plot_fit

Plot the fitted values and sample quantiles for a walker object

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

plot_fit function

<dl><dt>object</dt>
<dd>An output from <code>walker()</code> or <code>walker_glm()</code>.</dd>
<dt>level</dt>
<dd>Level for intervals. Default is 0.05, leading to 90% intervals.</dd>
<dt>alpha</dt>
<dd>Transparency level for <code><a href="https://ggplot2.tidyverse.org/reference/geom_ribbon.html">ggplot2::geom_ribbon()</a></code>.</dd>
<dt>...</dt>
<dd>Further arguments to <code>bayesplot::ppc_ribbon()</code>.</dd></dl>

Arguments

Plot the fitted values and sample quantiles for a walker object — plot_fit

<dl>

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


<dt>level</dt>
<dd>Level for intervals. Default is 0.05, leading to 90% intervals.</dd>


<dt>alpha</dt>
<dd>Transparency level for <code><a href='https://ggplot2.tidyverse.org/reference/geom_ribbon.html'>ggplot2::geom_ribbon()</a></code>.</dd>


<dt>...</dt>
<dd>Further arguments to <code>bayesplot::ppc_ribbon()</code>.</dd>

</dl>

plot_fit: Plot the fitted values and sample quantiles for a walker object

Description

Usage

Arguments