formula.mvgam

formula.mvgam_prefit

Extract formulae from mvgam objects

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

formula.mvgam function

<dl><dt>x</dt>
<dd><code>mvgam</code>, <code>jsdgam</code> or <code>mvgam_prefit</code> object</dd>
<dt>trend_effects</dt>
<dd><code>logical</code>, return the formula from the
observation model (if <code>FALSE</code>) or from the underlying process
model (if<code>TRUE</code>)</dd>
<dt>...</dt>
<dd>Ignored</dd></dl>

Arguments

Author

Extract formulae from mvgam objects — formula.mvgam

<dl>

<dt>x</dt>
<dd><code>mvgam</code>, <code>jsdgam</code> or <code>mvgam_prefit</code> object</dd>


<dt>trend_effects</dt>
<dd><code>logical</code>, return the formula from the
observation model (if <code>FALSE</code>) or from the underlying process
model (if<code>TRUE</code>)</dd>


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

</dl>

formula.mvgam: Extract formulae from mvgam objects

Description

Usage

Value

Arguments

Author