summary.walker_fit

Return summary information of time-invariant model parameters.

Efficient Bayesian generalized linear models with time-varying coefficients
as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial
observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using
Hamiltonian Monte Carlo provided by Stan, using a state space representation
of the model in order to marginalise over the coefficients for efficient sampling.
For non-Gaussian models, the package uses the importance sampling type estimators based on
approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).

Jouni Helske

walker

Bayesian Generalized Linear Models with Time-Varying
Coefficients

summary.walker_fit function

<dl><dt>object</dt>
<dd>An output from <code>walker()</code> or <code>walker_glm()</code>.</dd>
<dt>type</dt>
<dd>Either <code>tiv</code> (time-invariant parameters, the default) or <code>tv</code> (time-varying coefficients).</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Summary of walker_fit Object — summary.walker_fit

<dl>

<dt>object</dt>
<dd>An output from <code>walker()</code> or <code>walker_glm()</code>.</dd>


<dt>type</dt>
<dd>Either <code>tiv</code> (time-invariant parameters, the default) or <code>tv</code> (time-varying coefficients).</dd>


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

</dl>

summary.walker_fit: Summary of walker_fit Object

Description

Usage

Arguments