MARSSfit

Uses the method of a marssMLE class object. Will call a function such as <code>MARSSkem()</code>, <code>MARSSoptim()</code> or <code>MARSStmb()</code> in the marssTMB package.

The MARSS package provides maximum-likelihood parameter
estimation for constrained and unconstrained linear multivariate autoregressive
state-space (MARSS) models, including partially deterministic models. MARSS models are a class
of dynamic linear model (DLM) and vector autoregressive model (VAR)
model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB'
(using the 'marssTMB' companion package). Functions are provided for parametric and
innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including
bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all
conditional residual types. See the user guide for examples of dynamic factor analysis,
dynamic linear models, outlier and shock detection, and multivariate AR-p models.
Online workshops (lectures, eBook, and computer labs)
at <https://atsa-es.github.io/>.

Elizabeth Holmes  NOAA Federal

MARSS

Multivariate Autoregressive State-Space Modeling

Elizabeth Eli Holmes

Eric J. Ward

Mark D. Scheuerell

Kellie Wills

MARSSfit function

<dl><dt>x</dt>
<dd>a marssMLE object.</dd>
<dt>...</dt>
<dd>additional arguments for the fitting function</dd></dl>

Arguments

Generic for fitting MARSS models — MARSSfit

<dl>

<dt>x</dt>
<dd>a marssMLE object.</dd>


<dt>...</dt>
<dd>additional arguments for the fitting function</dd>

</dl>

MARSSfit: Generic for fitting MARSS models

Description

Usage

Arguments