coef.walker_fit

Returns the time-varying regression coefficients from output of <code>walker</code> or <code>walker_glm</code>.

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

coef.walker_fit function

<dl><dt>object</dt>
<dd>Output of <code>walker</code> or <code>walker_glm</code>.</dd>
<dt>summary</dt>
<dd>If <code>TRUE</code> (default), return summary statistics. Otherwise returns samples.</dd>
<dt>transform</dt>
<dd>Optional vectorized function for transforming the coefficients (for example exp).</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Extract Coeffients of Walker Fit — coef.walker_fit

<dl>

<dt>object</dt>
<dd>Output of <code>walker</code> or <code>walker_glm</code>.</dd>


<dt>summary</dt>
<dd>If <code>TRUE</code> (default), return summary statistics. Otherwise returns samples.</dd>


<dt>transform</dt>
<dd>Optional vectorized function for transforming the coefficients (for example exp).</dd>


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

</dl>

coef.walker_fit: Extract Coeffients of Walker Fit

Description

Usage

Value

Arguments