model.frame.mvgam

model.frame.mvgam_prefit

Extract model.frame from a fitted mvgam object

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 (2022) <doi:10.1111/2041-210X.13974>.

Nicholas J Clark

mvgam

Multivariate (Dynamic) Generalized Additive Models

Sarah Heaps

Scott Pease

Matthijs Hollanders

model.frame.mvgam function

<dl><dt>formula</dt>
<dd>a model <code><a href="/link/formula?package=mvgam&version=1.1.4" data-mini-rdoc="mvgam::formula">formula</a></code> or <code><a href="/link/terms?package=mvgam&version=1.1.4" data-mini-rdoc="mvgam::terms">terms</a></code>
 object or an R object.</dd>
<dt>trend_effects</dt>
<dd><code>logical</code>, return the model.frame 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 model.frame from a fitted mvgam object — model.frame.mvgam

<dl>

<dt>formula</dt>
<dd>a model <code><a href='https://rdrr.io/r/stats/formula.html'>formula</a></code> or <code><a href='https://rdrr.io/r/stats/terms.html'>terms</a></code>
 object or an R object.</dd>


<dt>trend_effects</dt>
<dd><code>logical</code>, return the model.frame 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>

model.frame.mvgam: Extract model.frame from a fitted mvgam object

Description

Usage

Value

Arguments

Author