summary.mvgam_fevd

This function takes an <code>mvgam_fevd</code> object and calculates
a posterior summary of the error variance decompositions of each series,
at all horizons

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Nicholas J Clark

mvgam

Multivariate (Dynamic) Generalized Additive Models

summary.mvgam_fevd function

<dl><dt>object</dt>
<dd>an object of class <code>mvgam_fevd</code> obtained using the
<code>fevd()</code> function. This object will contain draws from the posterior
distribution of the forecast error variance decompositions.</dd>
<dt>probs</dt>
<dd>The upper and lower percentiles to be computed by the
<code>quantile</code> function, in addition to the median</dd>
<dt>...</dt>
<dd>ignored</dd></dl>

Arguments

Author

Posterior summary of forecast error variance decompositions — summary.mvgam_fevd

<dl>

<dt>object</dt>
<dd>an object of class <code>mvgam_fevd</code> obtained using the
<code>fevd()</code> function. This object will contain draws from the posterior
distribution of the forecast error variance decompositions.</dd>


<dt>probs</dt>
<dd>The upper and lower percentiles to be computed by the
<code>quantile</code> function, in addition to the median</dd>


<dt>...</dt>
<dd>ignored</dd>

</dl>

summary.mvgam_fevd: Posterior summary of forecast error variance decompositions

Description

Usage

Value

Arguments

Author

See Also