plot_mvgam_randomeffects

This function plots posterior empirical quantiles for random effect smooths (bs = re)

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

plot_mvgam_randomeffects function

<dl><dt>object</dt>
<dd><code>list</code> object of class <code>mvgam</code>. See <code>mvgam()</code></dd>
<dt>trend_effects</dt>
<dd>logical. If <code>TRUE</code> and a <code>trend_formula</code> was used in model
fitting, terms from the trend (i.e. process) model will be plotted</dd></dl>

Arguments

Plot random effect terms from mvgam models — plot_mvgam_randomeffects

<dl>

<dt>object</dt>
<dd><code>list</code> object of class <code>mvgam</code>. See <code>mvgam()</code></dd>


<dt>trend_effects</dt>
<dd>logical. If <code>TRUE</code> and a <code>trend_formula</code> was used in model
fitting, terms from the trend (i.e. process) model will be plotted</dd>

</dl>

plot_mvgam_randomeffects: Plot random effect terms from mvgam models

Description

Usage

Value

Arguments

Details