</code>] How many rows to print in
parameter-specific convergence measures. The default is 3. Should be a
positive (unrestricted) integer.</dd>

</dl>

mcmc_diagnostics: Diagnostic Values of a dynamite Model

Description

Usage

Value

Arguments

See Also

Examples